| T.R | Title | User | Personal
Name | Date | Lines |
|---|
| 1409.1 | Anybody experimenting yet? | CUJO::MEIER | Systems Engineering Resident... | Thu Jul 11 1991 00:13 | 7 |
|
Has anybody in Digital play around Wavelet Transforms yet?
If so, any side by side compares to FFTs?
Al
|
| 1409.2 | pointer to current article | STAR::ABBASI | i^(-i) = SQRT(exp(PI)) | Sun Apr 05 1992 01:56 | 4 |
| see current issue of Dr Dobb's magazine , it has a good article on
Wavelet transforms and C code.
/nasser
|
| 1409.3 | Listings from Dr. Dobbs (Thanks to George Hetrick for downloading from CompuServe) | SEASID::SYNGE | James M Synge, PSS, Portland, OR | Fri Apr 17 1992 13:30 | 327 |
| _THE FAST WAVELET TRANSFORM_
by Mac A. Cody
[LISTING ONE]
#define WAVE_MGT
#include <alloc.h>
#include "wave_mgt.h"
double **BuildTreeStorage(int inlength, int levels)
{
double **tree;
int i, j;
/* create decomposition tree */
tree = (double **) calloc(2 * levels, sizeof(double *));
j = inlength;
for (i = 0; i < levels; i++)
{
j /= 2;
if (j == 0)
{
levels = i;
/* printf("\nToo many levels requested for available data\n");
printf("Number of transform levels now set to %d\n", levels); */
continue;
}
tree[2 * i] = (double *) calloc((j + 5), sizeof(double));
tree[2 * i + 1] = (double *) calloc((j + 5), sizeof(double));
}
return tree;
}
void DestroyTreeStorage(double **tree, int levels)
{
char i;
for (i = (2 * levels - 1); i >= 0; i--)
free(tree[i]);
free(tree);
}
void TreeCopy(double **TreeDest, double **TreeSrc, int siglen, int levels)
{
int i, j;
for (i = 0; i < levels; i++)
{
siglen /= 2;
for (j = 0; j < siglen + 5; j++)
{
if ((i + 1) == levels)
TreeDest[2 * i][j] = TreeSrc[2 * i][j];
else
TreeDest[2 * i][j] = 0.0;
TreeDest[(2 * i) + 1][j] = TreeSrc[(2 * i) + 1][j];
}
}
}
void TreeZero(double **Tree, int siglen, int levels)
{
int i, j;
for (i = 0; i < levels; i++)
{
siglen /= 2;
for (j = 0; j < siglen + 5; j++)
{
Tree[2 * i][j] = 0.0;
Tree[(2 * i) + 1][j] = 0.0;
}
}
}
void ZeroTreeDetail(double **Tree, int siglen, int levels)
{
int i, j;
for (i = 0; i < levels; i++)
{
siglen /= 2;
for (j = 0; j < siglen + 5; j++)
Tree[(2 * i) + 1][j] = 0.0;
}
}
[LISTING TWO]
/* WAVELET.C */
#include <math.h>
typedef enum { DECOMP, RECON } wavetype;
#include "wavelet.h"
void WaveletCoeffs(double alpha, double beta, double *wavecoeffs)
{
double tcosa, tcosb, tsina, tsinb;
char i;
/* precalculate cosine of alpha and sine of beta to reduce */
/* processing time */
tcosa = cos(alpha);
tcosb = cos(beta);
tsina = sin(alpha);
tsinb = sin(beta);
/* calculate first two wavelet coefficients, a = a(-2) and b = a(-1) */
wavecoeffs[0] = ((1.0 + tcosa + tsina) * (1.0 - tcosb - tsinb)
+ 2.0 * tsinb * tcosa) / 4.0;
wavecoeffs[1] = ((1.0 - tcosa + tsina) * (1.0 + tcosb - tsinb)
- 2.0 * tsinb * tcosa) / 4.0;
/* precalculate cosine and sine of alpha minus beta to reduce */
/* processing time */
tcosa = cos(alpha - beta);
tsina = sin(alpha - beta);
/* calculate last four wavelet coefficients c = a(0), d = a(1), */
/* e = a(2), and f = a(3) */
wavecoeffs[2] = (1.0 + tcosa + tsina) / 2.0;
wavecoeffs[3] = (1.0 + tcosa - tsina) / 2.0;
wavecoeffs[4] = 1 - wavecoeffs[0] - wavecoeffs[2];
wavecoeffs[5] = 1 - wavecoeffs[1] - wavecoeffs[3];
/* zero out very small coefficient values caused by truncation error */
for (i = 0; i < 6; i++)
{
if (fabs(wavecoeffs[i]) < 1.0e-15)
wavecoeffs[i] = 0.0;
}
}
char MakeWaveletFilters(double *wavecoeffs, double *Lfilter,
double *Hfilter, wavetype transform)
{
char i, j, k, filterlength;
/* find the first non-zero wavelet coefficient */
i = 0;
while(wavecoeffs[i] == 0.0)
i++;
/* find the last non-zero wavelet coefficient */
j = 5;
while(wavecoeffs[j] == 0.0)
j--;
/* form decomposition filters h~ and g~ or reconstruction filters h and g.
Division by 2 in construction of decomposition filters is for normalization */
filterlength = j - i + 1;
for(k = 0; k < filterlength; k++)
{
if (transform == DECOMP)
{
Lfilter[k] = wavecoeffs[j--] / 2.0;
Hfilter[k] = (double) (((i++ & 0x01) * 2) - 1) * wavecoeffs[i] / 2.0;
}
else
{
Lfilter[k] = wavecoeffs[i++];
Hfilter[k] = (double) (((j-- & 0x01) * 2) - 1) * wavecoeffs[j];
}
}
/* clear out the additional array locations, if any */
while (k < 6)
{
Lfilter[k] = 0.0;
Hfilter[k++] = 0.0;
}
return filterlength;
}
double DotP(double *data, double *filter, char filtlen)
{
char i;
double sum;
sum = 0.0;
for (i = 0; i < filtlen; i++)
sum += *data-- * *filter++; /* decreasing data pointer is */
/* moving backward in time */
return sum;
}
void ConvolveDec2(double *input_sequence, int inp_length,
double *filter, char filtlen, double *output_sequence)
/* convolve the input sequence with the filter and decimate by two */
{
int i;
char shortlen, offset;
for(i = 0; (i <= inp_length + 8) && ((i - filtlen) <= inp_length + 8); i += 2)
{
if (i < filtlen)
*output_sequence++ = DotP(input_sequence + i, filter, i + 1);
else if (i > (inp_length + 5))
{
shortlen = filtlen - (char) (i - inp_length - 4);
offset = (char) (i - inp_length - 4);
*output_sequence++ = DotP(input_sequence + inp_length + 4,
filter + offset, shortlen);
}
else
*output_sequence++ = DotP(input_sequence + i, filter, filtlen);
}
}
int DecomposeBranches(double *In, int Inlen, double *Lfilter,
double *Hfilter, char filtlen, double *OutL, double *OutH)
/* Take input data and filters and form two branches of have the
original length. Length of branches is returned */
{
ConvolveDec2(In, Inlen, Lfilter, filtlen, OutL);
ConvolveDec2(In, Inlen, Hfilter, filtlen, OutH);
return (Inlen / 2);
}
void WaveletDecomposition(double *InData, int Inlength, double *Lfilter,
double *Hfilter, char filtlen, char levels, double **OutData)
/* Assumes input data has 2 ^ (levels) data points/unit interval. First InData
is input signal; all others are intermediate approximation coefficients */
{
char i;
for (i = 0; i < levels; i++)
{
Inlength = DecomposeBranches(InData, Inlength, Lfilter, Hfilter,
filtlen, OutData[2 * i], OutData[(2 * i) + 1]);
InData = OutData[2 * i];
}
}
double DotpEven(double *data, double *filter, char filtlen)
{
char i;
double sum;
sum = 0.0;
for (i = 0; i < filtlen; i += 2)
sum += *data-- * filter[i]; /* decreasing data pointer is moving */
/* backward in time */
return sum;
}
double DotpOdd(double *data, double *filter, char filtlen)
{
char i;
double sum;
sum = 0.0;
for (i = 1; i < filtlen; i += 2)
sum += *data-- * filter[i]; /* decreasing data pointer is moving */
/* backward in time */
return sum;
}
void ConvolveInt2(double *input_sequence, int inp_length, double *filter,
char filtlen, char sum_output, double *output_sequence)
/* insert zeros between each element of the input sequence and
convolve with the filter to interpolate the data */
{
int i;
if (sum_output) /* summation with previous convolution if true */
{
/* every other dot product interpolates the data */
for(i = (filtlen / 2) - 1; i < inp_length + filtlen - 2; i++)
{
*output_sequence++ += DotpOdd(input_sequence + i, filter, filtlen);
*output_sequence++ += DotpEven(input_sequence + i + 1, filter, filtlen);
}
*output_sequence++ += DotpOdd(input_sequence + i, filter, filtlen);
}
else /* first convolution of pair if false */
{
/* every other dot product interpolates the data */
for(i = (filtlen / 2) - 1; i < inp_length + filtlen - 2; i++)
{
*output_sequence++ = DotpOdd(input_sequence + i, filter, filtlen);
*output_sequence++ = DotpEven(input_sequence + i + 1, filter, filtlen);
}
*output_sequence++ = DotpOdd(input_sequence + i, filter, filtlen);
}
}
int ReconstructBranches(double *InL, double *InH, int Inlen,
double *Lfilter, double *Hfilter, char filtlen, double *Out)
/* Take input data and filters and form two branches of have
original length. length of branches is returned */
{
ConvolveInt2(InL, Inlen, Lfilter, filtlen, 0, Out);
ConvolveInt2(InH, Inlen, Hfilter, filtlen, 1, Out);
return Inlen * 2;
}
void WaveletReconstruction(double **InData, int Inlength, double *Lfilter,
double *Hfilter, char filtlen, char levels, double *OutData)
/* assumes that input data has 2 ^ (levels) data points per unit interval */
{
double *Output;
char i;
Inlength = Inlength >> levels;
/* Destination of all but last branch reconstruction is the next
higher intermediate approximation */
for (i = levels - 1; i > 0; i--)
{
Output = InData[2 * (i - 1)];
Inlength = ReconstructBranches(InData[2 * i], InData[(2 * i) + 1],
Inlength, Lfilter, Hfilter, filtlen, Output);
}
/* Destination of the last branch reconstruction is the output data */
ReconstructBranches(InData[0], InData[1], Inlength, Lfilter, Hfilter,
filtlen, OutData);
}
double CalculateMSE(double *DataSet1, double *DataSet2, int length)
{
/* calculate mean squared error of output of reconstruction as
compared to the original input data */
int i;
double pointdiff, topsum, botsum;
topsum = botsum = 0.0;
for (i = 0; i < length; i++)
{
pointdiff = DataSet1[i] - DataSet2[i];
topsum += pointdiff * pointdiff;
botsum += DataSet1[i] * DataSet1[i];
}
return topsum / botsum;
}
[LISTING THREE]
/* WAVE_MGT.H */
double **BuildTreeStorage(int inlength, int levels);
void DestroyTreeStorage(double **tree, int levels);
void TreeCopy(double **TreeDest, double **TreeSrc, int siglen, int levels);
void TreeZero(double **Tree, int siglen, int levels);
void ZeroTreeDetail(double **Tree, int siglen, int levels);
/* WAVELET.H */
void WaveletCoeffs(double alpha, double beta, double *wavecoeffs);
char MakeWaveletFilters(double *wavecoeffs, double *Lfilter,
double *Hfilter, wavetype transform);
double DotP(double *data, double *filter, char filtlength);
void ConvolveDec2(double *input_sequence, int inp_length,
double *filter, char filtlen, double *output_sequence);
int DecomposeBranches(double *In, int Inlen, double *Lfilter,
double *Hfilter, char filtlen, double *OutL, double *OutH);
void WaveletDecomposition(double *InData, int Inlength, double *Lfilter,
double *Hfilter, char filtlen, char levels, double **OutData);
double DotpEven(double *data, double *filter, char filtlength);
double DotpOdd(double *data, double *filter, char filtlength);
void ConvolveInt2(double *input_sequence, int inp_length, double *filter,
char filtlen, char sum_output, double *output_sequence);
int ReconstructBranches(double *InL, double *InH, int Inlen,
double *Lfilter, double *Hfilter, char filtlen, double *Out);
void WaveletReconstruction(double **InData, int Inlength, double *Lfilter,
double *Hfilter, char filtlen, char levels, double *OutData);
double CalculateMSE(double *DataSet1, double *DataSet2, int length);
|
| 1409.4 | where is alloc.h ? | STAR::ABBASI | i^(-i) = SQRT(exp(PI)) | Fri Apr 17 1992 16:16 | 3 |
| ref .1
thanx, but what is <alloc.h> ?
/nasser
|
| 1409.5 | | SEASID::SYNGE | James M Synge, PSS, Portland, OR | Mon Apr 20 1992 18:19 | 3 |
| I don't know for sure, but if you replace that with stdlib.h, you'll probably
get the same result. I don't think there is an alloc.h in ANSI C, so that is
probably a file from the MS-DOS compiler the author used.
|
| 1409.6 | Wavelet Digest distribution list. | CADSYS::COOPER | Topher Cooper | Mon Jul 13 1992 17:44 | 58 |
| From: [email protected] (Wavelet Digest)
Subject: Announcement: Wavelet Digest
Keywords: wavelets
Date: 13 Jul 92 14:15:40 GMT
Sender: [email protected] (Wim Sweldens)
Reply-To: [email protected]
Organization: Univ. of South Carolina, Columbia
We are happy to announce to you a
W A V E L E T D I G E S T.
Nowadays, wavelets are one of the most rapidly developing research areas
both in pure/applied mathematics and signal/image processing.
More and more people are becoming interested in using wavelets
for a broad spectrum of applications.
For researchers it is essential to stay informed on the latest
developments. Hence we are planning, here at the University of
South Carolina, to start an edited "wavelet digest."
Topics we would like to cover in our digest are:
- Announcements of:
- future wavelet conferences, call for papers, etc.
- talks on wavelets
- wavelet courses
- new preprints (and how to get them, e.g. by ftp)
- wavelet articles appearing in journals
- new books on wavelets
- wavelet-related job opportunities
- Questions (and answers) concerning:
- mathematical background of wavelets
- technical aspects of wavelets
- references
- addresses
- Regularly updated reference lists
- Reviews of recently published books on wavelets
- Posting of open problems
- Available wavelet software, where and how to get it
If you would like to subscribe to this mailing list,
send a message with "subscribe" as the subject to
[email protected].
If you want to submit something to the digest,
send a message with "submit" as the subject to
[email protected].
Please pass this message on to others who you think might be interested.
We are hoping to present the first wavelet digest to you soon.
Wim Sweldens
Bjorn Jawerth
Department of Mathematics,
University of South Carolina.
|
| 1409.7 | wavelet Digest Vol. 1, Nr. 1 | STAR::ABBASI | i^(-i) = SQRT(exp(PI)) | Fri Jul 24 1992 23:49 | 390 |
| From: DECWRL::"[email protected]" 24-JUL-1992 20:35:25.11
To: star::abbasi
CC:
Subj: Wavelet Digest, Vol. 1, Nr. 1.
Wavelet Digest Friday, July 24, 1992 Volume 1 : Issue 1
Today's Editor: Wim Sweldens
Today's Topics:
1. Wavelet digest takes off
2. ACHA, a new wavelet journal
3. Call for abstracts and papers, SPIE Orlando 93 conference
4. Wavelet conference, Cambridge, England, March 93
5. Numerical Recipes wavelet software
6. A question about orthonormal wavelets
7. Book announcement
8. Looking for a reference
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
--------------------------- Topic #1 -----------------------------------
From: Wavelet Digest editors, University of South Carolina.
Subject: Wavelet Digest takes off
Dear Wavelet Digest subscriber,
We are very happy to inform you that the Wavelet Digest really has
taken off. We would like to thank you for all your replies to our
initial announcement and for your encouraging remarks.
At the time of this writing, we have 1452 subscribers from over 400
institutions, including sites in 26 countries, 161 US universities,
12 US government agencies and 83 companies.
So, if you have any information you want to send out to the
wavelet community, please submit it to the digest.
The frequency of the digest will depend on the number of submissions.
We hope to send an issue about every two weeks.
At the moment, we are gathering several reference lists on wavelets.
As soon as they are ready we will announce them in the digest.
We also would like to express our thanks to Brygg Ullmer for
helping us write the software for the digest.
Wim Sweldens, Bjorn Jawerth.
Note: If you subscribe to the digest, your e-mail address will be
kept confidential, and will only be used for sending this digest to
regularly.
--------------------------- Topic #2 -----------------------------------
From: Charles Chui, Texas A&M University.
Date: Wed, 22 Jul 92 13:49:06 CDT
Subject: ACHA, new wavelet journal
Applied and Computational Harmonic Analysis:
Wavelets, Signal Processing, and Applications
A new journal published by Academic Press, Inc.
Editors-in-Chief:
Charles K. Chui, Texas A&M University
Ronald R. Coifman, Yale University
Ingrid Daubechies, Rutgers University
Description:
Various techniques for time-frequency decomposition have recently been
developed and continue to evolve. Applied and Computational Harmonic Analysis,
an interdisciplinary journal, will publish high-quality papers in all areas
related to the applied and computational aspects of harmonic analysis with
special emphasis on wavelet analysis and signal processing. The objectives of
this journal are to chronicle the important publications in the rapidly growing
field of wavelet analysis and to stimulate research in this area.
This subject is considered a major breakthrough in mathematical sciences
and provides a common link between mathematicians and engineers. Wavelets are
considered in its broadest sense, covering topics in related areas such as:
% Decomposition and reconstruction algorithms
% Subdivision algorithms
% Continuous and discrete wavelet transform
% Time-frequency localization
% Phase-space analysis
% Subband coding
% Image compression
% Real-time filtering
% Radar and sonar applications
% Transient analysis
% Medical imaging
% Multigrid methods
% Frames
Manuscripts should be submitted in quadruplicate to:
Applied and Computational Harmonic Analysis
Editorial Office, Third Floor
1250 Sixth Avenue
San Diego, CA 92101
U.S.A.
Subscribe today and receive the latest information in this expanding field of
research! Volume 1 will appear in 1993.
For more information, please contact:
Academic Press, Inc.
Journal Promotion Department
1250 Sixth Avenue
San Diego, CA 92101 U.S.A.
(619) 699-6742
--------------------------- Topic #3 -----------------------------------
From: Brian Telfer, Naval Surface Warfare Center.
Date: Tue, 21 Jul 92 15:23:10 EDT
Subject: Call for abstracts / papers SPIE Orlando'93
C A L L F O R A B S T R A C T S / P A P E R S
---------------------------------------------------
SPIE-Orlando'93
April 12-16, 1993
Wavelet Transform Session
(Part of Visual Information Processing II Conf.)
Abstract Due Date: 14 Sept. 1992
Manuscript Due Date: 15 March 1993
Topics: Any work in wavelet or other multiresolution transforms for
information processing.
Work emphasizing adaptive wavelets will be especially
welcomed.
For examples, see the September 1992 _Optical Engineering_
special issue on wavelet transforms.
Send abstracts to:
Dr. Harold Szu
Naval Surface Warfare Center, Code R44
Silver Spring, MD 20903
(301) 394-3097
(301) 394-3923 (fax)
If the abstract deadline poses a hardship, please contact Dr. Szu.
Please include the following information:
Abstract title
Author listing with full names and affiliations as they
will appear in the program
Correspondence for each author - mail address, telephone,
fax, e-mail
Presentation preference - oral or poster
Abstract - 500 words
Brief biography - 50 to 100 words
--------------------------- Topic #4 -----------------------------------
Date: Tue, 21 Jul 1992 17:41:06 +0000
From: Patrick Flandrin, ENS Lyon.
Subject: Conference announcement
MULTISCALE STOCHASTIC PROCESSES
analysed using
MULTIFRACTALS and WAVELETS
March 29-31, 1993
Cambridge, England
Traditional techniques have shown to be insufficient for the study of
random phenomena with strongly localised events. New methods, like the
wavelet transform which acts like a mathematical microscope, and
multifractal analysis, which concentrates on local similarity scalings
and the self-similar structure of the supporting `fractal' sets have
recently been developed and may be well suited to describe and analyse
such localised structures. Localised events in complex signals often
exhibit multiscale and self-similar properties and/or may be distributed
about the space in a multiscale and self-similar fashion.
Different methods are appropriate for different phenomena, but for the same
phenomena different methods reveal different characteristic properties.
In particular, it is important to establish which aspects of interscale
non-linear dynamics can best be captured or described by which tools.
Many basic mathematical questions underlying these issues will be addressed
at the meeting. Special emphasis will be given to fluid mechanics,
particularly turbulence structure, but applications in other fields of
knowledge will also be included, and comparisons encouraged. Finally the
relevance and application of these methods and concepts to industrial
problems will be the object of a special session involving both industrial
and academic specialists who have had experience of these problems.
This conference follows the successful meeting held in December 1990, the
Proceedings of which are published this year.
The conference will be held under the auspices of the I.M.A. (U.K.), SIAM
and other U.S. agencies and in association with ERCOFTAC and SMAI (France).
Location: St Catherine's College, Cambridge, where accommodation will
be available for the participants.
Call for papers: Abstracts of papers are invited to be submitted to
the Organising Committee by 15 October 1992
Program: Presentations of papers will be either by poster or oral.
The conference will cover the following general topics:
- relationships between multiscale and multi - `fractal' structure;
self-similarity, space fillingness and scale invariance
- issues of interscale dynamics in highly non-linear dynamical systems
and relationships to structure and the stochastic nature of turbulence
- multiscale stochastic processes: modelling, estimation, identification
- the application of the wavelet transform and related techniques to the
above issues
- the application of the above issues, methods and concepts in the
improvement of technology
Confirmed Invited speakers: A. Arn R. Benzi, R. Kraichnan,
S.A. Orszag K.R. Sreenivasan
Organising Committee:
Jim Brasseur (Penn. State., U.S.), Patrick Flandrin (ENS, Lyon),
Julian Hunt (Met Off. Bracknell), Christos Vassilicos (DAMTP, Silver
Street, Cambridge, England CB3 9EW ([email protected]))
Note from the editor: this announcement is also available as a tex
file. If you need it, you can retrieve it using anonymous ftp to
maxwell.math.scarolina.edu, file /pub/wavelet/announce/conf1.tex
--------------------------- Topic #5 -----------------------------------
From: Bill Press, Harvard University.
Date: Tue, 21 Jul 92 11:40:31 -0400
Subject: Numerical Recipes software
This fall, the second edition of the Numerical Recipes book will come
out. It will include a chapter on wavelets and software both in Fortran
and C.
A preliminary version of the paper and the software can be downloaded
from anonymous ftp to 128.103.40.79, in /pub/wavelet.tex and
/pub/wavelet.f.
The figures are, unfortunately, NOT available on-line, and there are
no more preprints.
--------------------------- Topic #6 -----------------------------------
From: Ming-Haw Yaou, National Chiao Tung University Taiwan.
Date: Wed, 15 Jul 92 10:18:49 CST
Subject: A question about "Wavelet"
Hello everyone !!
I have a question about the binary orthonormal wavelets.
Let P(x) be the scaling function and Q(x) be the wavelet,
Pmn(x):=2^(-m/2)P(2^(-m)x-n) and Qmn(x):=2^(-m/2)Q(2^(-m)x-n) be
the bases for multiresolution wavelet signal approximation.
In the orthonormal case, both Pmn(x) and Qmn(x) are orthonormal bases,
also P(x) and Q(x) are mutually orthogonal.
My question is :
Is it possible to find a pair of functions P(x) and Q(x) such that the
following two properties can be satisfied:
(a) Both Pmn(x) and Qmn(x) are orthonormal bases, P(x) and Q(x)
are mutually orthogonal. That is, P(x) and Q(x)
can construct a binary orthonormal wavelet analysis.
(b) The second derivatives of P(x) and Q(x), denoted as P"(x) and Q"(x),
are mutually orthogonal too.
I will very appreciate if any answer or advice is sent from you.
Please e-mail to : [email protected]
Thank in advance
Ming-Haw Yaou.
--------------------------- Topic #7 -----------------------------------
From: Michelle Jones, SIAM.
Date: Mon, 13 Jul 92 15:01:06 EST
Subject: Book Announcement
Ten Lectures on Wavelets
Ingrid Daubechies
CBMS-NSF Regional Conference Series in Applied Mathematics 61
Wavelets are a mathematical development that may revolutionize
the world of information storage and retrieval according to many
experts. They are a fairly simple mathematical tool now being
applied to the compression of data--such as fingerprints, weather
satellite photographs, and medical x-rays--that were previously
thought to be impossible to condense without losing crucial
details.
The author has worked on several aspects of the wavelet transform
and has developed a collection of wavelets that are remarkably
efficient. This monograph contains 10 lectures presented by Dr.
Daubechies as the principal speaker at the 1990 CBMS-NSF
Conference on Wavelets and Applications.
Although it is only in the past 10 years that interest in
wavelets has experienced explosive growth, they are the result
of a synthesis of ideas that originated during the last 20 or 30
years in fields ranging from engineering to pure mathematics.
Because of their interdisciplinary origins, wavelets appeal to
scientists and engineers of many different backgrounds.
Contents
Introduction; Preliminaries and Notation; The What, Why, and How
of Wavelets; The Continuous Wavelet Transform; Discrete Wavelet
Transforms: Frames; Time-Frequency Density and Orthonormal Bases;
Orthonormal Bases of Wavelets and Multiresolutional Analysis;
Orthonormal Bases of Compactly Supported Wavelets; More About the
Regularity of Compactly Supported Wavelets; Symmetry for
Compactly Supported Wavelet Bases; Characterization of Functional
Spaces by Means of Wavelets; Generalizations and Tricks for
Orthonormal Wavelet Bases; References; Indexes.
Special Features
- Only comprehensive book in English on this subject.
- Ideal for mapping out a graduate course or a seminar on the
subject.
- Includes an extensive bibliography.
Audience
Mathematicians or other scientists and engineers interested in
the applications (in signal analysis, time-frequency methods,
numerical analysis, etc.) of wavelets will benefit most from this
book. A background in Fourier analysis and some real analysis is
suggested. This volume is also appropriate for graduate or
advanced undergraduate courses on wavelets.
About the Author
Ingrid Daubechies is a member of the technical staff at AT&T Bell
Laboratories while on leave from her tenured position in the
theoretical physics department at the Free University, Brussels.
She is a full professor in the mathematics department at Rutgers
University. She is a frequent lecturer and has published 46
papers.
1992 / xix + 357 pages / Softcover / ISBN 0-89871-274-2
For additional information please contact SIAM Customer Service
Department, 3600 University City Science Center, Philadelphia, PA
19104-2688; 215-382-9800; fax: 215-386-7999; e-mail:
[email protected].
--------------------------- Topic #8 -----------------------------------
From: Danie Behr, University of Pretoria South Africa.
Date: 16 Jul 92 12:23:02 WET-2
Subject: Looking for a reference
I am doing research on the use of wavelet transforms for pitch
extraction from speech signals. I cannot get hold of the following
article concerning wavelets:
''Complete signal representation with multiscale edges'' by S.G.
Mallat and S. Zhong. According to at least six references this
article could be found in the Robotics Research Technical Report no.
483, Robotics Report no.219, New York University Courant Institute of
Mathematical Sciences, December 1989.
Inter library could not locate it for me. Does anybody by any chance
have this article in their library?
Thank you
Danie Behr
Unit for Software Engineering
Dept Computer Science
University of Pretoria
South Africa
<[email protected]>
-------------------- End of Wavelet Digest -----------------------------
|
| 1409.8 | wavelet Digest Vol. 1, Nr. 2 | STAR::ABBASI | i^(-i) = SQRT(exp(PI)) | Wed Aug 05 1992 18:05 | 464 |
| From: DECWRL::"[email protected]" "MAIL-11 Daemon" 5-AUG-1992 16:59:11.89
To: star::abbasi
CC:
Subj: Wavelet Digest, Vol. 1, Nr. 2.
Wavelet Digest Wednesday, August 5, 1992 Volume 1 : Issue 2
Today's Editor: Wim Sweldens
Today's Topics:
1. Preprints available from National Institute of Health
2. Call for Papers for a Special Issue of the IEEE
Trans. on Signal Processing
3. Announcement of Epic software
4. Available proceedings of NJIT symposia on wavelets.
5. Book announcement from Academic Press
6. Question: looking for wavelets with compact support in Fourier-space
7. Book announcement from Jones and Bartlett Publishers
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
Archive site:
Anonyous ftp to maxwell.math.scarolina.edu,
directory /pub/wavelet/archive.
Gopher server: bigcheese.math.scarolina.edu.
We will have details on how to use this server in the next issue.
Current number of subscribers: 1669
--------------------------- Topic #1 -----------------------------------
From: Akram Aldroubi
BEIP/National Center for Research Resources,
National Institutes of Health,
Bethesda, MD 20892
Date: Mon Jul 27 14:16:24 1992
Subject: Preprints available
The following two preprints are available:
1. Families of multiresolution and wavelet spaces with optimal properties
Akram Aldroubi, Michael Unser
Abstract:
Under suitable conditions, if the scaling functions $\varphi_1$ and
$\varphi_2$ generate the multiresolutions $V_{(j)}(\varphi_1)$ and
$V_{(j)}(\varphi_2 )$ , then their convolution
$\varphi_1*\varphi_2$ also generates a multiresolution
$V_{(j)}(\varphi_1*\varphi_2)$. Moreover, if p is an appropriate
convolution operator from $l_2$ into itself and if $\varphi$ is a
scaling function generating the multiresolution $V_{(j)}(\varphi)$,
then $p*\varphi$ is a scaling function generating the same
multiresolution $V_{(j)}(\varphi)=V_{(j)}(p*\varphi)$. Using these
two properties, we group the scaling and wavelet functions into
equivalent classes and consider various equivalent basis functions
of the associated function spaces. We use the n-fold convolution
product to construct sequences of multiresolution and wavelet
spaces $V_{(j)}(\varphi^n)$ and $W_{(j)}(\varphi^n)$ with increasing
regularity. We discuss the link between multiresolution analysis
and Shannon's sampling theory. We then show that the interpolating
and orthogonal pre- and post-filters associated with the
multiresolution sequence $V_{(0)}(\varphi^n)$ asymptotically
converge to the ideal lowpass filter of Shannon. We also prove that
the filters associated with the sequence of wavelet spaces
$W_{(0)}(\varphi^n)$ converge to the ideal bandpass filter. Finally,
we construct the basic-wavelet sequences $\psi_b^n$ and show that
they tend to Gabor functions. This provides wavelets that are nearly
time-frequency optimal. The theory is illustrated with the example
of polynomial splines.
2. Discrete spline filters for multiresolutions and wavelets of $l_2$
Akram Aldroubi, Michael Unser, Murray Eden
Abstract:
We consider the problem of approximation by B-spline functions in the
context of the discrete sequence-space $l_2$ instead of the usual space
$L_2$. This setting is natural for digital signal/image processing, and for
numerical analysis. To this end, we use sampled B-splines to define a
family of approximation spaces $\SS^n_m \subset l_2$ that we partition
into sets of multiresolution and wavelet spaces of $l_2$. We show that
the least square approximation in $\SS^n_m$ of a finite energy signal is
obtained using translation-invariant filters. We study the asymptotic
properties of these filters and provide the link with Shannon's sampling
procedure. We derive and compare two pyramidal representations of
signals; the $l_2$-optimal and the stepwise $l_2$-optimal pyramids. The
advantage of the latter pyramid is that it can be computed by the
repetitive application of a single procedure. Finally, we derive a step by
step discrete wavelet transform of $l_2$ that is based on the stepwise
optimal representation of signals. As an application we implement and
compare these representations with the Gaussian/Laplacian pyramid
which is widely used in computer vision.
For a copy of these preprints, send an e-mail to [email protected].
Note from the editor: There is a list of other papers by these authors
together with the abstracts available by anonymous ftp.
Connect to maxwell.math.scarolina.edu and retrieve the file
/pub/wavelet/abstracts/aldroubi.txt.
--------------------------- Topic #2 -----------------------------------
From: Martin Vetterli, Columbia University New York.
Date: Fri, 31 Jul 92 08:00:43 EDT
***************************************************************************
Call for Papers for a Special Issue of the IEEE Trans. on Signal Processing
***************************************************************************
``WAVELETS AND SIGNAL PROCESSING''
A special issue of the IEEE Transactions on Signal Processing will be
dedicated to the subject of wavelets and their applications in signal
processing. Original unpublished research papers are sought in this area
(a list of suggested topics is provided below).
The special issue should be oriented towards signal processing applications,
and provide a picture of the state of the art. Thus tutorial papers
will be considered as well. Papers comparing wavelet based with more
classical techniques are also encouraged.
Papers talking about the application of wavelets should emphasize what
a wavelet based approach has to offer over a traditional one.
Topics include but are not limited to:
Theory of wavelets and related techniques
- computation of the wavelet transform and its approximation
- wavelets and wavelet packets
- continuous wavelet transforms and frames
- approximation using wavelets
- multiresolution techniques
- perfect reconstruction filter banks and regularity
- multiscale difference equations
Wavelets and signal analysis
- signal analysis using wavelets and filter banks
- time-frequency and time-scale representation of signals
- statistical signal analysis using multiscale methods
- multiscale signal modeling
Wavelets and signal compression
- subband coding and generalizations
- multidimensional wavelets and filter banks for image and video coding
- quantization and entropy coding in subband decompositions
- multiscale difference equations in speech and image coding
New applications
- signal restoration using wavelets
- signal reconstruction based on wavelet decompositions
- fast multiscale algorithms
- wavelets in Medical Imaging
- signal separation via wavelet techniques
Prospective authors should follow the regular guidelines of the
IEEE Transactions on Signal Processing, except that they should forward 5
copies of the manuscript to one of the guest editors listed below. The
deadline for submission is Sept. 1 1992, and early submission is
encouraged. The special issue will appear in the summer of 1993.
Guest editors:
P.Duhamel Patrick Flandrin
CNET/PAB/RPE Ecole Normale superieure de Lyon
38-40 Rue du General Leclerc Laboratoire de Physique (URA1325 CNRS)
F-92131 Issy-les-Moulineaux 46 allee d'italie 69364 Lyon Cedex 07
France France
Tel (33) 1 4529 44 38 Tel (33) 72 72 81 60
Fax (33) 1 4529 60 52 fax (33) 72 72 80 80
email [email protected] email [email protected]
Takao Nishitani Ahmed H. Tewfik
C&C Systems Research Labs. Department of Electrical Engineering
NEC Corporation Univ. of Minnesota
1-1, Miyazaki-4, Miyamae-Ku Room 4-178 EE/CSci. Bldg.
Kawasaki, 216, Japan Minneapolis, MN 55455, USA
Tel (81) 44 856 2118 Tel (1) 612 625-6024
Fax (81) 44 856 2232 Fax (1) 612 625-4583
email [email protected] email: [email protected]
M.Vetterli
Dept. of EE and CTR
Columbia University
500 W 120th Street
New York, NY10027-6699, USA
Tel (1) 212 854 3109
Fax (1) 212 932 9421
email [email protected]
--------------------------- Topic #3 -----------------------------------
From: Eero Simoncelli, MIT.
Subject: Epic
----------------------------------------------------------------------
--- EPIC (Efficient Pyramid Image Coder) ---
--- Designed by Eero P. Simoncelli and Edward H. Adelson ---
--- Written by Eero P. Simoncelli ---
--- Developed at the Vision Science Group, The Media Laboratory ---
--- Copyright 1989, Massachusetts Institute of Technology ---
--- All rights reserved. ---
----------------------------------------------------------------------
Permission to use, copy, or modify this software and its documentation for
educational and research purposes only and without fee is hereby granted,
provided that this copyright notice and the original authors' names appear on
all copies and supporting documentation. For any other uses of this software,
in original or modified form, including but not limited to distribution in
whole or in part, specific prior permission must be obtained from M.I.T. and
the authors. These programs shall not be used, rewritten, or adapted as the
basis of a commercial software or hardware product without first obtaining
appropriate licenses from M.I.T. M.I.T. makes no representations about the
suitability of this software for any purpose. It is provided "as is" without
express or implied warranty.
EPIC (Efficient Pyramid Image Coder) is an experimental image data compression
utility written in the C programming language. The compression algorithms are
based on a biorthogonal critically-sampled wavelet (subband) decomposition and a
combined run-length/Huffman entropy coder. The filters have been designed to
allow extremely fast decoding on conventional (ie, non-floating point) hardware,
at the expense of slower encoding and a slight degradation in compression
quality (as compared to a good orthogonal wavelet decomposition).
We are making this code available to interested researchers who wish to
experiment with a subband pyramid coder. We have attempted to optimize the
speed of pyramid reconstruction, but the code has not been otherwise optimized,
either for speed or compression quality. In particular, the pyramid
construction process is unnecessarily slow, quantization binsizes are chosen to
be the same for each subband, and we have used a very simple scalar entropy
coding scheme to compress the quantized subbands. Although this coding
technique provides good coding performance, a more sophisticated coding scheme
(e.g., utilizing an arithmetic coder, or a vector quantizer) combined with this
pyramid decomposition could result in substantial coding gains. EPIC is
currently limited to 8-bit monochrome square images, and does not explicitly
provide a progressive transmission capability (although this capability is
easily added since it uses a pyramid representation).
EPIC is available via anonymous ftp from whitechapel.media.mit.edu (IP number
18.85.0.125) in the file pub/epic.tar.Z. This is a unix-compressed tarfile, so
don't forget to put ftp into binary mode. Comments, suggestions, or questions
should be sent to:
Eero P. Simoncelli
MIT Media Laboratory, E15-385
20 Ames Street
Cambridge, MA 02139
Phone: (617) 253-3891, E-mail: [email protected]
References (the third of these describes EPIC and is available via ftp):
Edward H. Adelson, Eero P. Simoncelli and Rajesh Hingorani. Orthogonal
pyramid transforms for image coding. In Proceedings of SPIE,
October 1987, Volume 845.
Eero P. Simoncelli. Orthogonal Sub-band Image Transforms. Master's Thesis,
EECS Department, Massachusetts Institute of Technology. May, 1988.
Edward H. Adelson, Eero P. Simoncelli. Subband Image Coding with
Three-tap Pyramids. Picture Coding Symposium, 1990. Cambridge, MA.
--------------------------- Topic #4 -----------------------------------
From: Ali N. Akansu, New Jersey Institute of Technology.
Date: Tue, 4 Aug 92 13:58:46 -0400
Subject: Available proceedings of njit symposia on wavelets.
PROCEEDINGS OF NJIT SYMPOSIA ON WAVELETS
MULTI-RESOLUTION SIGNAL DECOMPOSITION TECHNIQUES: WAVELETS, SUBBANDS,
and TRANSFORMS (Co-organizers: A.N. Akansu(NJIT), E. Feig(IBM Research)
April 30, 1990(first Wavelet meeting in USA)
Speakers:
I. Daubechies, ``Wavelets: A Different Way to Look at Subband Coding"
S. Mallat, ``Compact Image Representation from Multiscale Edges"
J.W. Woods, ``Subband Coding of Video"
M. Vetterli, ``Filter Banks and Wavelets: Relationships, New Results and
Applications"
A. N. Akansu, ``An Efficient QMF-Wavelet Structure"
E. Feig, ``Algebraic Structure of the Discrete Cosine Transform"
MULTIRESOLUTION IMAGE AND VIDEO PROCESSING: SUBBANDS AND WAVELETS
(Co-organizers: A.N. Akansu(NJIT), M. Vetterli(Columbia U.), J.W. Woods(RPI))
March 20, 1992
Speakers:
E.H. Adelson, ``Steerable, Shiftable Subband Transforms"
A.N. Akansu, ``Some Aspects of Optimal Filter Bank Design for
Image-Video Coding"
A. Jacquin, ``Comparative Study of Different Filterbanks for Low Bit Rate
Subband-based Video Coding"
P.M. Cassereau, ``Wavelet Based Video Coding"
M. Barlaud, ``Image Coding Using Biorthogonal Wavelet Transform and
Entropy Lattice Vector Quantization"
J. Biemond, ``Hierarchical Subband Coding of HDTV"
M. Vetterli, ``Multiresolution Joint Source-Channel Coding for HDTV Broadcast"
J.W. Woods, ``Compression Coding of Video Subbands"
R. Ansari, ``Hierarchical Video Coding: Some Options and Comparisons"
PROCEEDINGS OF TWO NJIT SYMPOSIA(1990, 1992) CAN BE REQUESTED FROM
A.N. Akansu
NJIT ECE Dept.
University Heights
Newark, NJ 07102
$10 each copy, please make your checks payable to NJIT/CCSPR
--------------------------- Topic #5 -----------------------------------
From: Ali N. Akansu, New Jersey Institute of Technology.
Date: Tue, 4 Aug 92 13:58:46 -0400
Subject: Announcement from academic press
ANNOUNCEMENT FROM ACADEMIC PRESS
MULTIRESOLUTION SIGNAL DECOMPOSITION: TRANSFORMS, SUBBANDS, and WAVELETS
by
A.N. Akansu and R.A. Haddad
This book provides an in-depth, integrated, and up-to-date exposition of
the topic of signal decomposition techniques. Application areas of these
techniques include speech and image processing, machine vision, information
engineering, HDTV, and telecommunications. The book will serve as the major
reference for those entering the field, instructors teaching some or all
of the topics in an advanced graduate course, and researchers needing to
consult an authoritative source.
KEY FEATURES and BENEFITS
*The first book to give a unified and coherent exposition of Multiresolution
Signal Decomposition Techniques
*Classroom tested textbook clearly describes the commonalities among three
key methods---transform coding, subband coding, and wavelet transforms
(Solutions manual to the homework problems will be available soon)
*Gives comparative performance evaluations of many proposed techniques
CONTENTS
*Introduction
*Why Signal Decomposition?
*Decompositions: Transforms, Subbands, and Wavelets
*Orthogonal Transforms
*Lapped Orthogonal Transforms(LOT)
*2D Transform Implementation
*Theory of Subband Decomposition
*Two-Channel Filter Banks
*M-band Filter Banks
*Cascaded Lattice Structures
*IIR Subband Filter Banks
*2D Subband Decomposition
*Quantization Effects in Filter Banks
*Filterbank Families: Design and Performance
*Wavelet Transform
*Multiresolution Signal Decomposition
*Wavelet Regularity and Wavelet Families
*Biorthogonal Wavelets and Filter Banks
*Discussions and Conclusion
*Epilogue
*Appendices
*Problems
To obtain a copy of the book please contact to
ACADEMIC PRESS
Book Marketing Department
1250 Sixth Avenue, San Diego, CA 92101-4311
HARCOURT BRACE JOVANOVICH INTERNATIONAL
Orlando, FL 32887
HARCOURT BRACE JOVANOVICH LIMITED
24-28 Oval Road, London NW1 7DX, England
HARCOURT BRACE JOVANOVICH GROUP
(Australia) Pty. Limited
Locked Bag 16, Marrickville
NSW 2204, Australia
HARCOURT BRACE JOVANOVICH JAPAN, Inc.
Ichibancho Central Bldg., 22-1 Ichibancho
Chiyoda-ku, Tokyo 102, Japan
U.S. and CANADIAN CUSTOMERS:
CALL TOLL FREE
1-800-321-5068
Fax:1-800-235-0256
Mon-Fri, 8:00am to5:00pm, Central Time
>From Missouri, Alaska, or Hawaii
CALL 1-314-528-8110
Note from the editor (after calling AP): this book is expected to be
ready october 28th, the estimated price is US$ 59.95.
I also shortened the description of the contents for inclusion in the
digest. The original submission can be found using anonymous ftp
to maxwell.math.scarolina.edu in the file /pub/wavelet/announce/akansu.txt.
--------------------------- Topic #6 -----------------------------------
From: Hans Kuehnel, TU Muenchen Germany.
Date: Mon, 3 Aug 92 17:47:12 +0200
Subject: looking for wavelets with compact support in Fourier-space
Hello everybody,
I am curious to know about wavelet bases with compact support in Fourier-space.
(In analogy to the Haar/Daubechies wavelets, which have compact support in
the space/time domain). Needless to say they should be localized in the
space/time domain as well. If you have references on this topic please
mail them to me ([email protected]) and I'll summarize the results
for the list.
Best regards,
--Hans
Hans Kuehnel ([email protected], Tel. +49-89-3209-3766)
--------------------------- Topic #7 -----------------------------------
From: Klaus Peters, Jones and Bartlett Publishers
Date: August 4, 1992
Subject: Book Announcement
WAVELETS AND THEIR APPLICATIONS
Edited by: Mary Beth Ruskai, Gregory Beylkin, Ronald Coifman,
Ingrid Daubechies, Stephane Mallat, Yves Meyer, and Louise Raphael
Description and Important Features:
Wavelets have become a major new tool in signal analysis, numerical
analysis, and approximation theory with additional applications in
theoretical physics, turbulence and a wide range of other areas.
This book has been designed to provide state-of-the-art surveys by
leading researchers in many of those areas in which wavelets are
making an impact. The book presents applications which utilize the
latest developments in orthonormal wavelets as well as continuous
transforms and non-orthogonal Gabor expansions. A review of the
mathematical theory of non-orthogonal wavelets and summaries of new
methods of constructing orthogonal wavelets are also included.
The following selection of subjects gives some indication of the
scope of the book:
*Construction of simple wavelet bases for fast matrix operations
*The optical wavelet transform
*Wavelets: a renormalization point of view
*Wavelets in numerical analysis
*Wavelets in digital signal processing
*Non-orthogonal wavelets and Gabor expansions and group representations
*The wavelet-Galerkin method for partial differential equations
*Wavelets and quantum mechanics
*Wavelets and filter banks for discrete-time signal processing
1992, ISBN 0-86720-225-4, 474 pp., cloth, $59.95
For additional information, please contact Jones and Bartlett Publishers
Customer Service Department, One Exeter Plaza, Boston, MA 02116,
phone: 1-800-832-0034, fax: 617-859-7675, email: [email protected]
|
| 1409.9 | Wavelet info , how to obtain more | STAR::ABBASI | I spell check | Fri Aug 14 1992 10:31 | 134 |
| From: DECWRL::"[email protected]" "MAIL-11 Daemon" 14-AUG-1992 09:29:40.27
To: star::abbasi
CC:
Subj: Wavelet Digest, Gopher announcement.
Wavelet Information available through Gopher
The Gopher server of the University of South Carolina Department of
Mathematics has been extended to hold a collection of wavelet information.
Currently, this information includes papers, programs, source code, and
USENET messages pertaining to wavelet theory, as well as an interactive
search service for wavelet references and archives of the Wavelet Digest.
1. What is Gopher?
Gopher is a distributed-information system developed at the University of
Minnesota. It consists of both information servers at institutions spanning
the globe, and client programs used by individuals to access and navigate
this data. Gopher has been designed to be easy-to-use; it is menu-based,
and most information can be retrieved using only the cursor keys. Another
major feature of Gopher is the seamless access it brings to distributed
information located on remote Gopher, anonymous FTP, WAIS, WWW, or other
services. For more information on Gopher, check out the Gopher Information
items on most Gopher services, the information via anonymous FTP on
boombox.micro.umn.edu in directory /pub/gopher, or the alt.gopher and
comp.infosystems.gopher USENET newsgroups and the Frequently Asked Questions
posting on Gopher in news.answers.
2. Accessing the USC-Math Gopher service.
Gopher is growing rapidly in popularity on the Internet; as a result, there
is a good chance your local computer system may already have a client
compiled. If your site already possesses a Gopher client (check with your
system administrator), type
gopher bigcheese.math.scarolina.edu
and you should find yourself at the USC Dept. of Mathematics Gopher service;
use the cursor keys to select "Wavelet information" on the menu
(currently item 14), and explore the information offerings of the
service as you please; the cursor keys can be used to navigate
the system.
If your site *doesn't* currently possess a Gopher client, the wavelet Gopher
service may be anonymously accessed using the Telnet program. Type
telnet consultant.micro.umn.edu
and login as "gopher" (case is important). After logging in, you will find
yourself at the Gopher server of the University of Minnesota. To reach the
University of South Carolina Gopher server, choose "Other Gopher and
Information Servers" from the main menu, then servers in "North America."
Choose the "University of South Carolina, Dept. of Mathematics" from the
list of servers; (currently number 71, but this changes as new
servers become available). If you have difficulties reaching Gopher with the
above instructions, ask your local system administrator for assistance, or
contact Brygg Ullmer as [email protected].
If you would like to obtain the Gopher client for installation upon your
system, it is available via anonymous FTP from boombox.micro.umn.edu in
directory /pub/gopher. Versions of Gopher are currently available for Unix,
VMS, NeXT, Macintosh, IBM PC-compatible, and other computer systems.
3. The Wavelet Reference Database.
A feature of the wavelet information on the USC Gopher service is a database
of wavelet references. Currently the references are accessible in two
forms. Individual reference lists from various individuals or papers are
available in their original formats; however, more powerful access is
provided through the local WAIS search engine. With the WAIS facilities,
references and abstracts (when available) are processed to yield a master
full-text index for all citations available, regardless of format. This
index can be queried using either keywords or full natural-language
requests. Documents containing the specified words are ranked on the
basis keyword proximity, frequency, and other factors, and listed in order
of the strength of search matches.
4. If you have references or papers to contribute...
If you have a reference list to literature concerning wavelets that you'd be
willing to contribute, we'd be happy to add it to our database and credit
you as its source. Mail any such lists with the subject "submit" to
[email protected]; BibTeX format is preferable but not necessary.
If abstracts for your references are readily available, please include those
as well... this will allow more accurate selection of articles and will be
more useful to people viewing references. Additionally, if you have papers
related to wavelet theory which are online, particularly if these papers are
available via anonymous FTP, feel free to submit these as well, and they
will be made publically available through the service as storage space
permits. If your papers are available via anonymous FTP, please send the
location of the papers rather than the papers themselves.
5. Other features of the USC-Math Gopher Service.
In addition to the wavelet information available on the above-described
Gopher service, a variety of information of more general interest is
available. Phone directories and libraries of universities around the
world are integrated into the service, as well as collections of technical
reports, electronic books from Project Gutenberg and the Online Book
Initiative, and a variety of other electronic texts. Throughpoints to
major NSF, NASA, NOAA, and other governmental databases are also integrated
into the system, as well as Frequently Asked Questions lists, search
for USENET newsgroup archives, and other bodies of information.
6. Credits
Gopher has been developed by the University of Minnesota.
WAIS is a freely-distributed product of Thinking Machine Corporation.
Contributions to the wavelet reference list have currently been received
from Akram Aldroubi, Bjorn Jawerth, Christopher Koenigsberg,
Juhana Kouhia (from a USENET posting), Eero Simoncelli
(from a USENET posting), Wim Sweldens, and Brygg Ullmer.
The USC-Math Gopher service was developed and is maintained by Brygg Ullmer.
Brygg Ullmer ([email protected] / [email protected])
University of South Carolina / University of Illinois
% ====== Internet headers and postmarks (see DECWRL::GATEWAY.DOC) ======
% Received: by enet-gw.pa.dec.com; id AA23494; Fri, 14 Aug 92 06:28:14 -0700
% Received: by isaac.math.scarolina.edu id AA02517 (5.65c/IDA-1.4.4 for [email protected]); Fri, 14 Aug 1992 09:28:01 -0400
% Message-Id: <[email protected]>
% Subject: Wavelet Digest, Gopher announcement.
% From: [email protected] (Wavelet Digest)
% Date: Fri, 14 Aug 92 9:28:01 EDT
% Reply-To: [email protected]
% To: star::abbasi
% X-Mailer: fastmail [version 2.3 PL11]
|
| 1409.10 | Wavelet Digest, VOL. 1, Nr. 3 | STAR::ABBASI | I spell check | Sat Aug 22 1992 14:13 | 442 |
| From: DECWRL::"[email protected]" "MAIL-11 Daemon" 22-AUG-1992 12:45:08.20
To: star::abbasi
CC:
Subj: Wavelet Digest, Vol. 1, Nr. 3.
Wavelet Digest Saterday, August 22, 1992 Volume 1 : Issue 3
Today's Editor: Wim Sweldens
Today's Topics:
1. Papers available from Aware Inc.
2. Papers available from University of Maryland
3. Call for papers: Journal of Mathematical Imaging and Vision
4. Announcement of a talk on wavelets
5. Two wavelet problems
6. Wavelet question
7. Looking for a job in wavelets
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
Archive site:
Anonyous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet/archive.
Gopher server: bigcheese.math.scarolina.edu.
Current number of subscribers: 1804
--------------------------- Topic #1 -----------------------------------
From: P. Heller, Aware.
Date: Mon, 10 Aug 92 10:42:33 -0400
Subject: Papers from Aware Inc.
We at Aware have a full list of technical reports available for
public distribution, including papers like
Wayne Lawton, ``Necessary and sufficient conditions for constructing
orthonormal wavelet bases"
W. Lawton and H. Resnikoff, ``Multidimensional wavelet bases"
David Pollen, ``Parametrization of compactly supported wavelets"
P. Heller, H. Resnikoff, & R. O. Wells, ``Wavelet matrices and the
representation of discrete functions"
J. Weiss, W. Lawton, et. al. ``Wavelet analysis and the numerical
solution of partial differential equations"
and more.
To request one of these papers or get a full list of the available
papers, contact:
Michael McPherson
Aware, Inc.
One Memorial Drive
Cambridge, MA 02142
(617) 577-1700
[email protected]
--------------------------- Topic #2 -----------------------------------
From: P.S.Krishnaprasad, University of Maryland.
Date: Thu, 20 Aug 92 16:02:54 EDT
Subject: Papers available
Following are abstracts of recent work related to wavelets and frames. Those
interested in obtaining copies of the same may contact the undersigned.
--------------------------------------------------------------------------
Professor P. S. Krishnaprasad | Tel: (301)-405-6843
Department of Electrical Engineering & | Fax: (301)-405-6707
Systems Research Center |
A.V. Williams Building - Rm 2233 | Internet:
University of Maryland | [email protected]
College Park, MD 20742. | [email protected]
--------------------------------------------------------------------------
SYSTEMS RESEARCH CENTER TECHNICAL REPORT, SRC-TR-90-44r1
University of Maryland, College Park, MD 20742.
(also to appear in IEEE Transactions on Neural Networks)
\centerline{\bf Analysis and Synthesis of Feedforward Neural Networks}
\centerline{\bf Using Discrete Affine Wavelet Transformations}
\centerline{by Y.C. Pati and P.S. Krishnaprasad}
In this paper we develop a theoretical description of standard feedfoward
neural networks in terms of discrete affine wavelet transforms. This
description aids in establishing a rigorous understanding of the behavior
of feedforward neural networks based upon the properties of wavelet
transforms. Time-frequency localization properties of wavelet transforms
are shown to be crucial to our formulation. In addition to providing a
solid mathematical foundation for feedforward neural networks, this theory
may prove useful in explaining some of the empirically obtained results in
the field of neural networks. Among the more practical implications of
this work are the following: (1) Simple analysis of training data
provides complete topological definition for a feedforward neural network.
(2) Faster and more efficient learning algorithms are obtained by reducing
the dimension of the parameter space in which interconnection weights are
searched for. This reduction of the weight space is obtained via the same
analysis used to configure the network. Global convergence of the
iterative training procedure discussed here is assured. Moreover, it is
possible to arrive at a non-iterative training procedure which involves
solving a system of linear equations. (3) Every feedforward neural
network constructed using our wavelet formulation is equivalent to a
'standard feedforward network.' Hence properties of neural networks, which
have prompted the study of VLSI implementation of such networks are
retained.
---------------------------------------------------------------
SYSTEMS RESEARCH CENTER TECHNICAL REPORT, SRC-TR-92-44,
University of Maryland, College Park, MD 20742.
(shorter version presented at Conference on Information Sciences and Systems,
Princeton, March 1992)
\centerline{\bf Affine Frames of Rational Wavelets in $H2 ( \Pi^+)$}
\centerline{by Y.C. Pati and P.S. Krishnaprasad}
In this paper we investigate frame decompositions of $H2(\Pi^+)$ as a method of
constructing rational approximations to nonrational transfer functions in
$H2(\Pi^+ )$. The frames of interest are generated from a single analyzing
wavelet. We consider the case in which the analyzing wavelet is rational
and show that by appropriate grouping of terms in a wavelet expansion,
$H2(\Pi^+ )$ can be decomposed as an infinite sum of a rational transfer
functions which are related to one another by dilation and translation.
Criteria for selecting a finite number of terms from such an infinite
expansion are developed using time-frequency localization properties of
wavelets.
-----------------------------------------------------------------
SYSTEMS RESEARCH CENTER Ph.D. Dissertation Series, Ph.D. 92-13
Title of Dissertation:
Wavelets and Time-Frequency Methods in Linear Systems and Neural Networks
Yagyensh C. Pati, Doctor of Philosophy, 1992
Dissertation directed by:Professor P. S. Krishnaprasad,
Department of Electrical Engineering
In the first part of this dissertation we consider the problem of rational
approximation and identification of stable linear systems.
Affine wavelet decompositions of the Hardy space H$^2(\Pi^+)$, are developed as
a means of constructing rational approximations to nonrational transfer functions.
The decompositions considered here are based on frames constructed from
dilations and complex translations of a single rational function.
It is shown that suitable truncations of such decompositions can lead
to low order rational approximants for certain classes of time-frequency
localized systems.
It is also shown that suitably truncated rational wavelet series may be used
as `linear-in-parameters' black box models for system identification.
In the context of parametric models for system identification, time-frequency
localization afforded by affine wavelets is used to incorporate {\em a priori}
knowledge into the formal properties of the model.
Comparisons are made with methods based on the classical Laguerre filters.
The second part of this dissertation is concerned with developing a
theoretical framework for feedforward neural networks which is suitable
for both analysis and synthesis of such networks.
Our approach to this problem is via affine wavelets and the theory of
frames.
Affine frames for L$^2$, are constructed using combinations
of sigmoidal functions and the inherent translations and dilations of
feedforward network architectures.
Time-frequency localization is used in developing methods for the synthesis
of feedforward networks to solve a given problem.
These two seemingly disparate problems both lie within the realm of approximation theory, and our approach to both
is via the theory of frames and affine wavelets.
--------------------------- Topic #3 -----------------------------------
From: Andrew Laine, University of Florida.
Date: Thu, 20 Aug 92 15:47:42 -0400
Subject: Call for papers
Dear Colleagues:
An announcement for papers was made at this summer's
NSF/SIAM wavelet conference for a special issue of
the Journal of Mathematical Imaging and Vision,
focusing on wavelet theory and applications.
This message is to inform you that the
call for papers has been extended beyond
the initial deadline.
Sincerely,
Andrew Laine
Guest Editor,
Journal of Mathematical Imaging and Vision
FINAL CALL FOR PAPERS:
EXTENDED DEADLINE: September 15, 1992
Papers focusing on wavelets and their applications including but
not limited to the topics listed below are solicited:
- image pyramids
- frames and overcomplete representations
- multiresolution algorithms
- wavelet-based noise reduction and restoration
- multiscale edge detection
- wavelet texture analysis and segmentation
- wavelet-based fractal analysis
- multiscale random processes
- image representations from wavelet maxima or zero-crossings
- wavelet coding and signal representation
- wavelet theory and multirate filterbanks
The aim of special issue is to emphasize the role of mathematics
as a rigorous basis for imaging science. Emphasis will be
on innovative or established mathematical techniques applied
to vision and imaging problems in a novel way, including
new developments (and problems) in mathematics arising from
these applications. Expository articles, and short
articles (no longer than 10 pages) are also encouraged.
The special issue will be published in early 1993.
Manuscripts should not exceed 30 pages double spaced.
Please submit three copies of your manuscript to:
Professor Andrew Laine
Department of Computer and Information Sciences
Computer Science and Engineering Building (CSE)
Room 301
University of Florida
Gainesville, FL 32611
Email: [email protected]
Phone: (904) 392-1239
Fax: (904) 392-1220
--------------------------- Topic #4 -----------------------------------
From: [email protected] (George D. Phillies)
Date: Thu, 20 Aug 1992 10:46:45 -0400
Subject: Talk.
The following is the title and abstract for a lecture I will be giving
at WPI on wavelet decompositions and their applications. The talk will
be given on Moday, August 31, 1992, at 4PM in the Olin Hall of Physics.
The treatment of wavelets will be strictly introductory, and aimed more
at physicists than mathematicians. The new material will be the use of
wavelet decompositions to study the Ising model of a magnet, and the
sorts of information one can extract from Monte Carlo dynamics
simulations via wavelet decompositions. We have just begun writing up
our results, so preprints are not available.
To reach WPI, take Route 290 to the Route 9-Highland Street exit, go
west on Highland Street approximately 1 mile to West Street (traffic
light), and three blocks north (right turn, if moving away from Route
290). Olin Hall is on the west (left) side of the street, the second
building before West Street comes to an end.
* * * * *
WAVELET DECOMPOSITIONS:
INTRODUCTION AND APPLICATION
TO THE ISING MODEL
George D. J. Phillies
Department of Physics
Worcester Polytechnic Institute
First, I will introduce wavelet expansions, which are a new
mathematical method that may be useful in physics. Second, I will show
how a specific wavelet transform, the Burt-Adelson$^{1}$ decomposition, can
be used to describe statics and dynamics of the Ising model. The
Burt-Adelson decomposition was originally introduced as a tool for
image processing; the Ising model is a highly-simplified statistico-
mechanical description of a spin lattice, nominally similar to the
lattices found in magnets.
Wavelets may formally be viewed as extensions of the orthogonal
functions familiarly used in physics. Isolated examples of wavelet
expansions, such as Haar and Gabor transforms, have long been known.
Recently, Mallat$^{2}$, Meyer$^{3}$, Daubechies$^{4}$ and others have
shown that Haar and Gabor transforms are special cases of an extensive
set of mathematical functions. Wavelets provide complete, sometimes
non-orthogonal, sets of basis functions. Some wavelet expansions can
be generated recursively, suggesting computation efficiencies. Other
wavelet expansions have compact support, so that, e.g., in a wavelet
decomposition of a piece of music, alteration of a single note changes
local coefficients without affecting most of the expansion.
While there is a large mathematical literature on wavelets, concrete
applications of wavelet expansions in the physics literature do not
appear to be common. I will describe Monte Carlo simulations (made by
J. Stott and I last Summer) on static and dynamic properties of the 1-D
Ising model, as revealed by wavelet expansions.
1) P. J. Burt and E. H. Adelson, IEEE Trans. on Communications 31, 532
(1983).
2) S. Mallat, {\em A Theory for Multiresolution Signal
Decomposition: The Wavelet Transform } (see IEEE Trans.\ on Pattern
Analysis and Machine Intelligence}, 11, 674-693 (1989).
3) Y. Meyer, {\em Ondelettes et Functions Splines}, Ecole
Polytechnique (Paris) (1986).
4) Ingrid Debauchies, Commun.\ Pure and Applied Mathematics
41, 909-996 (1988).
--------------------------- Topic #5 -----------------------------------
From: Steve Lutes, Texas A&M University.
Date: Wed, 19 Aug 92 21:21:30 CDT
Subject: Two wavelet problems
Hello! I've got two wavelet probelms for your consideration.
They are:
- First Problems-Detecting Abnormal EEG (brainwaves) Waveforms:
Brain/CNS (Central Nervous System) pathologies often produce ab-
normal waveforms in the EEG. Can wavelet techinques detect these wave-
forms? Because EEG waveform interpetation is a specialized field with
jargon, I've prepared a PostScript document explaining the jargon, giv-
ing pictures of abnormal EEG waveforms, and explaining this problem in
more detail. Please download and print this file before commenting.
The file name is waveprob.ps.
(Note from the editor: this file can be retrieved using anonymous
ftp to maxwell.math.scarolina.edu, directory /pub/wavelet/announce.)
- Second Problem: Making Wavelets from Data or Custom Wavelets.
Can you make a wavelet from data? That is, can you take a waveform
and change it into a wavelet? Addendum: I heard that you cam use splines
to generate wavelets. Could you use splines to approximate a waveform,
wavelet?
Reply to [email protected].
--------------------------- Topic #6 -----------------------------------
From: Ming-Haw Yaou, National Chiao Tung University Taiwan.
Date: Mon, 17 Aug 92 16:18:48 CST
Subject: Wavelet question.
In the wavelet digest vol.1 nr.1 (Topic #6), I asked a question
about orthonormal wavelet transform.
It is very kind of many readers to email me their constructive
advice. I also have to apologize for that some misunderstanding was
happened due to my unclear expression in the problem. To specify my question
more clearly, I correct some mistakes in the properties (a) and (b) and express
them in explicit equation.
------------------ The rewritten properties (a) and (b) ------------------
(a) Both Pmn(x) and Qmn(x) are orthonormal bases. This can be written as
< P(x-n) , P(x-m) > = delta(m,n).
< Q(x-n) , Q(x-m) > = delta(m,n).
Pmn(x) and Qmn(x) are mutually orthogonal (under the same value of m).
This can be expressed as
< P(x-n) , Q(x-m) > = 0.
(b) Denote the second derivatives of P(x) and Q(x) as P"(x) and Q"(x).
P"(x) and Q"(x) satisfy
< P"(x-n) , Q"(x-m) > = 0.
(c) Further constraints (for avoiding trivial solution) :
The regions of support of P(x) and Q(x) should not be non-overlapped.
Also the regions of support of P(x) and Q(x) should be greater than 1,
i.e., in the interval [p,q] and |p-q| > 1.
Where operation < a , b > stands for the inner product of continuous functions
a with b. Symbol delta(m,n) stands for the Kronecker delta function.
Variables m and n are integers and x are real.
The constraints in property(a) are well known as the necessary conditions in
the construction of dyadic orthonormal wavelet bases. The property(b) came
from my current study ( I wish to solve curve fitting problem from wavelet
theory).
PS:
It is known that the property(a) can be indirectly solved by
transferring the constraints to the filters of an associated QMF banks.
And P(x) and Q(x) can be iteratively calculated from the filters. I try to
transfer the additional property(b) to the filters. But It seems not to be
very easy.
I will very appreciate if any answer or advice is sent from you.
Please e-mail to : [email protected]
Thank in advance, Ming-Haw Yaou.
--------------------------- Topic #7 -----------------------------------
From: Evan Greenberg, University of Massachusetts.
Subject: Looking for a job in wavelets.
I recently completed my M.S. in Applied Mathematics at the University of
Massachusetts, Amherst. I'm currently seeking employment in the mathematical
sciences, preferably along the lines of research or applications/research.
My undergraduate education is a B.S.E.E. from Tufts University.
As Wavelets seems to be an area which holds great promise for the future (and
present) in both pure and applied areas, I'd be very enthusiastic about a good
opportunity for wavelet-related work where I could also gain more knowledge on
some specific topics. I currently have some basic background in wavelets and
multiresolution analysis; my interests also include Kalman filter theory and
signal processing.
Ideally, I'd like to be able to work in an environment that provides both
flexibility and good support for learning. Nothing would be please me more
than being able to actually work with mathematics that I've learned in grad
school. Although I'd love to get a Ph.D., I'd rather be employed full-time
now. Wavelets are in a diverse variety of applications now, but I'm especially
interested in their use with audio compression technology and state-of-the-art
recording. I have some work experience in non-mathematical engineering, during
the time between my bachelor's and master's degrees. I'd welcome any insight
into how I might find worthwhile employment, in addition to any actual job
opportunites.
email: [email protected] phone (413) 253-0555
address: evan greenberg, 216 bel-air drive, longmeadow, ma 01106
-------------------- End of Wavelet Digest -----------------------------
% ====== Internet headers and postmarks (see DECWRL::GATEWAY.DOC) ======
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% Received: by isaac.math.scarolina.edu id AA22965 (5.65c/IDA-1.4.4 for [email protected]); Sat, 22 Aug 1992 12:44:08 -0400
% Message-Id: <[email protected]>
% Subject: Wavelet Digest, Vol. 1, Nr. 3.
% From: [email protected] (Wavelet Digest)
% Date: Sat, 22 Aug 92 12:44:08 EDT
% Reply-To: [email protected]
% To: star::abbasi
% X-Mailer: fastmail [version 2.3 PL11]
|
| 1409.11 | pointer to a course in Wavelets at Northeastern univ. | STAR::ABBASI | I spell check | Sat Aug 22 1992 14:30 | 24 |
| Northeastern Univ. has a course this fall in the dept of EE titled
"Introduction to Wavelets", given by A.J.Devaney. ECE 3504, special
topics in DSP.
Abstract:
the course will use the book, An intro. to Wavelets by Charles Chui
and will cover the basic theory of Wavelets as developed by
Ingid Daubechies, Mallat, and others.
the first half of the course will be devoted to fundamental
mathematical concepts and will require the student to have at least a
basic familiarity with function spaces and complex variable theory.
the second half of the course will deal with applications and will
require extensive reading of the current literature by the student.
applications on interest will include the use of Wavelets in data
compression, signal detection and estimation, and radar and
communication theory applications. application drawn from physics
literature will also be included if time permits.
EE dept 617-437-5281
Network Northeastern 617-437-5620 (to ask if course if broadcasted
over the network)
/Nasser
|
| 1409.12 | Wavelet Digest, VOL .1, Nr. 4 | STAR::ABBASI | Have you spelled checked today? | Sat Sep 05 1992 13:20 | 305 |
| From: DECWRL::"[email protected]" "MAIL-11 Daemon" 5-SEP-1992 08:10:45.83
To: star::abbasi
CC:
Subj: Wavelet Digest, Vol. 1, Nr. 4.
Wavelet Digest Saturday, September 5, 1992 Volume 1 : Issue 4
Today's Editor: Wim Sweldens
Today's Topics:
1. Wavelet session at ANS annual meeting.
2. Ph.D. Thesis available
3. Replies of question in WD 1.2, topic 6.
4. Question
5. Question
6. Paper published.
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
Archive site:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet/archive.
Gopher server: bigcheese.math.scarolina.edu.
Current number of subscribers: 1904
--------------------------- Topic #1 -----------------------------------
From: Felix C. Difilippo, Oak Ridge National Laboratory.
Date: Thu, 27 Aug 1992 13:29:13 -0400 (EDT)
Subject: Wavelet session at ANS annual meeting.
The Mathematics and Computation Division of the American Nuclear Socie-
ty is organizing a Special Session for the annual meeting of the Society enti-
tled Application of Wavelet and Fourier Transforms. The meeting is going to
take place in San Diego, june 20-24, 1993. The idea is to make possible the
transference of ideas in this new field to nuclear science and engineering.
There are areas like the processing of signals from nuclear power plants,
the analysis of nuclear cross sections ans the solution of PDE's relevant
to the nuclear industry that can benefit from the rapid development of the
wavelet analysis.
Papers are wellcome, for more information:
Felix C. Difilippo
Oak Ridge National Laboratory
P. O. Box 2008
Oak Ridge. TN 37831-6363
Phone: (615) 574-6188 FTS 624-6188
Bitnet: FCD@ORNLSTC
Internet: [email protected]
--------------------------- Topic #2 -----------------------------------
From: Touradj EBRAHIMI, Swiss Federal Institute of Technology at Lausanne.
Date: Sat, 22 Aug 1992 23:41:29 +0200
Subject: Ph.D. Thesis alert
Title: Perceptually derived localized linear operators
- Application to image sequence compression -
by : Touradj EBRAHIMI
Directed by: Professor Murat Kunt
Signal Processing Laboratory
Swiss Federal Institute of Technology at Lausanne (EPFL)
CH-1015 Lausanne
date: June 12th, 1992
Abstract
The theory of linear operators covers a large number of applications
in applied mathematics and signal processing. This thesis discusses
three commonly used linear operators, namely, the short time Fourier
transform, the wavelet decomposition and the subband decomposition.
Common features and differences between the above operators are
examined in the frame work of image sequence compression. In
particular, the Gabor expansion, as a special case of short time
Fourier transform in discussed in details. A new and efficient
technique to perform Gabor expansion of signals is introduced. A
comparative study between the above approach and other commonly used
techniques for Gabor expansion of signals is given in terms of
computational complexity. The subband decomposition of signals and the
wavelets theory are two sub-classes of linear operators which were
developed in two different disciplines. However, there exist big
similarities between the two approaches. After a brief introduction of
subband decomposition and wavelet theory, the equivalence and
differences between them are discussed, in both orthonormal and
biorthonormal cases. The multiresolution representation of data is
considered in particular. A new approach to filter bank design in
time domain, using matrix formulation is introduced. Using this
approach, an efficient filter bank with low complexity and good
frequency response is designed. The characteristics of this filter
bank are compared with that of other commonly used short kernel filter
banks, for video compression applications. The generic coding is an
important component in any modern communication system. In this
thesis, a generic video codec is introduced which is able to compress
efficiently image sequences with any format at desired bitrate,
without any change in its architecture. The approach is based on
multiresolution representation of data, which is generated by the
filter bank proposed in this work. The use of multiresolution data
structure in conjunction with other components of the system such as
multiresolution motion estimation, allows a simple and efficient
implementation. Simulations on typical image sequences show that it is
possible to perform generic coding with reduced complexity and good
efficiency.
---
Copies of this thesis can be obtained by requesting from the
above address. A postscript version of the same document (with lower
quality of pictures) can also be obtained from ``[email protected]''.
Touradj EBRAHIMI
Swiss Federal Institute of Technology at Lausanne
Signal Processing Laboratory
1015 Lausanne L TTTTTTT SSSSSS
Switzerland L T S
L T SSSSSS
tel: (+4121) 693 2796 L T S
fax: (+4121) 693 4660 LLLLLL T SSSSSS
e-mail: [email protected]
--------------------------- Topic #3 -----------------------------------
From: Hans Kuehnel.
Date: Fri, 28 Aug 92 14:50:01 +0200
Subject: Replies of question in WD 1.2, topic 6.
Hello everybody,
here's the summary of replies on my question about
"wavelets with compact support in Fourier-space", Topic #6 in
Wavelet Digest, Wednesday, August 5, 1992, Volume 1, Issue 2
My original question:
> I am curious to know about wavelet bases with compact support in Fourierspace
> (In analogy to the Haar/Daubechies wavelets, which have compact support in
> the space/time domain). Needless to say they should be localized in the
> space/time domain as well. If you have references on this topic please
> mail them to me and I'll summarize the results
> for the list.
Thanks to all who responded:
> From: Wim Sweldens <[email protected]>
> From: [email protected] (Lolina Alvarez)
> From: [email protected] (Mladen Victor Wickerhauser)
The punchline was:
Wavelets with compact support in Fourier space are in fact the original ones.
Y. Meyer was the first to design them. As, e.g., described in
(P. Lemarie and Y.Meyer, Ondelettes et bases Hilbertiennes, Rev.
Mat. Iberoamericana 2 (1986), 1-18) the scaling function looks something
like (LaTeX notation):
\[
\hat\phi(\omega)=\left\{
\begin{array}{ll}
1,
&\mbox{for $|\omega|\leq {2\pi\over 3}$}\\
\cos\left({\pi\over 2}\nu({3\over 2\pi}|\omega|-1)\right),
&\mbox{for ${2\pi\over 3} < |\omega| < {4\pi\over 3}$}\\
0,
&\mbox{otherwise}
\end{array}
\right.
\]
Here, $\nu(x)$ is a $C^k$ function with
$
\nu(x)=
\left\{
\begin{array}{ll}
0, &\mbox{$x\leq 0$}\\
1, &\mbox{$x>1$}
\end{array}
\right.
$
and $\nu(x)+\nu(1-x)=1$.
The case
$
\nu(x)=
\left\{
\begin{array}{ll}
0, &\mbox{$x<{1\over 2}$}\\
1, &\mbox{$x\geq{1\over 2}$}
\end{array}
\right.
$
corresponds to the Shannon wavelet.
The replies in detail:
------
> From [email protected]
>
> Wavelets with compact support in Fourier space are actually older then
> the ones with compact support in time space.
> The most famous ones are:
> 1. the Meyer wavelet: has faster then inverse polynomial decay in time space
> and is infinitely many times differentiable and orthogonal.
> ref: Yves Meyers book "Ondelettes" published by Hermann in Paris (French).
> 2. the Shannon wavelet:
> in this case the scaling function is the famous Shannon sampling function:
> phi(x) = sin(pi x) / (pi x), and the wavelet is
> psi(x) = [ sin(2 pi x) - sin(pi x) ] / ( pi x ).
> They have compact support in Fourier space and are orthogonal in time
> space, the decay however is very poor.
------
> From [email protected]
>
> Dear Hans, On page 74 of the first volume of his book on wavelets-ondelettes
> Meyer considers a wavelet basis generated by a function with compactly
> supported Fourier transform. A similar example is presented by David on his
> Lecture Notes # 1465, page 4 and he calls it Meyer's basis.
------
> From [email protected]
>
> Lemarie and Meyer [1] first constructed band-limited wavelets, and their
> construction has been simplified in [2]. Recent work by Auscher, Bonami,
> Soria and Weiss in `Revista matematica iberoamericana' has given a complete
> characterization.
>
> [1] Meyer, "Ondelettes et operateurs, Vols I,II", Hermann, Paris (1990)
> [2] Ausher, Weiss, Wickerhauser, "Local sine and cosine bases...", in
> "Wavelets--a tutorial in theory and applications", ed. Chui, Academic Press,
> New York (1992)
------
If someone has some more references on the subject I'd be happy to receive
them.
Regards,
Hans Kuehnel ([email protected], Tel. +49-89-3209-3766)
--------------------------- Topic #4 -----------------------------------
From: Raj Patil, USDA-ARS, NMSU-CRL.
Date: Fri, 28 Aug 92 10:48:31 MDT
Subject: question
Hi:
I am looking for information on application of wavelet transform to
speech signal processing. I am interested in speech synthesis and recognition.
I would appreciate if someone working in this area could tell me where can
find more information. Thanks..
best regards,
Raj Patil
USDA-ARS, NMSU-CRL
[email protected]
--------------------------- Topic #5 -----------------------------------
From: Lennart Damm, Ericsson Radar Electronics AB, Stockholm, Sweden.
ate: Wed, 26 Aug 1992 11:28 +0200
Subject: question
A wavelet related question seeking an answer:
How do I use the wavelet transform to solve differential equations?
Computational speed (in general or compared to e.g. Fourier transform)?
References?
Lennart Damm, Systems Design Engineer
Ericsson Radar Electronics AB, S-164 84 Stockholm, Sweden
Email: [email protected]
--------------------------- Topic #6 -----------------------------------
From: Paul Kainen, University of Maryland.
Date: Sun, 16 Aug 92 17:25:51 -0400
Subject: Wavelet paper published.
H. J. Caulfield, Wavelet transforms and their relatives, Photonics Spectra,
Aug. 1992, p.73.
<< Several interesting claims are made regarding the power of wavelet
transform analysis to compress data and do pattern recognition.
It is argued that optical implementation allows continuous changes
in frequency, location and amplitude of wavelets.
Using a "matched filter" model, a signal of dimension s is expanded
into a signal of dimension 2s for s = 1 and 2. However, the known
problems of optical implementation such as alignment and analog
control of error are not addressed. Caulfield asserts that "This
research is likely to represent an important new direction for
optical processing." Since Gabor and Fourier transforms both have
natural optical implementations, Caulfield is surely correct in his
conclusion but supporting evidence may need modification. >>
-------------------- End of Wavelet Digest -----------------------------
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% Message-Id: <[email protected]>
% Subject: Wavelet Digest, Vol. 1, Nr. 4.
% From: [email protected] (Wavelet Digest)
% Date: Sat, 5 Sep 92 8:09:58 EDT
% Reply-To: [email protected]
% To: star::abbasi
% X-Mailer: fastmail [version 2.3 PL11]
|
| 1409.13 | Wavelet digest , Vol .1 , Nr. 5 | STAR::ABBASI | the poet in me want to rise | Thu Oct 01 1992 00:34 | 177 |
| Subj: Wavelet Digest, Vol. 1, Nr. 5.
Wavelet Digest Wednesday, September 30, 1992 Volume 1 : Issue 5
Today's Editor: Wim Sweldens
Today's Topics:
1. ANS wavelet session: call for papers
2. Ninth Auburn miniconference on harmonic analysis
3. Gabor expansion review.
4. Question
5. Question
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
Archive site:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet/archive.
Gopher server: bigcheese.math.scarolina.edu.
Current number of subscribers: 2038
--------------------------- Topic #1 -----------------------------------
From: Felix C. Difilippo, Oak Ridge National Laboratory
Date: Tue, 15 Sep 1992 15:54:02 -0400 (EDT)
Subject: ANS wavelet session
CALL FOR ABSTRACTS
The Mathematics Division of the American Nuclear Society
is organizing a Session entitled "Application of Wavelet and Fourier Transform"
for the annual meeting in San Diego(CA) ,June 20-24,93. The idea is to bring
the developments of this new field to Nuclear Engineering and related science;we
envision that the analysis of 1) signals from nuclear power plants, 2) neutron
cross sections and 3) turbulences and the solution of partial differential equa-
tions are fields where the transfusion of ideas is obvious.
The session will then be very general and we hope to include
tutorial talks. Authors should prepare a summary with the following guidelines:
CONTENT: Introduction, Description of the work, Results, References.
LENGTH: At least 450 words (excluding tables and figures).No more than
900 words(including tables and figures. Count figures and tables
as 150 words each and lines of equations as 10 words each.
SUBMIT: your abstract to
Loyd A. Wright, Technical Program Chair
Attn: Transactions Office
American Nuclear Society
555 North Kensington Av.
La Grange Park, IL 60525
the deadline is Jan 8,93 ,the abstract is going to be published in the
Transactions of the American Nuclear Society; please ask me for the forms
that should go with your abstract. The presentation of the papers is going
to take one or two sessions, depending of the number of authors, each
author has 20 minutes for the talk; for those willing to give a tutorial
we plan to reserve 40 minutes.
Felix C. Difilippo
Oak Ridge National Laboratory
(615) 574-6188
Fax (615) 574-9619
[email protected]
--------------------------- Topic #2 -----------------------------------
From: Bjorn Jawerth, University of South Carolina
Date: Tue, 15 Sep 92 09:20:19 EDT
Subject: Ninth Auburn miniconference on harmonic analysis
NINTH AUBURN MINICONFERENCE ON HARMONIC ANALYSIS
AND RELATED AREAS
"WAVELETS AND APPLICATONS"
Sponsored by NSF
Friday and Saturday, December 4-5, 1992
The four principal speakers will be
Colin Bennett, University of South Carolina
Ronald DeVore, University of South Carolina
Robert Sharpley, University of South Carolina
Victor Wickerhauser, Washington University.
The principal speakers will deliver a series of 8 1-hour lectures covering
wavelet theory and applications from the introductory level to applications
to image and data compression, to PDE's, and other areas of applied
mathematics, physics, and engineering.
Anyone who would be interested in participating in the Miniconference should
contact
Geraldo DeSouza (205) 844-6565 (office) (205) 821-0533 (home)
[email protected] (email)
or
Jack Brown (205) 844-6595 (office) (205) 887-9612 (home)
[email protected] (email)
--------------------------- Topic #3 -----------------------------------
From: Shidong Li, University of Maryland
Date: Thu, 17 Sep 1992 14:54:24 -0400 (EDT)
Subject: Gabor expansion review.
I have a short review of some recent advances in the Garbor expansion.
Hopefully, it can bring some attention to readers who are interested in.
Shidong Li
Department of Applied mathematics & Statistics
University of Maryland
Baltimore, MD 21228
Note from the editor: This review (3 pages) is titled "A Look at Recent
Advances in the Gabor Expansion" and it is available as a tex file.
Ftp to maxwell.math.scarolina.edu, file /pub/wavelet/papers/gabor.tex.
--------------------------- Topic #4 -----------------------------------
From: Michael Nikolaou, Texas A&M University
Date: Mon, 7 Sep 1992 10:51:13 -0500 (CDT)
Subject: Question
I would appreciate any information on the use of wavelets for the solution
of partial differential equations, particularly nonlinear hyperbolic.
Michael Nikolaou
Chemical Engineering
Texas A&M University
College Station, TX 77843-3122
[email protected]
--------------------------- Topic #5 -----------------------------------
From: Haihong Li, IGP, ETH-Hoenggerberg, Zuerich
Date: Sun, 6 Sep 92 14:19:49 +0200
Subject: question
Hello, all,
I'm looking for information on application of wavelet transform to image
processing. I'm interested in the extraction of edge, valley and ridge in
image. Could anybody working this area tell me where can find more informa-
tion. Any help will be appreciated.
Thanks in advance.
Best regards!
Haihong Li
IGP, ETH-Hoenggerberg, Zuerich
[email protected]
-------------------- End of Wavelet Digest -----------------------------
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|
| 1409.14 | Wavelet digest , Vol .1 , Nr. 6 | STAR::ABBASI | life without the DECspell ? | Fri Oct 09 1992 17:31 | 519 |
| Subj: Wavelet Digest, Vol. 1, Nr. 6.
Wavelet Digest Friday, October 9, 1992 Volume 1 : Issue 6
Today's Editor: Wim Sweldens
Today's Topics:
1. Course on wavelets and applications
2. Short course on wavelets
3. Question
4. Call for papers: Conference on Information Sciences and Systems
5. Preprint available from University of Colorado
6. Preprints available from MIT Media Lab
7. Conference announcement: Approximation, Probability and Related Fields
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
Archive site:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet/archive.
Gopher server: bigcheese.math.scarolina.edu.
Current number of subscribers: 2117
--------------------------- Topic #1 -----------------------------------
From: James S. Byrnes, Prometheus Inc.
Date: Thu, 1 Oct 1992 11:04:43 -0400
Subject: Course on wavelets and applications
PROMETHEUS INC.
Preliminary Announcement
WAVELETS DOWN UNDER
Adelaide, South Australia
18-22 January 1993
A 4-day intensive course
Wavelets with Applications
18 January: Professor Guido Weiss, Washington University,
A basic introduction, with an overview of applications.
19 January: Professor John Benedetto, University of Maryland,
Sampling and frames.
20 January: Dr. Ramesh Gopinath, Rice University,
Signal processing and image analysis.
21 January: Professor M. Victor Wickerhauser, Washington University,
Data compression.
22 January: An extra day, devoted to recent research results.
Cosponsors: University of Adelaide, University of South Australia Signal
Processing Research Institute, IEEE South Australia
For more information, please contact:
Jim Byrnes
President, Prometheus Inc.
email: [email protected]
--------------------------- Topic #2 -----------------------------------
From: Gordon Erlebacher, ICASE, NASA Langley Research Center
Date: Fri, 2 Oct 92 09:33:45 -0400
Subject: Short course on wavelets
ICASE/LaRC SHORT COURSE ON WAVELETS
February 22-26, 1993
HOLIDAY INN - HAMPTON COLISEUM HOTEL AND CONFERENCE CENTER
Hampton, VA
We are pleased to announce that the Institute for Computer Applications in
Science and Engineering (ICASE) and NASA Langley Research Center will conduct
a Short Course on Wavelets at the Holiday Inn - Hampton Coliseum Hotel and
Conference Center from February 22 to 26, 1993.
The objective of this course is to give scientists and engineers a practical
understanding of wavelets: their origins, their purpose, their use, and their
prospects. The objective is NOT to talk about the latest advances in the
subject, but rather to impart practical knowledge to allow scientists and
engineers to evaluate objectively where these new tools stand in relation to
their needs.
There will be two four hour lectures a day, each will cover a particular theme
in the field of wavelets. The total course will consist of ten lectures, will
cover five themes and will last a week. The course is intended to cover
applications of wavelets as a diagnostic tool, and the use of wavelet basis
functions to solve differential equations. The contents of the course will be
limited to current and applicable knowledge.
The topics and lecturers are:
INTRODUCTION TO WAVELETS - Gilbert Strang, M.I.T., USA
1. Fast Wavelet Transforms versus FFT
2. Accuracy of Approximation by Wavelets
3. Some Applications to Signal Compression
4. Dilation Equation and Infinite Products of Matrices
5. Continuous Dilation Equation
WAVELET TRANSFORMS - Philippe Tchamitchian, IMST-LUMINY, France
1. Multiresolution Schemes and Related Techniques
- Orthogonal Wavelets
- Biorthogonal Wavelets
- Wavelets on the Finite Interval
- Wavelet Packets
2. Wavelets and Operators
WAVELETS AND NUMERICAL ALGORITHMS - Martin Beylkin, University of CO, USA
1. Standard and Non-Standard forms
2. Fast Algorithms
- Solution to Integral Equations
- Matrix Manipulations
3. Computation of the Generalized Inverse and other Functions of Matrices
4. Wavelet Representations
- Differential Operators
- Fractional Derivatives
5. Solving Two-Point (elliptic) Boundary-Value Problems
- Diagonal Preconditioning
WAVELET ANALYSIS OF FRACTALS: FROM MATHEMATICAL CONCEPTS TO
EXPERIMENTAL REALITY - Alain Arneodo, CNRS, France
1. The Continuous Wavelet Transform as a Scanner of Singularities
2. The Multifractal Formalism Revisited by Wavelets
3. Wavelet Analysis of Fully-Developed Turbulence Data
4. Uncovering Fibonacci Sequences in the "Quasifractal" Morphology of
Diffusion-Limited Aggregates
5. The Optical Wavelet Transform
WAVELET-BASED NUMERICAL METHODS AND DIAGNOSTICS -
Jacques Liandrat, IMST, France
1. Numerical Algorithms for Partial Differential Equations
- Equations with Constant Coefficients
- Equations with Non-Constant Coefficients
2. Local Scale Decomposition for Analysis and Modeling in Fluid Mechanics
- Transition to Turbulence
- Fully-Developed Turbulence
There will be ample time for question and answer sessions with the lecturers. Lecture notes will be made available prior to the course to increase its
effectiveness.
Due to space considerations, attendance will be limited to about 100 people.
There will be a registration fee which is still undecided. If you wish to
attend or would like further information, please fill in the attached form and
return BY NOVEMBER 30, 1992 to
Emily Todd,
ICASE, Mail Stop 132C,
NASA Langley Research Center,
Hampton, VA 23681-0001.
Tel. 804- 864-2175
E-mail: [email protected]
FAX: (804) 864-6134
---------------------------------------------
POSITION AND NAME:
ADDRESS:
TELEPHONE NUMBER:
E-MAIL:
FAX:
FIELDS OF INTEREST:
CURRENT WORK RELATED TO THE USE OF WAVELETS:
--------------------------- Topic #3 -----------------------------------
From: Dale Bryan
Date: Mon, 5 Oct 92 08:01:12 -0700
Subject: question
I'm working on a project that involves transmitting reconnaisance
video from a remote robotic vehicle through a rf data link. I am
interested in finding out if there is any work going on in video
compression utilizing 3D wavelet transformations.
Dale Bryan
email: bryan.nosc.mil
--------------------------- Topic #4 -----------------------------------
From: Thordur Runolfsson
Date: Wed, 7 Oct 92 11:51:06 EDT
Subject: call for papers
Contributed by Thordur Runolfsson ([email protected])
CALL FOR PAPERS: CISS 93
Conference on Information Sciences and Systems
March 24-26, 1993
Baltimore, Maryland
Sponsor: The Johns Hopkins University.
Submit a "regular paper" or "short paper" designation, title, summary,
and a list of 3-4 keywords by January 15, 1993, to:
1993 CISS
Department of Electrical and Computer Engineering
The Johns Hopkins University
Baltimore, MD 21218
Phone (410) 516-7033
Fax (410) 516-5566
--------------------------- Topic #5 -----------------------------------
From: Lawrence Baggett, University of Colorado
Date: Fri, 9 Oct 92 13:16:26 -0600
Subject: preprint available
The following preprint is available.
General Existence Theorems for Orthonormal Wavelets,
An Abstract Approach
L. Baggett, A. Careyg, W. Morang, P. Ohring
Abstract:
Methods from noncommutative harmonic analysis are used to
develop an abstract theory of orthonormal wavelets.
The relationship between the existence of an
orthonormal wavelet and the existence of a multi-resolution is
clarified, and four theorems guaranteeing the
existence of wavelets are proved.
As a special case of the fourth theorem,
a generalization of known results on the existence of
smooth wavelets having compact support is obtained.
Request by email to [email protected]
--------------------------- Topic #6 -----------------------------------
From: Steve Mann, Media Lab, MIT
Date: Wed, 30 Sep 92 22:27:29 -0400
Subject: new preprints
``The Chirplet'' Submitted to IEEE SP special issue on wavelets.
ftp 18.85.0.2
Name (18.85.0.2:loginname): anonymous
ftp> cd pub
ftp> cd steve
ftp> get README
The README file lists ``The Chirplet'' as well as some other related
papers, and tells what the filenames are, along with a brief summary
of each.
In short, a ``chirplet'' is a piece of a chirp, in the same sense that
a wavelet is, loosely speaking, a piece of a wave. In fact, it was
using this loose definition (which really embodies time-frequency-scale
transforms as opposed to just time-scale transforms) that led to the
extension to time-frequency-scale-shear transforms, or ``chirplets''.
The ``wavelet of constant shape'' (or just the `wavelet', using the
modern definition) is a member of a family of functions formed from
one primitive element -- the mother chirplet -- whereby each member
represents an AFFINE change of coordinates, imposed on the mother
chirplet. Thus, for simplicity of discussion, let us refer to the
wavelet as the ``physical affinity'' -- for 1-D wavelets, that means
2 degrees of freedom. Affine coordinate changes in the physical domain
are characterized by time dilation (or contraction) ``a'' and time
shift (translation) ``b''.
The chirplet, is the family of time-frequency-affinites: the family
is generated from one mother chirplet by affine coordinate changes in
the time-frequency plane. Thus we have translation in time (``b'' above)
and translation in frequency, and dilation-in-time. These form a
TFS space, but there can also be shears and rotations in the TF plane
which lead to chirping when viewed in the physical domain. Just like
putting the data window over a linear FM (chirp) function. Strictly
speaking, the dilation-in-time and dilation-in-frequency impose an
area-preserving (symplectic) geometry, though a philosophical framework
for varying the time-bandwidth product is presented, so that the bases
of the underlying group representation may be conceptualized as a
TF-affinity, in contrast to the physical (e.g. time) affinity of the
usual wavelet transform. This form of the chirplet has been successfully
applied to detection of floating iceberg fragments in radar, and has
given very good detection probablities, better than the other more
traditional methods. Others have also built upon this early work on
radar.
The paper also includes some of the more recent extensions to a family
of chirplets of the form g((ax+b)/(cx+1)) where c is the chirpiness.
This generalization of the wavelet is different than the TF-affinities
but the two generalizations are combined in the paper, and explanation
is given as to when subgroup transforms of the former are more appropriate
versus when the latter is the more appropriate.
--------------------------- Topic #7 -----------------------------------
From: George Anastassiou, Memphis State University
Date: 1 Oct 92 14:22:28 CDT
Subject: Conference announcement: Approximation, Probability and related fields
INTERNATIONAL CONFERENCE
APPROXIMATION, PROBABILITY AND RELATED FIELDS
May 20-22, 1993
University of California, Santa Barbara
The main purpose of the conference is to bring together national
and international researchers in approximation, probability and
related fields in an intense and intimate environment conducive to
interacting. The emphasis is on the interplay of important topics
in approximation theory and probability.
Topics will include:
- approxmation of functions by polynomials, splines operators and
their applications to stochastics.
- numerical methods for approximation of deterministic and
stochastic integrals.
- orthogonal polynomials and stochastic processes.
- positive linear operators and related deterministic and
stochastic inequalities.
- multivariate approximation and interpolation.
- approximations and martingales.
- deterministic and stochastic inequalities.
- stability of deterministic and stochastic models.
- signal analysis.
- prediction theory.
- wavelets and approximations
During the 3 days, approximately fifty papers will be presented,
in a series of two parallel sessions. The program committee will
select the papers to be presented on the basis of extended
abstracts to be submitted as described in this brochure.
The proceedings of the conference will contain full texts of all
presented papers and will be published in 1994. Several publishers
have shown interest in publishing the proceedings.
ORGANIZING COMMITTEE
Chairmen:
George Anastassiou
Department of Mathematical Sciences
Memphis State University
Memphis, TN USA
Svetlozar T. Rachev
Department of Statistics and Applied Probability
University of California
Santa Barbara, CA 93106 USA
Members:
Paul L. Butzer
Lehrstuhl A fur Mathematik RWTH Aachen
Templergraben 55, W-5100 Aachen
Stamatis Cambanis
Department of Statistics
University of North Carolina
Chapel, Hill, NC 27599-3260
Zuhair Nashed
Department of Mathematics
University of Delaware
Newark, Delaware 19716
The Meeting
The International Conference on "Approximation Probability and
Related Fields: will take place at the University of California,
Santa Barbara, Department of Statistics and Applied probability,
Santa Barbara, CA 93106-3110, on Thurs, Fri and Sat, May, 20, 21
and 22 of 1993.
Main Speakers
The following have already accepted an invitation to be main
speakers
R. Adler, Technion University
P.L. Butzer, RWTH, Aachen
S. Cambanis, University of North Carolina
P. Erdos, Hungarian Academy of Sciences
R. Karandikar, Indian Statistical Institute
J.H.B. Kemperman, Rutgers University
G.G. Lorentz, University of Texas
M. Maejima, Keio Unversity
C. Micchelli, IBM, Yorktown Heights
L. Ruschendorf, University of Munster
G. Samorodnitsky, Cornell University
G. Strang, M.I.T.
M. Taqqu, Boston University
W. Vervaat, Catholic University at Nijmegen
Instructions fo Contributors
Persons wishing to submit a paper should sent 3 copies of an
extended abstract before December 31, 1992 to the following
address: Dr. S. T. Rachev
Statistics and Applied Probability
University of California
Santa Barbara, CA 93106-3110
The extended abstract should be between 5 and 10 pages in length
(typed, double spaced) i.e. approximately 2,000 words. It must
proved sufficient details concerning the results and their
significance to enable the program committee to make its selection.
The speakers of all accepted talks will be notified by March 15,
1993.
Final full versions of all papers for the proceedings must be
provided by September 15, 1993. This will enable the proceedings
to be printed and made available in 1994.
Authors of all accepted papers for the proceedings will be provided
with notification of acceptance.
Conference Fee
General fee $120
Early registration: $100 by March 31, 1993
Student: $50
Student early registration: $40
Please sent check to:
Organizing Committee for APR
Statistics and Applied Probability
University of California
Santa Barbara, CA 93106-3110
Accommotation
Rooms have been set aside for conference participants at:
South Coast Inn, (800) 350-3614
5620 Calle Real
Goleta, California 93117 and
Holiday Inn, (800) 465-4329
5650 Calle Real
Goleta, California 93117
Make reservations on your own and ask for the special rates for
this conference (approximately $68 per night plus tax.) Rooms will
be released after April 24, 1993.
Dates
December 31, 1992 Deadline for submission of extended abstracts
of papers.
March 15, 1993 Notification of acceptance.
March 31, 1993 Deadline for early registration.
April 24, 1993 Hotel Reservation Deadline
May 20-22, 1993 International Conference "Approximation,
Probability and Related Fields"
Sept. 15, 1993 Deadline for submission of full text
of the papers for the proceedings.
Dec. 31, 1993 Notification of acceptance of papers.
Statistic and Applied Probability
University of California
Santa Barbara, California 93106-3110
Registration Form
Approximation, Probability and Related Fields (May 20-22, 1993)
Last Name First Middle Initial
Affiliation Position
Address City State Postal code
Business Phone Home Email
Tenative Title of Talk
Amount Enclosed
All charges must be paid in U.S. currency, drawn on a U.S. bank in
the form of a check or money order made payable to Organizing
Committee for APR. Mail to Organizing Committee for APR;
Statistics and Applied Probability; University of California, Santa
Barbara, CA 93106-3110; USA
|
| 1409.15 | Wavelet digest, Vol. 1, Nr 7 | STAR::ABBASI | I love DECspell | Mon Oct 26 1992 14:55 | 350 |
| Subj: Wavelet Digest, Vol. 1, Nr. 7.
Wavelet Digest Friday, October 23, 1992 Volume 1 : Issue 7
Today's Editor: Wim Sweldens
Today's Topics:
1. Lost messages
2. Book announcement
3. Preprint available on Galerkin wavelet methods
4. Question
5. Question
6. Papers available: The Multiresolution Fourier Transform
7. Reference lists available via ftp
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
Archive site:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet/archive.
Gopher server: bigcheese.math.scarolina.edu.
Current number of subscribers: 2179
--------------------------- Topic #1 -----------------------------------
From: Wavelet digest
Date: Mon, 12 Oct 92 16:50:52 EDT
Subject: Lost messages
LS,
We were informed that some mail sent to the wavelet user was lost during
the last couple of weeks, in some cases without notifying the sender.
If you sent a submission to the digest and it is not included, please
resend your message. We are sorry for this inconvenience.
Also, people who submit questions to the digest are invited to
summerize and resubmit relevant answers.
Thanks for your collaboration.
Wim Sweldens, Bjorn Jawerth
--------------------------- Topic #2 -----------------------------------
From: Lora G. Weiss, Penn State Univ.
Data: October 20, 1992 EST
Subject: NEW BOOK ANNOUNCEMENT
WAVELET THEORY AND ITS APPLICATIONS
Randy K. Young
Applied Research Lab
Penn State Univ.
(814) 863-4499
Kluwer Academic Publishers
ISBN: 0-7923-9271-X
ordering info:
call: (617) 871-6300, ask for Customer Service, give ISBN
(0-7923-9271-X) & VISA...
or write: Kluwer Academic Publishers, PO Box 358, Accord Station,
Hingham, MA 02018, with ISBN and check/money order/etc.
%%%%%%%%%%%%%%%% CHAPTERS %%%%%%%%%%%%%%%%%%%%%%%%%%%%
Chapter 1: Introduction/Background
Chapter 2: The Wavelet Transform
Chapter 3: Practical Resolution, Gain, and Processing Structures
Chapter 4: Wavelet Theory Extensions and Ambiguity Functions
Chapter 5: Linear Systems Modelling with Wavelet Theory
Chapter 6: Wideband Scattering and Environmental Imaging
This book reviews, extends, and applies wavelet theory. The book
concentrates on the practical applications of wavelet theory. Many
pictures (86 of them) provide visualizations of wavelet theory and its
new extensions, as well as relationships to established concepts.
Wavelet theory is integrated with other general theories, including
linear systems theory and template matching or matched filtering.
These relationships create analogies with related research and
connections to practical applications. In addition, by demonstrating
the effectiveness of wavelet theory in these general applications, many
other specific applications may be improved. Temporal and spatial
signals and systems are considered. The properties of the wavelet
transform representation are sensitive to the chosen mother wavelet
(the kernel of the wavelet transform, analogous to the exponential
function in a Fourier transform). These properties are examined and
techniques for analyzing these sensitivities are presented.
Wavelet theory is extended with the new mother mapper operator that
efficiently maps a wavelet transform with respect to one mother wavelet
to a new wavelet transform with respect to a different mother wavelet.
The mother mapper efficiently calculates concise wavelet
representations that utilize multiple mother wavelets. The mother
mapper operator is also employed to efficiently compute "cross" wavelet
transforms or wideband cross ambiguity functions; these "cross"
operators extract the "commonalities" between two signals or systems to
determine the existence or structure of these commonalities.
An original system model, the space-time-varying (STV) wavelet
operator, is constructed with wavelet theory. As a special case, the
STV model can represent linear time-invariant (LTI) systems. LTI
systems are represented by the one-dimensional (1D)impulse response.
This one-dimensional impulse response is the center slice of the
two-dimensional (2D) STV representation. Both the LTI and STV models
can be made to also vary with time (leading to 2D and 3D models,
respectively).
Physically, the STV wavelet operator creates an output by summing
weighted, scaled and translated replicas of the input; these weights
are the new system model. This is analogous to the LTI system model in
which the output is a weighted sum of translated replicas of the input
signal; with the weights being the LTI system model, the impulse
response. Obviously, time scaling is the additional feature of the STV
wavelet representation and is also the key to efficient representations
of the wideband reflection or scattering process and improved
estimation gains. By including the scaling operation as part of the
STV system model (that is independent of time), the estimation process
for this new system model can account for the linear time variation of
the system and, thus, have a valid model over a longer interval of
time. By estimating over a longer interval of time, more robust and
higher gain and resolution estimates can be formed. Finally, the
advantages of the new STV model are exploited to characterize or image
an environment.
Note from the editor: this book is available and the price is 67.50 US$.
--------------------------- Topic #3 -----------------------------------
From: Shann Wei-Chang, National Central University, Taiwan.
Date: Sat, 17 Oct 92 16:20:37 -0800
Subject: Preprint available
The following paper was done in May, 1991, with Prof. Jinchao Xu, when
I was a graduate student in Penn State. Our point of view was from
the finite element method. The key point is that the derivatives of
the one dimensional piecewise linear hierarchical basis is exactly the
Haar functions. Or, the lowest order of a family of Daubechies' wavelets
with compact support. We thus extend this idea to wavelets with higher
approximation power.
The article was accepted by Numerische Mathematik.
Jinchao Xu and Wei-Chang Shann
Galerkin-wavelets methods for two-point boundary value problems
Abstract. Anti-derivatives of wavelets are used for the numerical solution
of differential equations. Optimal error estimates are obtained in the
applications to two-point boundary value problems of second order.
The orthogonal property of the wavelets is used to construct efficient
iterative methods for the solution of the resultant linear algebraic systems.
Numerical examples are given.
Key words. Wavelets, finite element method, two-point boundary value problem,
conjugate gradient method, preconditioner, piecewise linear hierarchical basis.
A PostScript file with the text and graphics of this article can be obtained
from the anonymous ftp account in dongpo.math.ncu.edu.tw (140.115.25.3).
The file is pub/shann/wavelets/xu.ps. This is an ASCII file of length
about 422K. You can take the compressed version xu.ps.Z if you know how
to do it.
Printed copies can be requested from the address below.
Wei-Chang Shann, Associate Professor
Dept of Mathematics, National Central University, Chung-Li, Taiwan, R.O.C.
(03)425-6704 ext. 166 (work); (03)425-7379 (work.FAX)
[email protected]
--------------------------- Topic #4 -----------------------------------
From: Keith Deacon
Date: Mon, 12 Oct 92 16:15:46 MDT
Subject: Question
Has anybody done any work with moving, i.e. Lagrangian, wavelets in the
solution of physical problems like advection-diffusion?
It is well known that orthogonal wavelets and shape functions on compact
support retain their orthogonality for certain finite distance translations.
For translations between allowable grid points orthogonality is lost and
coefficients vary dramatically. When wavelets are used to represent certain
physical phenomena their coefficients can be related to grid nodes through
Galerkin or collocation techniques to solve Eulerian formulated equations.
The physical problems of convection and diffusion involve moving concentrations
or mass. While the equations can be formulated on a fixed grid, the Eulerian
grid, the equations can also be formulated from the viewpoint of the moving
mass, Lagrangian equations. Is there a way to take advantage of the multi-
resolution properties of wavelets in the Lagrangian frame of reference?
This would probably require the formulation of moving multi-resolution
wavelets and shape functions.
Any references or correspondence would be welcome. Thanks,
Replies can be emailed to
[email protected] or
[email protected]
Phone correspondence to myself or Mr. Ron Meyers at
(505) 678-4037
Thank you again,
Keith Deacon
--------------------------- Topic #5 -----------------------------------
From: Bhavik Bakshi. Massachusetts Institute of Technology.
Date: Mon, 12 Oct 92 16:32:47 EDT
Subject: Question
Hi,
My question is regarding multi-dimensional wavelets. The usual way of
generating wavelets in higher dimensions is by taking a tensor product. For an
n-dimensional system, this gives (2^n - 1) wavelets, and one scaling function.
Thus, the number of wavelets increases exponentially, as the dimension increases.
Is there any way of generating multi-dimensional wavelets where the number of
wavelets does not increase as an exponential power of the number of dimensions?
Thank-you.
Bhavik Bakshi
Department of Chemical Engineering
Massachusetts Institute of Technology.
--------------------------- Topic #6 -----------------------------------
From: Andy Davies, University of Warwick,
Date: Mon, 12 Oct 1992 15:01:16 +0100
Subject: Papers available: The Multiresolution Fourier Transform
The Multiresolution Fourier Transform
-------------------------------------
A generalized form of Wavelet Transform which enables adaptive
analysis of the `time-frequency' plane over multiple scales has been
developed by the Signal Processing Group at the University of Warwick,
England. The MFT has been applied successfully to segmentation
problems in both image and audio signal analysis. Details of these and
properties of the transform can be found in the following:
A Generalized Wavelet Transform for Fourier Analysis : the
Multiresolution Fourier Transform and its Application to Image and
Audio Signal Analysis
R. Wilson, A.D. Calway, and E.R.S. Pearson.
IEEE Trans. Information Theory, Special Issue on Wavelet Transforms
and Multiresolution Signal Analysis, vol. 38, 2, 1992.
Abstract
A wavelet transform specifically designed for Fourier analysis at
multiple scales is described and shown capable of providing a { em
local representation which is particularly well suited to
segmentation problems. It is shown that by an appropriate choice of
analysis window and sampling intervals, it is possible to obtain a
Fourier representation which can be computed efficiently and overcomes
the limitations of using a fixed scale of window, yet by virtue of its
symmetry properties allows simple estimation of such fundamental
signal parameters as instantaneous frequency and onset time/position.
The transform is applied to the segmentation of both image and audio
signals, demonstrating its power to deal with signal events which are
localised in either time/space or frequency. Feature extraction and
segmentation are tackled through the introduction of a class of
multiresolution Markov models, whose parameters represent the signal
events underlying the segmentation. In the case of images, this
provides a unified and computationally efficient approach to boundary
curve segmentation; in audio analysis, it provides an effective way of
note segmentation, giving accurate estimates of onset time and pitch
in polyphonic musical signals.
Additional information can also be found in:
R. Wilson and A.D. Calway, ``A general multiresolution signal
descriptor and its application to image analysis'', Proc.
EUSIPCO-88, pp. 663-666, Grenoble, 1988.
A. Calway and R. Wilson, ``A unified approach to feature extraction
based on an invertible linear transform'', Proc. 3rd Int. Conf.
Image process. & its Appl. , pp. 651-655, 1989.
A.D. Calway, The Multiresolution Fourier Transform: A General
Purpose Tool for Image Analysis , Ph.D. Thesis, Warwick Univ., 1989.
A.D. Calway and R. Wilson, ``Curve extraction in images using the
multiresolution Fourier transform'', Proc. IEEE ICASSP-90 , pp.
2129-2132, Albuquerque, 1990.
E.R.S. Pearson and R. Wilson, ``Musical event detection from audio
signals within a multiresolution framework'', Proc. ICMC-90,
Glasgow,1990.
E.R.S. Pearson, The Multiresolution Fourier Transform and its
Application to the Analysis of Polyphonic Music , Ph.D. Thesis,
Warwick Univ., 1991.
A.R. Davies and R. Wilson, ``Curve and corner extraction using the
multiresolution Fourier transform'', Proc. IEE Conf. on Image
Process. and its Appl. , Maastricht, Holland, 1992.
R. Wilson, A.D. Calway, E.R.S. Pearson and A.R. Davies. ``An
introduction to the multiresolution Fourier transform and its
applications'', Res. Report RR204, Dept. of Computer Science,
University of Warwick, 1992.
Tao-I Hsu, A.D. Calway and R.Wilson, ``Analysis of structured texture
using the multiresolution Fourier transform'', Res. Report RR226,
Dept. of Computer Science, University of Warwick, 1992.
A.D. Calway, H.Knutsson, and R. Wilson. ``Multiresolution estimation
of 2-d disparity using a frequency domain approach'', Proc. British
Machine Vision Conf., Leeds, September 1992.
For copies of the above and further information contact:
Dr Roland Wilson
Department of Computer Science
University of Warwick
Coventry CV4 7AL
England
email: [email protected]
--------------------------- Topic #7 -----------------------------------
From: Wavelet digest
Date: Mon, 12 Oct 92 18:50:32 EDT
Subject: Reference lists available via ftp
The reference lists sent to the digest are now also available via anonymous
ftp. There are three reference lists from Milos Doroslovacki (~100 entries),
Christopher Koenigsburg (~80 entries) and Tom Koornwinder (~280 entries).
We would like to thank these people for their contributions.
To retrieve them, connect to maxwell.math.scarolina.edu and look for
/pub/wavelet/references/*.ref.
Obviously there is quite some overlap between those lists. If there is
anybody who would be interested in merging these lists into one with a
consistent format (such as bibtex), please get in touch with the wavelet
digest.
|
| 1409.16 | Wavelet Digest, Vol. 1, Nr. 8 | STAR::ABBASI | what happened to family values ? | Sat Nov 07 1992 17:26 | 362 |
| Wavelet Digest Saterday, November 7, 1992 Volume 1 : Issue 8
Today's Editor: Wim Sweldens
Today's Topics:
1. Paper available
2. Visit
3. Book announcement
4. Call for papers
5. Question
6. Question
7. Contents Numerical Algorithms
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
Archive site:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet/archive.
Gopher server: bigcheese.math.scarolina.edu.
Current number of subscribers: 2248
--------------------------- Topic #1 -----------------------------------
From: Fritz Keinert, Department of Mathematics, Iowa State University
Date: Tue, 03 Nov 92 09:49:43 CST
Subject: paper available
Author: Fritz Keinert
Dept. of Mathematics
Iowa State University
Ames, IA 50011
(515) 294-5223
[email protected]
Title: Biorthogonal Wavelets for Fast Matrix Computations
Status: Submitted to "Applied and Computational Harmonic Analysis"
October 1992
Abstract: In (G. Beylkin, R. Coifman, V. Rokhlin, Fast wavelet
transforms and numerical algorithms: I, Comm. Pure Appl.
Math. 44(1991), 141--183), Beylkin et al. introduced a
wavelet-based algorithm that converts matrices of a certain
type into highly sparse matrices, as the basis for efficient
approximate calculations. Compression is achieved by doing
a wavelet decomposition of the matrix and setting small
entries to zero. The wavelets best suited for achieving the
highest possible compression with this algorithm are
Daubechies wavelets, while coiflets lead to a faster
decomposition algorithm at slightly lesser compression. We
demonstrate how the same algorithm can be based on
biorthogonal instead of orthogonal wavelets, and derive two
classes of biorthogonal wavelets that achieve high
compression and high decomposition speed, respectively. In
numerical experiments, these biorthogonal wavelets achieved
both higher compression and higher speed than their wavelet
counterparts, at comparable accuracy.
Availability: anonymous ftp from pollux.math.iastate.edu (129.186.52.4),
in directory pub/keinert. Get file README first.
- biortho.dvi (text of paper, without figures), 84K
- biortho_figures.ps (figures only, in PostScript), 925K
--------------------------- Topic #2 -----------------------------------
From: Lolina Alvarez, New Mexico State University.
Date: Mon, 2 Nov 92 16:45:34 MST
Subject: visit
Prof. Ingrid Daubechies will be the Distinguished Visiting Professor at
New Mexico State University on March 15-22, 1993.
The visit is supported by a University grant and sponsored by the Departments
of mathematics, Physics and Astronomy, the Center for Space Telemetering,
the Computer Research Laboratory and the Committee for the History,
Philosophy and Social Studies of Science.
Prof. Daubechies will deliver a series of lectures and will conduct discussions
on topics of interest to the sponsoring units.
For more information, please contact
Lolina Alvarez, (505) 646-2717, [email protected]
--------------------------- Topic #3 -----------------------------------
From: P.P. Vaidyanathan, Caltech.
Date: Fri, 30 Oct 92 10:53:33 PST
Subject: Book announcement
NEW BOOK ANNOUNCEMENT
The following text was recently published.
MULTIRATE SYSTEMS AND FILTER BANKS, by P. P. Vaidyanathan,
Prentice Hall (A. V. Oppenheim series on Signal Processing).
911 pages, over 300 homework problems, 65 dollars, hardbound.
Fourteen chapters:
1. Introduction.
2. Review of discrete-time systems.
3. Review of digital filters.
4. Fundamentals of multirate systems.
5. Maximally decimated filter banks.
6. Paraunitary perfect reconstruction systems.
7. Linear phase perfect reconstruction QMF banks.
8. Cosine modulated filter banks.
9. Quantization effects.
10. Multirate filter bank theory and related topics.
11. The wavelet transform and its relation to multirate filter banks.
12. Multidimensional multirate systems.
13. Review of discrete time multi-input multi-output LTI systems.
14. Paraunitary and lossless systems.
Five Appendices:
A. Review of matrices.
B. Review of random processes.
C. Quantization of subband signals.
D. Spectral factorization techniques.
E. Mason's gain formula.
Extensive bibliography.
--------------------------- Topic #4 -----------------------------------
From: Todd Reed, University of California.
Date: Thu, 29 Oct 92 12:22:59 -0800
Subject: call for papers
Dear Colleague:
The Society for Information Display will meet in Seattle, Washington on
May 16-21, 1993. The meeting will include many sessions of interest to those
involved in image processing, image compression, and image display. I will be
chairing a session entitled "Wavelets and Local Frequency Representations."
Because of your work in this area, I encourage your contribution to this
session.
I enclose a more complete CALL FOR PAPERS below. I hope you will join us at
what promises to be a relevant and exciting meeting.
If you expect to contribute to the session on "Wavelets and Local Frequency
Representations," or if you need further information, please send me a note at:
Todd R. Reed
Department of Electrical and Computer Engineering
University of California
Davis, CA 95616
(916) 752-4720
[email protected]
CALL FOR PAPERS
SID '93
Society For Information Display (SID)
International Symposium, Seminar And Exhibition
May 16-21, 1993
Washington State Convention Center
Seattle, Washington, USA
Session on
WAVELETS AND LOCAL FREQUENCY REPRESENTATIONS
Session Chair: Todd R. Reed, Univ. of Calif., Davis
The value of the analysis of 1-D phenomena has long been appreciated, in fields
ranging from quantum mechanics to communications. The extension of local
analysis to higher dimensions (2-D and above) has proven valuable in a number
of imaging and computer vision applications, including image segmentation,
enhancement, and coding. There are a variety of representations under
investigation that are suitable as foundations for local analysis. The most
widely known are the class of representations referred to as wavelets. In this
session, we will examine some of the more promising of these representations,
and the applications to which they can be applied.
The following are some other sessions that may be of interest.
SELECTED TOPICS IN IMAGE PROCESSING
SUPER HIGH-DEFINITION IMAGING
Chair: Vasudev Bhaskaran, Hewlett-Packard
IMAGE DATA COMPRESSION
Chair: Ralph Algazi, University of California, Davis
PERCEPTUAL ASPECTS OF IMAGE COMPRESSION
Chair: Andrew B. Watson, NASA Ames Research Center
FIDELITY AND CODING OF IMAGE SEQUENCES
Chair: Stan Klein, University of California, Berkeley
SELECTED TOPICS IN APPLIED VISION
EXTENDING THE DYNAMIC RANGE OF THE DISPLAY
Chair: Eli Peli, Eye Research Institute
IMAGE QUALITY METRICS
Chair: Jeffrey Lubin, David Sarnoff Research Center
Co-Chair: Al Ahumada, NASA Ames Research Center
DIGITAL TYPOGRAPHY
Chair: Avi Naiman, University of Rochester
AVIONIC DISPLAYS
Chair: Brian Tsou, U.S. Air Force
WHERE TO SUBMIT ABSTRACTS:
Send one copy of your abstract and technical summary to:
Beth Akers (SID'93)
Asst. Conference Coordinator
Palisades Institute for Research Services
201 Varick St., Suite 1140,
New York, NY 10014
212/620-3375 (Fax:620-3379)
DEADLINES and KEY DATES:
* Abstract/Summary............Nov. 30, 1992
* Acceptance/Notification.....Jan. 22, 1993
* Paper Submission............Mar. 15, 1993
FORMAT OF SUBMISSIONS:
Please submit your double-spaced 35-50 word abstract and
technical summary on one side of 8.5 x 11 in. pages. The
following format needs to be adhered to:
Page 1: paper title and abstract
Page 2-5: technical summary (including objective,
background, results, impact, references). Four-Page maximum.
--------------------------- Topic #5 -----------------------------------
From: Samir Chettri, Nasa.
Date: Mon, 26 Oct 92 11:51:52 -0500
Subject: question
Hello Fellow-Surfers:
I have a question related to the article by Gilbert Strang
"Wavelets and Dilation Equations," SIAM, V. 31, #4, pp. 514-627,
December 1989.
On page 617, Strang says:
"For approximation with accuracy $h^p$ , the Fourier transform $ \phi^{\hat} $
must have zeros of order p at all points $ \xi = 2\pi n $ ....."
This statement occurs before "CONDITION A" in his paper. Can anyone
tell me where this statement comes from, i.e., from functional
analysis, approximation theory etc.
It is my understanding that a comprehension the origin of the above
statement is important in understanding CONDITION A.
I have many questions related to the paper but I would like to have
this answered first.
Thanks
Samir ([email protected]).
--------------------------- Topic #6 -----------------------------------
From: John Sasso.
Date: Fri, 23 Oct 92 18:56:55 -0400
Subject: question
We have been studying wavelets for some time now and have managed
to get through some of the math that is involved, successfully proving
some of the orthonormality conditions and such for the discrete wavelet
transform. However, I am having trouble with one proof of an
equation which is very important.
Given a set of Daubechies scaling function coefficioents, {h[k]},
k = 0,...,2N-1, the wavelet function psi(t) can be defined in terms of
the scaling function as
psi(t) = ok SUM( (-1)^k h[k+1] phi(2t+k) )
I have had the hardest time trying to derive this equation. I have looked
at Daubechies' proof and others, but I get lost due to the
vagueness of the proof steps. If anyone can send me e-mail on how
to do the proof, I would greatly appreciate it.
Thank you for your help.
John ([email protected])
--------------------------- Topic #7 -----------------------------------
From: baltzer%[email protected]
Date: Fri Oct 23 12:11:13 1992
Subject: Contents Numerical Algorithms, Vol. 2, no. 2:
Contents new journal NUMERICAL ALGORITHMS,
Editor-in-Chief: CLAUDE BREZINKSI,
Laboratoire d'Analyse Numrique et d'Optimisation,
UFR IEEA - M3,
Universit de Lille 1,
France,
fax: +33 - 20 43 49 95,
e-mail: [email protected]
Editorial Board: A. BJORCK, Linkoping University, Sweden;
J.C. BUTCHER, University of Auckland, New Zealand;
T.F. CHAN, UCLA, USA;
P.G. CIARLET, Universite Pierre et Marie Curie, France;
W.M. COUGHRAN, Murray Hill, USA;
J. DELLA DORA, Grenoble, France;
M. GASCA, Universidad de Zaragoza, Spain;
G.H. GOLUB, Stanford University, USA;
M.H. GUTKNECHT, ETH Zurich, Switzerland;
P.J. LAURENT, Universite Joseph Fournier, France;
G. MEURANT, Villeneuve St. Georges, France;
C.A. MICCHELLI, IBM Yorktown Heights, USA;
G. MUHLBACH, Universitat Hannover, Germany;
S. PASZKOWSKI, Polish Academy of Sciences, Poland;
K. TANABE, Institute of Statistical Mathematics Tokyo, Japan;
P. VAN DOOREN, Urbana, USA;
R.S. VARGA, Kent State University, USA;
J. VIGNES, Universite Pierre et Marie, France;
M.F. WHEELER, Houston, USA; H. WOZNIAKOWSKI, Warszawa, Poland;
M.H. WRIGHT, Murray Hill, USA;
Software Editor: M. REDIVO ZAGLIA, Universita di Padova, Italy.
Contents Volume 2, no. 2:
N. Osada: Extensions of Levin's transformations to vector sequences;
C. Brezinski, M. Redivo Zaglia and H. Sadok:
Addendum to 'Avoiding breakdown and near-breakdown in Lanczos type algorithms';
K. Tanabe and M. Sagae: Pivoting strategy for rank-one modification
of LDM-like factorization;
S. Paszkowski: Convergence acceleration of continued
fractions of Poincare's type 1;
R.S. Varga and A.J. Carpenter: Some numerical
results on best uinform rational approximation of x^L on [0,1];
J.L. Merrien: A family of Hermite interpolants by bisection algorithms;
S. Cabay and G. Labahn: A superfast algorithm for multi-dimensional Pade
systems;
M. Gasca, C.A. Micchelli and J.M. Pena: Almost strictly positive matrices.
Please submit your papers to one of the Editors of NUMERICAL ALGORITHMS.
ISSN 1017 1398, Volumes 2 and 3 to be published in 1992.
Requests for sample copies and orders are to be sent to
J.C. Baltzer AG,
Scientific Publishing,
fax: +41-61-692 42 62,
e-mail: [email protected]
|
| 1409.17 | | 3D::ROTH | Geometry is the real life! | Tue Nov 10 1992 12:27 | 11 |
| The second edition of Numerical Recipes in Fortran contains a short
section on discrete wavelet transforms (along with a number of other
added topics and bugfixes to code from the earlier edition.)
They mention the application of making a matrix sparse by treating it
as a 2D image and performing a DWT on it. An interesting speculative
idea, though I don't know how it fits in with other multi-resolution
solution schemes (such as multigrid, or adaptive subdivision of
finite elements.)
- Jim
|
| 1409.18 | Wavelet Digest, Vol1, Issue 9 | STAR::ABBASI | iam very wise | Thu Dec 03 1992 13:52 | 403 |
| Wavelet Digest Tuesday, December 1, 1992 Volume 1 : Issue 9
Today's Editor: Wim Sweldens
Today's Topics:
1. Call for papers, 93 spring AGU meeting.
2. Question
3. Looking for matlab routines
4. Call for papers:- Advances in Computational Mathematics
5. Announcement: International Conference on Wavelets:
Theory, Algorithms, and Applications
6. Call for papers "Mathematical Imaging: Wavelet Applications in
Signal and Image Processing"
7. Preprints available
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
Archive site:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet/archive.
Gopher server: bigcheese.math.scarolina.edu.
Current number of subscribers: 2299
--------------------------- Topic #1 -----------------------------------
From: [email protected]
Date: Tue, 10 Nov 1992 7:32:17 -0500 (EST)
Subject: Call for papers, 93 spring agu meeting.
CALL FOR PAPERS
1993 SPRING AGU MEETING
May 24 - 28, Baltimore, Maryland
SPECIAL UNION SESSION
"Applications of Wavelet Transforms in Geophysics"
Wavelet transforms arose in the analysis of seismic signals. They have
since been formalized into a rigorous mathematical framework during the last
decade and have found numerous applications in diverse areas such as harmonic
analysis, numerical analysis, signal and image processing, non-linear dynamics,
turbulence, stochastic processes, study of fractals and multifractals, and
analysis of rainfall, ocean waves and topography. Properties of wavelets that
make such a wide range of applications possible are time-frequency localization,
orthogonality, multirate filtering, and scale-space analysis, to name a few.
It is expected that, owing to these unique properties, wavelet transforms will
play a key role in the future in understanding and modeling complex physical
phenomena.
The purpose of this special session is to bring together scientists using
wavelet transforms for the understanding, analysis, and description of
geophysical processes. A tutorial paper providing an introduction to wavelet
transforms will be arranged at the beginning of the session. If you would like
to contribute a paper, please send an abstract by February 20 to
Efi Foufoula-Georgiou or Praveen Kumar,
St. Anthony Falls Hydraulic Laboratory,
Mississippi River and 3rd Ave. S.E.,
University of Minnesota, Minneapolis, MN 55414
(612/627-4580; [email protected]).
In addition, the original abstract and two copies must be submitted by March 4
directly to AGU meetings (2000 Florida Ave., N.W. Washington, DC 20009).
Non-AGU members please contact the organizers for information about abstract
format and sponsorship. This session is sponsored by the precipitation
committee of the AGU Hydrology section.
--------------------------- Topic #2 -----------------------------------
From: Lotfi Senhadji, LTSI Universite de Rennes I FRANCE
Date: Fri, 13 Nov 92 13:12:38 MET
Subject: Question
My work deals with the ECG and ECG transients events detection.
Those signals are typical examples of non stationary signals, their
analysis performed by classical signal processing tools (as Fourier analysis)
does not lead to interesting performance. The wavelet analysis seems to be
more adapted to this kind of signals but until now, their is no efficient
detection schemes based on wavelet transform .
My question is : What are the recent developments in this area ? (not specially
in the biomedical field, also in Radar, Sonar, Pitch detection,etc...).
All the responses can be addressed to me, here is my e-mail
[email protected]
THANK YOU IN ADVANCE.
--------------------------- Topic #3 -----------------------------------
From: Maarten Steinbuch, Philips Research Labs
Date: November, 17, 1992
Subject: Matlab
I am interested in one-D wavelet transformation for signal analysis.
Does someone has .m files for use in MATLAB that perform this?
Thanks
Maarten Steinbuch
email: [email protected]
Philips Research Labs.
P.O.Box 80.000
5600 JA Eindhoven, Netherlands
tel 31-40743597
fax 31-40744810
--------------------------- Topic #4 -----------------------------------
From: [email protected]
Date: Fri, 20 Nov 92 15:37 BST
Subject: call for papers:- Advances in Computational Mathematics
ANNOUNCEMENT OF NEW JOURNAL - AND CALL FOR PAPERS :
ADVANCES IN COMPUTATIONAL MATHEMATICS (First issue: Spring 1993)
Editors-in-Chief:
Charles A. Micchelli - Academic Editor
Mathematical Sciences Department
IBM Research Center
PO Box 218
Yorktown Heights NY 10598
USA
e-mail: cam@YKTVMZ (Bitnet)
John C. Mason - Managing Editor
Applied & Computational Mathematics Group
Royal Military College of Science
Shrivenham
Swindon SN6 8LA
England
e-mail: [email protected]
Publisher:
J.C. Baltzer AG
Scientific Publishing Company
Wettsteinplatz 10
CH-4058 Basel
Switzerland
EDITORIAL BOARD
D. Arnold (USA), C.T.H. Baker (England), D. Braess (Germany),
J.H. Bramble (USA), C. Brezinski (France), K. Burrage (Australia),
C.K. Chui (USA), M.G. Cox (England), G. Cybenko (USA), W. Dahmen (Germany),
R.A. De Vore (USA), D. Donoho (USA), C. Douglas (USA),
S.W. Ellacott (England), W.H. Enright (Canada), R. Fletcher (Scotland),
W. Freeden (Germany), T.L. Freeman (England), M. Gasca (Spain),
K.O Geddes (Canada), R.N. Goldman (USA), G.H. Golub (USA),
T.N.T. Goodman (Scotland), J.A. Gregory (England), E. Grosse (USA),
S.J. Hammarling (England), C. Hoffman (USA), A. Iserles (England),
R.-Q. Jia (Canada), S.L. Lee (Singapore), T. Lyche (Norway), S. Mallat (USA),
J.C. Mason (England), C.A. Micchelli (USA), T. Poggio (USA),
A. Quarteroni (Italy), L. Reichel (USA), J.M. Sanz-Serna (Spain),
R. Schaback (Germany), L.L. Schumaker (USA), S. Seatzu (Italy),
T.W. Sederburg (USA), I.H. Sloan (Australia), E. Tadmoor (Israel),
G. Wahba (USA), W.L. Wendland (Germany).
Software Editor: R. Reuter, IBM Deutschland GmbH, Heidelberg, Germany.
E-mail: [email protected]
SCOPE
Advances in Computational Mathematics is an interdisciplinary journal of
high quality, driven by the computational revolution and emphasising
innovation, application and practicality. This journal will be of
interest to a wide audience of mathematicians, scientists and engineers
concerned with the development of mathematical principles and practical
issues in computational mathematics.
Publication areas include computational aspects of algebraic, differential
and integral equations, statistics, optimization, approximation, spline
functions and wavelet analysis. Submissions are especially encouraged in
modern computing aspects such as parallel processing and symbolic computation
and application areas such as neural networks and geometric modelling.
All contributions should involve novel research. Expository papers are
also welcomed provided they are informative, well-written and shed new
light on existing knowledge. The journal will consider the publication
of lengthy articles of quality and importance. From time to time
special issues of the journal devoted to meetings or topics of
particular interest to the readers will be published with the guidance
of a guest editor. Ideas for such special issues can be communicated to
the Editors-in-Chief.
Software of accepted papers will be tested and be made available to
readers. Short announcements, a problem section and letters to the
Editor will also appear in the journal at regular intervals. Advances
in Computational Mathematics will be published quarterly.
CALL FOR PAPERS
Authors are cordially invited to submit their manuscripts in triplicate
to the Managing Editor .
All the manuscripts will be refereed. The decision for publication will
be communicated by the Managing Editor. After acceptance of their
paper, authors who can should send a diskette with the TEX (or LATEX or
AMS-TEX) source of their paper together with a hard copy of it and the
letter of acceptance to the Managing Editor. For papers concerning
software an ASCII diskette is needed.
--------------------------- Topic #5 -----------------------------------
From: Luigia Puccio, Dept. of Mathematics Univ. of Messina, Italy
Date: Thu, 26 Nov 92 15:20:01 039
Subject: Conference Announcement
International Conference on Wavelets:
Theory, Algorithms, and Applications
October 14-20, 1993
Jolly Hotel Diodoro
Taormina, Sicily
Italy
Organizers: Laura Montefusco
Dept. of Mathematics
Univ. of Bologna, Italy
[email protected]
Luigia Puccio
Dept. of Mathematics
Univ. of Messina, Italy
gina@imeuniv
Charles K. Chui
Dept. of Mathematics
Texas A&M Univ, Texas, USA
Further details will be forthcoming. Meanwhile direct your inquiry to
Montefusco and Puccio.
--------------------------- Topic #6 -----------------------------------
From: Andrew Laine, University of Florida.
Date: Sat, 28 Nov 92 15:59:53 -0500
Subject: Call for papers
CALL FOR PAPERS
Conference Title: "Mathematical Imaging: Wavelet Applications in
Signal and Image Processing"
Part of SPIE`s Annual International Symposium on
Optoelectronic Applied Science and Engineering
July 15-16, 1993
San Diego, California
San Diego Convention Center, Marriot Hotel Marina
Conference Chair: Andrew Laine, University of Florida
Conference Co-chairs: Charles Chui, Texas A&M University
(Program Committee) Bjorn Jawerth, University of South Carolina
Michael Unser, National Institutes of Health
Steven Tanimoto, University of Washington
Gorge Sanz, IBM Almaden Research Center
Alan Bovik, University of Texas, Austin
Arun Kumar, Southwestern Bell Technology Resources
KEYNOTE ADDRESS: Ronald R. Coifman
Department of Mathematics
Yale University
The analysis of signals and images at multiple scales
is an attractive framework for many problems in
computer vision, signal and image processing.
Wavelet theory provides a mathematically precise understanding
of the concept of multiresolution.
The conference shall focus on novel applications of wavelet methods of
analysis and processing techniques, refinements of existing methods,
and new theoretical models. When possible, papers should compare
and contrast wavelet based approaches to traditional techniques.
Topics for the conference may include but are not limited to:
- image pyramids
- frames and overcomplete representations
- multiresolution algorithms
- wavelet-based noise reduction and restoration
- multiscale edge detection
- wavelet texture analysis and segmentation
- Gabor transforms and space-frequency localization
- wavelet-based fractal analysis
- multiscale random processes
- wavelets and neural networks
- image representations from wavelet maxima or zero-crossings
- wavelet compression, coding and signal representation
- wavelet theory and multirate filterbanks
- wavelets in medical imaging
ABSTRACT DUE DATE: 14, December, 1992.
MANUSCRIPT DUE DATE: 19, April, 1993.
(Proceeding will be made available at the conference)
Applicants shall be notified of acceptance by March, 1993.
Your abstract should include the following:
1. Abstract Title.
2. Author Listing (Principal author first and affiliations).
3. Correspondence address for EACH author.
4. Submitted to: Mathematical Imaging: Wavelet Applications
in Signal and Image Processing,
Andrew Laine, Chairman.
5. Abstract: 500-1000 word abstract.
6. Brief Biography: 50-100 words (Principal author only).
Please send FOUR copies of your abstract to the address below
via FAX, email (one copy) or carrier:
San Diego '93
SPIE, P.O. Box 10, Bellingham, WA 98227-0010
Shipping Address: 1000 20th Street, Bellington, WA 98225
Phone: (206) 676-3290
email: [email protected]
FAX: (206) 647-1445
CompuServe: 71630,2177
Notice: Late submissions may be considered subject to program
time availability and approval of the program committee.
--------------------------- Topic #7 -----------------------------------
From: Wim Sweldens, Department of Mathematics, University of South Carolina
Date: Dec 1 92
Subject: Preprints available.
The following two papers are available.
1. Quadrature Formulae for the Calculation of the Wavelet Decomposition
Wim Sweldens and Robert Piessens
Abstract:
In many applications concerning wavelets, inner products of functions
f(x) with wavelets and scaling functions have to be calculated.
This paper involves the calculation of these inner products from
function evaluations of f(x).
Firstly, one point quadrature formulae are presented. Their accuracy
is compared for different classes of wavelets. Therefore the relationship
between the scaling function phi(x), its values at the integers and
the scaling parameters h_k is investigated.
Secondly, multiple point quadrature formulae are constructed. A method
to solve the nonlinear system coming from this construction is presented.
Since the construction of multiple point formulae using monomials is
ill-conditioned, a modified, well-conditioned construction using
Chebyshev polynomials is presented.
Status: Preprint Department of Computer Science, K.U.Leuven, Belgium, submitted
Can be retrieved as a postscript file using anonymous ftp to
maxwell.math.scarolina.edu, file /pub/wavelet/papers/quad.ps.
2. Asymptotic error expansions for wavelet approximations of smooth functions
Wim Sweldens and Robert Piessens
Abstract:
This paper deals with asymptotic error expansions of orthogonal wavelet
approximations of smooth functions. Two formulae are derived and compared.
As already known, the error decays as O(2^(-jN)) where j is the multi-
resolution level and N is the number of vanishing wavelet moments.
It is shown that the most significant term of the error expansion is
proportional to the N-th derivative of the function multiplied with
an oscillating function. This result is used to derive asymptotic
interpolating properties of the wavelet approximation. Also a numerical
extrapolation scheme based on the multiresolution analysis is presented.
Status: Report TW 164, Department of Computer Science, K.U.Leuven,
Belgium, submitted.
Can be retrieved as a postscript file using anonymous ftp to
maxwell.math.scarolina.edu, file /pub/wavelet/papers/error.ps.
Wim Sweldens
Department of Mathematics
University of South Carolina
Columbia SC 29208
[email protected]
|
| 1409.19 | Wavelet Digest, Vol 1, Issue 10 | STAR::ABBASI | iam your friendly psychic hotline | Mon Dec 21 1992 20:31 | 394 |
|
Wavelet Digest Monday, December 21, 1992 Volume 1 : Issue 10
Today's Editor: Wim Sweldens
Today's Topics:
1. Note
2. IMP submission (Wavelet software for NeXT)
3. Looking for a reference
4. Call for papers 1993 IEEE VLSI Signal Processing Workshop
5. Looking for compression software
6. IMACS Annals of Computing and Applied Mathematics
7. Extended deadlines for the SPIE conference on wavelets
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
Archive site:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet/archive.
Gopher server: bigcheese.math.scarolina.edu.
Current number of subscribers: 2350
--------------------------- Topic #1 -----------------------------------
From: Bjorn Jawerth and Wim Sweldens
Dear Wavelet Digest subscriber,
This will be the last issue of the year 1992. We had over 2300
subscriptions to the digest and were able to send out 10 issues.
We would like to thank everybody who subscribed or submitted and
hope to continue our collaboration next year.
--------------------------- Topic #2 -----------------------------------
From: [email protected] (Mladen Victor Wickerhauser)
Date: Fri, 18 Dec 92 23:03:35 -0600
Subject: IMP submission (Wavelet software for NeXT)
INSTANT MATH PREPRINTS
Use tn3270; Log in as math1 (passwd math1) at yalevm.ycc.yale.edu
TI: Wavelet Packet Laboratory, version 3.03 for NeXT computers
AU: David Rochberg and M. Victor Wickerhauser
IN: Washington University in St. Louis
SO: NeXT Academic Software CD-ROM
ST: Published
AB: WPLab reads ASCII collections of floating-point numbers (files with the
extension .asc) and 8-bit mu-law or 16-bit linear sounds (with a .snd
extension). Several example signals of both types are provided. Stereo
files are mixed down to mono on reading. Segments of the signal file are
plotted in the window titled "Global signal view" A tape-recorder "Play"
glyph with a wholly selected signal permits listening to the selected
segment. The transform chooser controls the selection of the transform used
to map the signal to the Time Frequency Plane: wavelet, wavelet packet, and
local sine/cosine transforms are supported. There are 17 quadrature mirror
filters (QMFs) available for wavelet packet analysis. The reconstruction
controls window offers methods of selecting coefficients according to
threshold cutoff, coefficient level (for observing the Gibbs phenomenon), or
relative ordering (top N). To reconstruct a signal from the selected
coefficients (in effect, zeroing all others), click the "Perform Inverse"
button. The reconstruction will be displayed in the "Reconstructed Signal"
window. Selecting "Save" in the menu will save the reconstruction of the
analyzed signal in one of several formats for later analysis.
DE: wavelets,signal processing,time frequency,local sine,qmf
SC:
MA: wuarchive.wustl.edu
FN: doc/techreports/wustl.edu/math/software/WPLab3.03.tar.Z
TL: compress'ed tar archive with NeXT executable and several data files
DA:
--------------------------- Topic #3 -----------------------------------
From: [email protected] (Shann Wei-Chang)
Date: Wed, 16 Dec 92 14:39:27 -0800
Subject: Looking for a reference
Kind souls,
There is a reference in Daubechies' Ten Lectures:
A. Cohen, I. Daubechies, and P. Vial, Wavelets and fast wavelet
transform on the interval, AT&T Bell Lab., preprint.
If you have a copy of this preprint and do not mind to share with me,
please contact:
Wei-Chang Shann, Associate Professor
Dept of Mathematics, National Central University, Chung-Li, Taiwan, R.O.C.
(03)425-6704 ext. 166 (work); (03)425-7379 (work.FAX)
[email protected]
Thank you.
---
Note from the editor: A short version of this paper (with B. Jawerth as fourth
author) is accepted by Comptes Rendues de l'Academie des Sciences Paris, but
I don't know if it is already published.
--------------------------- Topic #4 -----------------------------------
From: Ed Deprettere <[email protected]>
Date: Fri, 11 Dec 92 12:22:43 +0100
Subject: Call for papers 1993 IEEE VLSI Signal Processing Workshop
1993 IEEE WORKSHOP ON VLSI SIGNAL PROCESSING
============================================
An activity of the IEEE SP Society's Technical Committee on VLSI
organized in cooperation with IEEE Benelux, IEEE Benelux Chapter
on Signal Processing and EURASIP
October 20-22 1993 Koningshof, Veldhoven, The Netherlands
=====================================================================
The objective of the workshop is to provide a forum for discussion of new
theoretical and applied developments in signal processing in its relation
to implementation as Very Large Scale Integrated Circuits. A key note
lecture and a panel discussion will focus on "Industrial and Technical
Challenges in Signal Processing for Consumer Applications". The aim is to
have also a session devoted to this topic. A hard-bound record of the
Workshop presentations will be published.
Papers are sollicited that relate to the technologies involved in the
design and implementation of signal processing algorithms and systems as
VLSI circuits:
* Digital Signal Processing * Integrated Circuits and Systems
------------------------- -------------------------------
Algorithms This area refers to the different
Architectures phases, methods and tools (CAD)
Languages used in designing signal processing
Transformational design algorithms and systems that may
lead to a final implementation in
silicon:
* Signal Processing Applications % Specification
------------------------------ -------------
speech and music design descriptions
digital audio data and control flow
image and video/HDTV
multimedia % Design
communications ------
computer graphics design methodologies
inspection/extraction cell/silicon compilers
radar and sonar chip sets
low-power analog/digital circuits
% Verification
------------
formal proofing
simulation/emulation
prototyping
testability and testing
Organizing Committee
=====================
General Chair Technical Program Chair
------------- ----------------------
Ludwig Eggermont Ed Deprettere
[email protected] [email protected]
General Co-Chair Technical Program Co-Chair
---------------- --------------------------
Patrick Dewilde Jef van Meerbergen
[email protected] [email protected]
U.S. Liaison Far East Liaison
------------ ----------------
Bob Owen Takao Nishitani
[email protected] [email protected]
Technical Program Committee
===========================
Maurice Bellanger Jochen Jess Keshab K. Pahri
Jichun Bu Konstant Konstantinides Hans Peek
Peter Cappello Thijs Krol Peter Pirsch
Luc Cleasen Sun-Yuan Kung Wojtek Przytula
Hugo De Man Tomas Lang Patrice Quinton
John Eldon Ray Liu Jan Rabaey
Gerhard Fettweis Elias Manolakos G. Robert Redinbo
Manfred Glesner Peter Marwedel Bing Sheu
Manfred Gloger John McWhirter Jerry Sobelman
Costa Goutis W. Mecklenbraucker Ken Steiglitz
Iiro Hartimo Teresa Meng Michel van Swaaij
Otto Herrmann Heinrich Meyr Earl Swartzlander, Jr.
Yu Hen Hu Yrjo Neuvo Lothar Thiele
Jenq-Neng Hwang Tobias Noll Johan Van Ginderbeuren
Leah Jamieson Jossef Nossek Kung Yao
Robert Owens Takao Yamazaki
The Workshop will be held in Conference Center
Koningshof
Veldhoven, The Netherlands
Veldhoven is a 1.5 hour drive from the international airports of Amsterdam,
Brussels and Duesseldorf, and 10 minutes from Eindhoven Airport.
Prospective authors are invited to submit
- no later than April 1, 1993 - four (4)
copies of a complete paper and an abstract
for review category classification to:
Ms. M. Emmers or Ms. M. van Kessel
Philips International - CPDC
Building VO-p
P.O. Box 218
5800 MD Einhoven, The Netherlands
Schedule of Events
===================
April 1, 1993 Submission of paper and abstract
June 5, 1993 Notification of acceptance
July 15, 1993 Receipt of final photo-ready paper
--------------------------- Topic #5 -----------------------------------
From: Licinda WOUDBERG
Date: Mon, 7 Dec 1992 15:00 +1300
Subject: Looking for compression software
Hello.
I would like to know if there are any commercially available products
for wavelet compression (of colour images).
I am currently using Press's wavelet transform (from Numerical Receips in C),
is the still the most optimised code (running under Unix).
Eventually I hope to be doing some wavelet work on the NeXT in Objective C.
Is there any wavelet NeXT specific code available, or do I just rewrite the
existing code in Objective C ??
Thanks for your help,
Licinda
e-mail: [email protected]
--------------------------- Topic #6 -----------------------------------
From: J.C. Baltzer AG, Science Publishers,
Date: Fri Dec 4 10:51:07 1992
Subject: IMACS Annals of Computing and Applied Mathematics
IMACS Annals of Computing and Applied Mathematics
Volume 12: E. Kaucher, S.M. Markov and G. Mayer (Eds), Computer
Arithmetic,
Scientific Computation and Mathematical Modelling
Volumes 10 & 11: R. Hanus, P. Kool and S. Tzafestas (Eds),
Mathematical and
Intelligent Models in System Simulation
Volume 9: C. Brezinski, L. Gori and A. Ronveaux (Eds), Orthogonal
Polynomials
and their Applications
Volume 8: P. Borne and V. Matrosov (Eds), Lyapunov Functions, Methods
and
Applications
Volume 7: C. Ullrich (Ed.), Contributions to Computer Arithmetic and
Self-validating Numerical Methods, SCAN '89
Volume 6: J. Robert and W. Midvidy (Eds), Electrical and Power Systems
Modelling
and Simulation
Volume 5: J. Eisenfeld and D.L. Levine (Eds), Biomedical Modelling and
Simulation
Volume 4: P. Borne, S.G. Tzafestas, P. Breedveld and G. Dauphin-Tanguy
(Eds),
Computing and Computers for Control Systems
Volume 3: P. Breedveld, G. Dauphin-Tanguy, P. Borne and S.G. Tzafestas
(Eds),
Modelling and Simulation of Systems
Volume 2: R. Huber, X. Kulikowski, J.M. David and J.P. Krivine (Eds),
Artificial
Intelligence in Scientific Computation: Towards Second Generation
Systems
Volume 1, 2 parts: W.F. Ames and C. Brezinski (Eds), Numerical and
Applied
Mathematics
Price per volume: Institutional price Sfr. 218.00/$ 156.00 / Personal
price per
volume Sfr. 130.00 / $ 92.00. Prices include distribution costs.
Extensive
brochure available upon request.
Please send your order to: J.C. Baltzer AG, Science Publishers,
Wettsteinplatz
10, CH-4058 Basel, Switzerland, Fax: +41-61-692 42 62, E-mail:
[email protected].
--------------------------- Topic #7 -----------------------------------
From: "Andrew Francis Laine" <[email protected]>
Date: Mon, 21 Dec 92 11:44:58 -0500
Subject: EXTENDED DEADLINE for the SPIE conference on wavelet
applications in signal and image processing.
Conference Title: "Mathematical Imaging: Wavelet Applications in
Signal and Image Processing"
Part of SPIE`s Annual International Symposium on
Optoelectronic Appied Science and Engineering
July 15-16, 1993
San Diego, California
San Diego Convention Center, Marriot Hotel Marina
Conference Chair: Andrew Laine, University of Florida
Conference Co-chairs: Charles Chui, Texas A&M University
(Program Committee) Bjorn Jawerth, University of South Carolina
Michael Unser, National Institutes of Health
Steven Tanimoto, University of Washington
Gorge Sanz, IBM Almaden Research Center
Alan Bovik, University of Texas, Austin
Arun Kumar, Southwestern Bell Technology Resources
KEYNOTE ADDRESS: Ronald R. Coifman
Department of Mathematics
Yale University
The analysis of signals and images at multiple scales
is an attractive framework for many problems in
computer vision, signal and image processing.
Wavelet theory provides a mathematically precise understanding
of the concept of multiresolution.
The conference shall focus on novel applications of wavelet methods of
analysis and processing techniques, refinements of existing methods,
and new theoretical models. When possible, papers should compare
and contrast wavelet based approaches to traditional techniques.
Topics for the conference may include but are not limited to:
- image pyramids
- frames and overcomplete representations
- multiresolution algorithms
- wavelet-based noise reduction and restoration
- multiscale edge detection
- wavelet texture analysis and segmentation
- Gabor transforms and space-frequency localization
- wavelet-based fractal analysis
- multiscale random processes
- wavelets and neural networks
- image representations from wavelet maxima or zero-crossings
- wavelet compression, coding and signal representation
- wavelet theory and multirate filterbanks
- wavelets in medical imaging
--------> EXTENDED DEADLINES <---------
Abstract Due Date: January 4, 1993.
MANUSCRIPT DUE DATE: 19, April, 1993.
(Proceeding will be made available at the conference)
Applicants shall be notified of acceptance by March, 1993.
Your abstract should include the following:
1. Abstract Title.
2. Author Listing (Principal author first and affiliations).
3. Correspondence address for EACH author (email, phone/FAX, ect.).
4. Submitted to: Mathematical Imaging: Wavelet Applications
in Signal and Image Processing,
Andrew Laine, Chairman.
5. Abstract: 500-1000 word abstract.
6. Brief Biography: 50-100 words (Principal author only).
Please send FOUR copies of your abstract to the address below
via FAX, email (one copy) or carrier:
San Diego '93
SPIE, P.O. Box 10, Bellingham, WA 98227-0010
Shipping Address: 1000 20th Street, Bellington, WA 98225
Phone: (206) 676-3290
email: [email protected]
FAX: (206) 647-1445
CompuServe: 71630,2177
Notice: Late submissions may be considered subject to program
time availability and approval of the program committee.
|
| 1409.20 | Wavelet Digest, Vol 2, Nr 1 | STAR::ABBASI | i dont talk in second person | Fri Jan 22 1993 21:36 | 483 |
| From: DECWRL::"[email protected]" "MAIL-11 Daemon" 22-JAN-1993 20:31:48.70
To: star::abbasi
CC:
Subj: Wavelet Digest, Vol. 2, Nr. 1.
Wavelet Digest Friday, January 22, 1993 Volume 2 : Issue 1
Today's Editor: Wim Sweldens
Today's Topics:
1. Wavelet Softwares Availables
2. Technical Memorandum available:
A Fast Discrete Periodic Wavelet Transform
3. Question concerning Numerical Recipes
4. Question concerning Pollen's parametrization of wavelets
5. Question concerning wavelets and computer graphics
6. Papers on wavelet de_noising available
7. Question concerning wavelet books
8. Looking for a Mathematica package that does wavelets
9. Question concerning the cascade algorithm
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
Archive site: (for retrieval of back issues)
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet/archive.
Gopher server: bigcheese.math.scarolina.edu.
Current number of subscribers: 2403
--------------------------- Topic #1 -----------------------------------
From: Stephane Mallat
Date: Tue, 22 Dec 92 12:56:39 -0500
Subject: Wavelet Softwares Availables
/****************************************************************/
/* Wave1 Software */
/* Authors: Emmanuel Bacry, Wen-Liang Hwang, */
/* Stephane Mallat and Sifen Zhong */
/* */
/* (c) Copyright 1992 New York University */
/* All rights reserved */
/****************************************************************/
/****************************************************************/
/* Wave2 Software */
/* Authors: Wen-Liang Hwang, Stephane Mallat, Sifen Zhong */
/* */
/* (c) Copyright 1992 New York University */
/* All rights reserved */
/****************************************************************/
Wave1 and Wave2 are two softwares written in C
to process signals with the wavelet transform.
These software run under Unix and X11 windows.
They provide shell environements that include
image displays, plotting and postscript facilities,
as well as general signal processing subroutines.
They can be particularly useful for researchers or engineers who
want to make experiments with the wavelet tranform.
The wavelet transform algorithms implemented in these softwares are
mainly based on two published papers:
[1] "Characterization of signals from multiscale edges" by Stephane Mallat
and Sifen Zhong,
IEEE Transactions on Pattern Analysis and Machine Intelligence,
Vol. 14, No. 7, p. 710-732, July 1992,
[2] "Singularity detection and processing with wavelets", by Stephane Mallat
and Wen Liang Hwang, IEEE Transactions on Information Theory,
Vol. 38, No. 2, p. 617-643, March 1992.
The Wave1 software processes one-dimensional signals whereas Wave2
processes images. Dyadic wavelet tranform decompositions and reconstruction
are implemented. Signal singularities and edges are detected from the
local maxima of the wavelet transform. We implemented the
subroutines that reconstruct signals from the local maxima of their wavelet
transform, with the algorithm described in [1]. One-dimensional signals
as well as images can thus be processed through the local
maxima of their wavelet transform (multiscale edges). Many subroutines
to process these local maxima are available. For images, applications to noise
removal and optical flow detection are implemented. An on-line
documentation is available.
A nonexclusive license to use this software and its documentation
for scholarly and research purposes only is hereby granted by
New York University. There is no fee to use this software for these
purposes. In using the software and documentation for scholarly and
research purposes you may copy or modify them but (a) you must include
the NYU copyright notice and the original authors' names on each copy
or modification you make of the software and of the supporting documentation;
and (b) you may not use, rewrite, or adapt the software or the documentation
(or any part of either) as the basis for any commercial software or hardware
product, without in each instance obtaining the prior written consent of
NYU or an appropriate license from NYU. You may not use this software
and its documentation (or any part of either) for any other purpose
without obtaining an appropriate license from NYU. To obtain any
license or consent from NYU, please contact: Patrick Franc, Office of
Industrial Liaison, New York University, 251 Mercer Street, New York,
NY 10012.
The source codes of Wave1 and Wave2 are available via anonymous ftp on the
machine cs.nyu.edu (IP number 128.122.140.24). After login with login name
"anonymous", do "cd pub/wave". The wave1 and wave2 softwares are
respectively in the compressed tarfiles "wave1.tar.Z" and "wave2.tar.Z".
These are unix-compressed tarfiles, so don't forget to put ftp under "binary"
mode before getting them. You must use the "uncompress" and "tar" unix
commands to restore the software.
Instructions for compilation are in the README files.
--------------------------- Topic #2 -----------------------------------
From: Neil Getz, University of California at Berkeley
Date: Tue, 22 Dec 92 11:20:14 PST
Subject: Technical Memorandum available
A Fast Discrete Periodic Wavelet Transform
Neil Getz
Department of Electrical Engineering and Computer Sciences
and
Electronics Research Laboratory
University of California
Berkeley, California 94720
email: [email protected]
Abstract
The discrete wavelet transform (DWT) is extended to functions
on the discrete circle to create a fast and complete discrete
periodic wavelet transform (DPWT) for bounded periodic sequences.
This extension also solves the problem of non-invertibility that
arises in the application of the DWT to finite dimensional sequences
as well as provides the proper theoretical setting for previous
incomplete solutions to the invertibility problem. It is shown how and
proven that the same filter coefficients used with the DWT to create
orthonormal wavelets on compact support in the space of square summable
sequences over the integers may be incorporated through the DPWT to
create an orthonormal basis of discrete periodic wavelets. By exploiting
transform symmetry and periodicity, easily implementable and fast
synthesis and analysis algorithms are derived. Matlab functions for DPWT
experimentation are included.
Electronics Research Laboratory technical memo M92/138.
Requests for a copy of this memo may be sent to the author
via email to [email protected].
--------------------------- Topic #3 -----------------------------------
From: Doug Lamb, University of Virginia
Date: Tue, 22 Dec 1992 18:58:15 -0500
Subject: Question concerning Numerical Recipes
Does anyone know the planned publication date for the 2nd
edition of Numerical Recipes in C etc. If it's too far distant,
I'll just get the FORTRAN code and translate it.
Doug Lamb
University of Virginia
[email protected]
--------------------------- Topic #4 -----------------------------------
From: Davids Stanhill, Department of electrical engineering, Technion, Israel
Date: Thu, 24 Dec 92 14:11:22 +0200
Subject: question concerning Pollen's parametrization of wavelets
Dear Wavelet'ers,
Does any one know of papers written on
(or using) Pollen's parametrization of wavelets ?
If so, I would appreciate getting the reference, if the
paper has been published, or the preprint
(hard copy or PS file) if it has not.
Thank you very much.
David Stanhill
Department of electrical engineering,
Technion,
Haifa 32000
ISRAEL
e-mail: [email protected]
--------------------------- Topic #5 -----------------------------------
Date: Wed, 6 Jan 1993 11:52:31 -0500
From: [email protected]
Subject: Question concerning wavelets and computer graphics
Does anyone know if there is any research and/or applications of wavelets
to computer graphics (i.e. 3-D modeling, shading, and other computer
graphics related areas)? If so, could someone provide me with references.
-- John
--------------------------- Topic #6 -----------------------------------
From: David Donoho, Department of Statistics, Stanford University
Date: Sat, 9 Jan 93 19:37:04 -0800
Subject: Papers on wavelet de_noising available
The following papers may be obtained via anonymous FTP
from playfair.stanford.edu.
David Donoho [email protected]
Iain Johnstone [email protected]
---
David L. Donoho and Iain M. Johnstone
"Minimax Estimation via Wavelet Shrinkage"
TR 402, July 1992, Submitted for publication.
mews.dvi
We attempt to recover an unknown function from noisy, sampled data.
Using orthonormal bases of compactly supported wavelets we develop a
nonlinear method which works in the wavelet domain by simple nonlinear
shrinkage of the empirical wavelet coefficients. The shrinkage can be
tuned to be nearly minimax over any member of a wide range of Triebel-
and Besov-type smoothness constraints, and asymptotically minimax over
Besov bodies with $p \leq q$. Linear estimates cannot achieve even the
minimax rates over Triebel and Besov classes with $p < 2$, so our
method can significantly outperform every linear method (kernel,
smoothing spline, sieve, \dots) in a minimax sense. Variants of our
method based on simple threshold nonlinearities are nearly minimax.
Our method possesses the interpretation of {\em spatial adaptivity\/}:
it reconstructs using a kernel which may vary in shape and bandwidth
from point to point, depending on the data. Least favorable
distributions for certain of the Triebel and Besov scales generate
objects with sparse wavelet transforms. Many real objects have
similarly sparse transforms, which suggests that these minimax results
are relevant for practical problems. Sequels to this paper discuss
practical implementation, spatial adaptation properties and
applications to inverse problems.
---
David L. Donoho and Iain M. Johnstone
"Ideal Spatial Adaptation via Wavelet Shrinkage"
TR 400, July 1992, Submitted for publication.
isaws.dvi, isaws1.ps, isaws2.ps, ... , isaws10.ps
(Figs 1 - 10)
With {\it ideal spatial adaptation}, an oracle furnishes information
about how best to adapt a spatially variable estimator -- piecewise
constant, piecewise polynomial, variable knot spline, or variable
bandwidth kernel -- to the unknown function. Estimation with the aid
of an oracle offers dramatic advantages over traditional linear
estimation by nonadaptive kernels; however, it is {\it a priori}
unclear whether such performance can be obtained by a procedure
relying on the data alone.
We describe a new principle for spatially-adaptive estimation--- {\it
selective wavelet reconstruction}. We show that %variable-bandwidth
kernels, variable-knot spline fits and piecewise-polynomial fits, when
equipped with an oracle to select the knots, are not dramatically more
powerful than selective wavelet reconstruction with an oracle.
We develop a practical spatially adaptive method, {\it WaveSelect},
which works by shrinkage of empirical wavelet coefficients. {\it
WaveSelect} mimics the performance of an oracle for selective wavelet
reconstruction as well as it is possible to do so. A new inequality
in multivariate normal decision theory which we call the {\it oracle
inequality} shows that attained performance differs from ideal
performance by at most a factor $ \sim 2 \log(n)$, where $n$ is the
sample size. Moreover no measurable function of the data can give a
better guarantee than this.
Within the class of spatially adaptive procedures, {\it WaveSelect} is
essentially optimal. Relying only on the data, it comes within a
factor $\log^2(n)$ of the performance of piecewise polynomial and
variable-knot spline methods equipped with an oracle. In contrast, it
is unknown how or if piecewise polynomial methods could be made to
function this well when denied access to an oracle and forced to rely
on data alone.
---
David L. Donoho and Iain M. Johnstone
"Minimax risk over $l_p$-balls for $l_q$-error"
July 1992, Submitted for publication
Revision of TR 322, May 1989
mrlp.dvi
Consider estimating the mean vector $\theta$ from data $N_n( \theta ,
\sigma^2 I )$ with $l_q$ norm loss, $q \geq 1$, when $\theta$ is known
to lie in an $n$-dimensional $l_p$ ball, $p \in (0, \infty )$. For
large $n$, the ratio of minimax \sl linear \rm risk to minimax risk
can be {\em arbitrarily large} if $p < q$. Obvious exceptions aside,
the limiting ratio equals 1 only if $p=q=2$. Our arguments are mostly
indirect, involving a reduction to a univariate Bayes minimax problem.
When $p<q$, simple non-linear co-ordinatewise threshold rules are
asymptotically minimax at small signal-to-noise ratios, and within a
bounded factor of asymptotic minimaxity in general. Our results are
basic to a theory of estimation in Besov spaces using wavelet bases
(to appear elsewhere).
---
David L. Donoho
"Nonlinear Solution of Linear Inverse Problems by Wavelet-Vaguelette
Decomposition"
TR 403 July 1992, Submitted for publication
nslip.dvi
We describe the Wavelet-Vaguelette Decomposition (WVD) of a linear
inverse problem. It is a substitute for the singular value
decomposition (SVD) of an inverse problem, and it exists for a class
of special inverse problems of homogeneous type -- such as numerical
differentiation, inversion of Abel-type transforms, certain
convolution transforms, and the Radon Transform.
We propose to solve ill-posed linear inverse problems by nonlinearly
``shrinking'' the WVD coefficients of the noisy, indirect data. Our
approach offers significant advantages over traditional SVD inversion
in the case of recovering spatially inhomogeneous objects.
We suppose that observations are contaminated by white noise and that
the object is an unknown element of a Besov space. We prove that
nonlinear WVD shrinkage can be tuned to attain the minimax rate of
convergence, for $L^2$ loss, over the entire Besov scale. The
important case of Besov spaces $\Bspq$, $p <2$, which model spatial
inhomogeneity, is included. In comparison, linear procedures -- SVD
included -- cannot attain optimal rates of convergence over such
classes in the case $p<2$. For example, our methods achieve faster
rates of convergence, for objects known to lie in the Bump Algebra or
in Bounded Variation, than any linear procedure.
---
David L. Donoho
"Interpolating Wavelet Transforms"
TR 408 November 1992, Submitted for publication
Interpol.tex (LATEX)
\begin{abstract}
We describe several ``wavelet transforms'' which characterize
smoothness spaces and for which the coefficients are obtained
by sampling rather than integration. We use them to re-interpret
the empirical wavelet transform, i.e. the common practice
of applying pyramid filters to samples of a function.
\end{abstract}
{\bf Key Words and Phrases.} Interpolating
Wavelet Transform. Interpolating Spline Wavelets.
Interpolating Deslauriers-Dubuc Wavelets. Wavelet transforms of Sampled
Data. Wavelet Interpolation of Sampled Data. Wavelets on the Interval.
---
David L. Donoho
"Unconditional Bases are Optimal Bases for Data Compression and
for Statistical Estimation"
TR 410 November 1992, Submitted for publication
UBRelease.tex (LATEX)
\begin{abstract}
An orthogonal basis of $L^2$ which is also an
unconditional basis of a functional space $\cal F$ is a kind
of optimal basis for compressing, estimating, and recovering
functions in $\cal F$. Simple thresholding operations,
applied in the unconditional basis,
work essentially better for compressing,
estimating, and recovering than they do in any other orthogonal basis.
In fact, simple thresholding in an unconditional basis works essentially
better for recovery and estimation than other methods, period.
(Performance is measured in an asymptotic minimax sense.)
As an application, we formalize and prove Mallat's Heuristic,
which says that wavelet bases are optimal for representing functions
containing singularities, when there may be an arbitrary
number of singularities, arbitrarily distributed.
\end{abstract}
{\bf Key Words.}
Unconditional Basis, Optimal Recovery, weak-$\ell^p$ spaces.
Minimax Decision theory. Besov, H\"older, Sobolev, Triebel Spaces.
Thresholding of Wavelet Coefficients.
---
David L. Donoho
"De-Noising via Soft Thresholding"
TR 409 November 1992, Submitted for publication
denoiserelease3.tex (LATEX)
\begin{abstract}
Donoho and Johnstone (1992a)
proposed a method for
reconstructing an unknown function $f$ on $[0,1]$
from noisy data $d_i =f(t_i ) + \sigma z_i$,
$i=1, \dots,n $, $t_i = i/n$, $z_i \stackrel{iid}{\sim} N(0,1)$.
The reconstruction $f_n^*$ is
defined in the wavelet domain by translating all the empirical wavelet
coefficients of $d$ towards 0 by an amount $\sqrt{2 \log (n)} \cdot \sa /
\sqrt{n}$. We prove two results about that estimator. [Smooth]: With high
probability $f_n^*$ is at least as smooth as $f$, in any of a wide variety
of smoothness measures. [Adapt]: The estimator comes nearly as close in
mean square to $f$ as any measurable estimator can come, uniformly over
balls in each of
two broad scales of smoothness classes. These two properties
are unprecedented in several ways. Our
proof of these results develops new facts about abstract statistical
inference and its connection with an optimal recovery model.
The abstract model also applies to other types of
noisy observations, such as higher-dimensional samples
and area samples.
\end{abstract}
{\bf Key Words and Phrases.} Empirical Wavelet Transform. Minimax
Estimation. Adaptive Estimation. Optimal Recovery.
--------------------------- Topic #7 -----------------------------------
From: Lode Vermeersch, Rijksuniversiteit Gent, Belgium
Date: Mon, 18 Jan 93 12:15:54 GMT
Subject: question concerning wavelet books
Hello fellow waves,
Is there someone, who can give me some indication on the contents of the
following books ?
- "Ten Lectures on Wavelets" by I. Daubechies.
- "An Introduction to Wavelets" by C. Chui.
- "Wavelets : An elementary treatment of theory and applications" by
T. Koornwinder, editor.
- "Wavelet Theory and its Applications" by R. Young.
To which audience are these books addressed ? Is it also possible to
give me some positive and negative critiques on the different books ?
Thanks in advance for possible reactions,
Lode Vermeersch.
University of Gent (Belgium)
[email protected]
--------------------------- Topic #8 -----------------------------------
From: Gideon Yuval, Microsoft
Date: Mon, 18 Jan 93 17:31:36 PST
Subject: looking for a Mathematica package that does wavelets
Is there a Mathematica package that does wavelets?
Thanks
Gideon Yuval ([email protected])
--------------------------- Topic #9 -----------------------------------
From: Norman C. Corbett, University of Waterloo, Canada
Date: Wed, 20 Jan 1993 10:27:57 -0500
Subject: question concerning cascade algorithm
Dear Digest Readers,
I am trying to implement the "Cascade Algorith", as described in
Daubechies' "Ten Lectures on Wavelets". This algorithm is suppose
to allow for the computation of the inner-products
C^j_k=<\phi(x), 2^{j/2} \phi(2^j x-k) >
C^j_k=\sum_l h_{k-2l} C^{j-1}_l,
where {h_k} is the associated two-scale sequence. Since \phi is in
L^1 and is unimodular, we can use the numbers 2^{j/2} C^j_k to
approximate the value of \phi at the dyadic rationals k/2^j
(for j large enough). However, when I use my program to compute
the C^j_k, my results to not agree with those of Daubechies. Has
anyone implemented this algorithm? I would appreciate any advice
(pseudo-code would be nice). Thanks
Norm Corbett
Dept. of Applied Mathematics
University of Waterloo
[email protected]
|
| 1409.21 | Wavelet Digest, Vol. 2, Nr. 2. | STAR::ABBASI | i think iam psychic | Sat Feb 06 1993 03:07 | 391 |
| From: US2RMC::"[email protected]" "MAIL-11 Daemon" 5-FEB-1993 17:48:54.15
To: star::abbasi
CC:
Subj: Wavelet Digest, Vol. 2, Nr. 2.
Wavelet Digest Friday, February 5, 1993 Volume 2 : Issue 2
Today's Editor: Wim Sweldens
Today's Topics:
1. Overview paper on wavelets available
2. Singularity- and wavelet analyses question
3. Papers on "Wavelets and Quantum Chemistry" available.
4. Constructive Approximation, special issue on wavelets,
table of contents
5. Wavelets and neural networks question
6. Paper available:
Eliminating Distortion in the Beylkin-Coifman-Rokhlin Transform
7. NSF summer program for undergraduates: wavelets and applications.
8. Call for Papers, IEEE Workshop on Speech Coding
9. Fourth annual MIT spring wavelet seminar series
10. Looking for papers
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
Archive site:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet/archive.
Gopher server: bigcheese.math.scarolina.edu.
Current number of subscribers: 2460
--------------------------- Topic #1 -----------------------------------
From: Bjorn Jawerth and Wim Sweldens, University of South Carolina.
Subject: Overview paper on wavelets available
An Overview of Wavelet Based Multiresolution Analyses
Bjorn Jawerth and Wim Sweldens
Abstract:
In this paper we give an overview of some wavelet based multiresolution
analyses. First, we briefly discuss the continuous wavelet transform in
its simplest form. Then we give the definition of multiresolution analysis
and show how wavelets fit into it. We take a closer look at orthogonal,
biorthogonal and semiorthogonal wavelets.
The fast wavelet transform, wavelets on closed sets (boundary wavelets),
multidimensional wavelets and wavelet packets are discussed. Several
examples of wavelet families are given and compared. Finally, the essentials
of two major applications are outlined: data compression and compression
of linear operators.
Research Report 1993:1, Industrial Mathematics Initiative, Department of
Mathematics, University of South Carolina.
Submitted to Siam Review.
You can retrieve a postscriptfile of this paper by using anonymous ftp to
maxwell.math.scarolina.edu, directory /pub/wavelet/papers, file overview.ps.
If you have any comments or suggestions concerning this paper, don't
hesitate to contact us.
Bjorn Jawerth ([email protected])
Wim Sweldens ([email protected])
--------------------------- Topic #2 -----------------------------------
From: Anthony Davis, NASA/GSFC (Code 913)
Date: Sat, 23 Jan 93 14:10:26 -0500
Subject: Singularity- and Wavelet Analyses
Dear wavelet experts,
I am new-comer to your field, with a background in different kinds of
scale-based data analysis and simulation techniques. Namely, I delve in
singularity ('multifractal') analysis as well as simple 2-point statistics
(in the form of 'structure functions' of all orders).
Is anybody aware of any fundamental connections between wavelet transforms
and the above scale-conditioned statistics (defined *and* performed in
physical space)?
I am thinking about general ideas, not just applications of wavelets to
given fields generated (say) by multiplicative cascades or (say) to
turbulence data which has been multifractally analyzed as well, although
these interest me too.
Please send any thoughts and/or references to:
Anthony Davis
NASA/GSFC (Code 913), Greenbelt MD 20771, USA
e-mail: [email protected]
Thank you very much.
--------------------------- Topic #3 -----------------------------------
From: Patrick Fischer, Universite de Paris-Dauphine - CEREMADE
Date: Mon, 25 Jan 93 12:46:21 GMT
Subject: Papers on "Wavelets and Quantum Chemistry" available.
The following papers are availables:
Patrick FISCHER (1,2) and Mireille DEFRANCESCHI (2)
1 - Universite de Paris-Dauphine - CEREMADE
2 - CEA - CE-Saclay, DSM / DRECAM / SRSIM
"Looking at Atomic Orbitals through Fourier and Wavelet Transforms."
Accepted by Intern. J. Quant. Chem.
Abstract:
Solutions of Hartree-Fock equations expressed as Gaussian functions are studied
in various spaces: position, momentum, and position-momentum spaces. The use
of the wavelet transform allows one to visualize position and momentum
characteristics of atomic orbitals on the same drawing. A complementary
viewpoint is then obtained on top of usual position and momentum
representations. Applications to Gaussian "atomic" orbitals modeled as
one-dimensional functions are performed.
Patrick FISCHER (1,2) and Mireille DEFRANCESCHI (2)
1 - Universite de Paris-Dauphine - CEREMADE
2 - CEA - CE-Saclay, DSM / DRECAM / SRSIM
"Iterative Process for Hartree-Fock Equations thanks to a Wavelet Transform."
Submitted for publication.
Abstract:
A continuous wavelet transform is used to define an iterative scheme to solve
Hartree-Fock equations. For the sake of simplicity, the case of the hydrogen
atom is treated only in a one-dimensional space. Starting with a gaussian
function, the first iterate is analytically performed and is compared with
the exact solution in a three-dimansional space, the Slater function.
The various functions are drawn and commentated in position-momentum
representations.
Printed copies can be requested from:
[email protected]
--------------------------- Topic #4 -----------------------------------
From: E.B. Saff <[email protected]>
Date: Mon, 25 Jan 93 11:38:49 EST
Subject: Constructive Approximation, Special Issue on ``Wavelets"
Volume 9, Issues 2/3, 1993
Table of Contents
``Preface" by Ronald A. DeVore and Charles Micchelli, p.121;
``On the Construction of Multivariate (Pre) Wavelets" by Carl de Boor,
Ronald A. DeVore, and Amos Ron, p. 123-166;
``On Trigonometric Wavelets" by C.K. Chui and H.N. Mhaskar, p. 167-190;
`Orlicz Spaces, Spline Systems, and Brownian Motion" by Z. Ciesielski,
p. 191-208;
``Compactly Supported Bidimensional Wavelet Bases with Hexagonal Symmetry"
by A. Cohen and J.-M. Schlenker, p.209-236;
`Wavelet-Galerkin Methods: An Adapted Biorthogonal Wavelet Basis" by
Stephan Dahlke and Ilona Weinreich, p. 237-262;
``Banded Matrices with Banded Inverses II: Locally Finite Decomposition of
Spline Spaces" by Wolfgang Dahmen and Charles A Micchelli, p. 263-281;
``Irregular Sampling of Wavelet and Short-Time Fourier Transforms" by
Karlheinz Gr\"{o}chenig, p.283-297;
``A Bernstein-Type Inequality Associated with Wavelet Decomposition" by
Rong-Qing Jia, p. 299-318;
``A Note on Orthonormal Polynomial Bases and Wavelets" by D. Offin and
K. Oskolkov, p. 319-325;
``Wavelets and Self-Affine Tilings" by Robert S. Strichartz, p. 327-346;
``Construction of Compact$p$-Wavelets" by Grant V. Welland and
Matthew Lundberg, p.347-371.
--------------------------- Topic #5 -----------------------------------
From: Jean-Marc Lina, Lab. Physique Nucleaire, Univ. of Montreal, Canada.
Date: Tue, 26 Jan 93 17:42:10 EST
Subject: wavelets and neural networks question
Dear Wavelet'ers,
Some recent works seemed to demonstrate a important role of
wavelet in Neural Networks (works of Krishnaprasad and all,
Boubez, Benveniste.) I would appreciate to receive some
enlightnings about the training that such models involve.
Any pointer on references concerning this subject will be
appreciated.
Thanks in advance,
Jean-Marc Lina,
Lab. Physique Nucleaire, Univ. of Montreal, Canada.
[email protected]
--------------------------- Topic #6 -----------------------------------
From: Bruce W. Suter, Ph.D., Air Force Institute of Technology.
Date: Wed, 27 Jan 93 14:54:27 EST
Subject: Paper available
Eliminating Distortion in the Beylkin-Coifman-Rokhlin Transform
John R. O'Hair and Bruce W. Suter
Department of Electrical and Computer Engineering
Air Force Institute of Technology
Wright-Patterson AFB, OH 45433
A systematic approach is presented for the elimination of distortion in the
Beylkin-Coifman-Rokhlin (BCR) transform, a technique that requires only O(N)
operations to apply an NxN matrix to an arbitrary vector. Since these matrices
and vectors are of finite length, implementations of the BCR require the
application of some extension technique, and these extension methods result in
an additional O(N) non-zero terms. The resulting algorithm retains O(N)
complexity while eliminating all distortion in a "perfect reconstruction"
sense. The only distortion remaining is in the wavelet coefficients and that
being due to the particular extension method chosen. The appendix of this paper
contains the derivation of a fast discrete periodic wavelet transform algorithm.
A talk based on this paper will be presented at the IEEE International
Conference on Acoustics, Speech, and Signal Processing in Minneapolis,
Minnesota on April 1993.
Requests for a copy of this paper may be sent to the second author via
email to [email protected].
--------------------------- Topic #7 -----------------------------------
From: Walter Richardson, Math. Dept., UT San Antonio
Date: Thu, 28 Jan 93 17:49:32 CST
Subject: NSF summer program for undergraduates: wavelets and applications.
The National Science Foundation is sponsoring
a summer program in Research Experiences for
Undergraduates: Wavelets and their Applications
at the University of Texas at San Antonio,
June 1 - July 31, 1993. Six students will
investigate wavelets and their applications to image
processing, computer vision, stochastic processes,
numerical methods, harmonic analysis, and speech synthesis.
For further information contact
Dr. Walter Richardson or Dr. Dwayne Collins at
[email protected]. (using "finger" on this
address will give program description and application).
--------------------------- Topic #8 -----------------------------------
From: Wai-Yip Geoffrey Chan
Date: Tue, 2 Feb 93 18:38:43 EST
Subject: Call for Papers, IEEE Workshop on Speech Coding
Call for Papers
IEEE Workshop on Speech Coding for Telecommunications
October 13--15, 1993
St. Jovite, Quebec, Canada
The 1993 IEEE Workshop on Speech Coding for Telecommunications will be held
at the Gray Rocks resort in St. Jovite, Quebec, about 115 kilometers
northwest of Montreal in the Laurentian Mountains. St. Jovite is near
Mont Tremblant Park and is about 2 hours of driving time from Ottawa,
the capital of Canada. Hiking, water sports on the lake, tennis, golf, fall
foliage viewing are some of the featured outdoor activities.
The Workshop provides a forum for presentation and discussion of the
latest developments and future directions in speech/audio coding for
telecommunications. Papers will be selected for presentation in lecture or
poster format. To promote the effective exchange of ideas, no concurrent
lecture sessions are planned and attendance will be limited to 150 people.
Papers describing original work in all aspects of speech coding are
solicited. Topics of interest include, but are not limited to the
following:
- Low-rate speech coding
- Speech coding for wireless communications
- Speech coding for packet networks
- Speech coding for storage
- Speech/audio coding in multimedia environments
- Robust coding techniques
- Perceptual models for speech/audio coding
- Speech quality evaluation
- Future speech/audio coding standards
- Unconventional and emerging techniques
- Current limits and future challenges
Papers that comport with the Workshop theme "Speech Coding for the
Network of the Future" are of particular interest.
Authors are invited to submit to the Technical Program Chair 5 copies of a
500--1000 word summary, which should include a title, the authors' names,
and their affiliations. The summary should indicate clearly the one author
responsible for correspondence, and the address, telephone and fax numbers
of that author. Also, one or more keywords that identify the topic(s)
addressed by the paper should be listed in the summary in an order of
decreasing relevance. Submission of a summary implies a commitment by
an author to present the paper, if accepted, at the Workshop.
Deadline for submission of 5 copies of summary April 1, 1993
Notification of acceptance May 31, 1993
Submission of photo-ready 2-page paper June 30, 1993
Conference Chair:
Dr. Paul Mermelstein
BNR, 16 Place du Commerce, Verdun, Quebec, Canada H3E 1H6
Tel: (514) 765-7769, (514) 761-8507 (FAX)
E-mail: [email protected]
Technical Program Chair:
Dr. Peter Kabal, Department of Electrical Engineering,
McGill University, 3480 University Street, Montreal, Canada H3A 2A7
Tel: (514) 398-7130, (514) 398-4470 (FAX).
E-mail: [email protected]
Sponsored by the IEEE Signal Processing Society, the IEEE Communications
Society, and the Canadian Institute for Telecommunications Research (CITR).
[A postscript typeset version of this Call for Papers may be obtained via
anonymous ftp from aldebaran.ee.mcgill.ca in /pub/speech_workshop93.ps.]
--------------------------- Topic #9 -----------------------------------
From: Chris Heil, Department of Mathematics, MIT.
Date: Thu, 4 Feb 93 12:01:07 EST
Subject: Fourth annual MIT spring wavelet seminar series
Fourth Annual MIT Spring Wavelet Seminar Series
This weekly interdisciplinary seminar provides an opportunity for
mathematicians, physicists, and engineers to discuss their latest
ideas on wavelets and their applications. The term "wavelets" is
used in the broadest possible sense. The seminar meets Wednesdays
at 4:00 p.m. in Room 2-105 of the MIT campus in Cambridge, MA.
Some of the early speakers and their topics are:
Feb. 3 Dr. Christopher Heil, MIT
"Wavelets, Linear Algebra, and Fractals"
Feb. 10 Prof. George Cybenko, Dartmouth College
"Perfect Reconstruction Operators and their Spectral Theory"
Feb. 17 Dr. Lars Villemoes, Royal Institute of Technology, Stockholm
"Besov Smoothness of Wavelets"
Feb. 24 Prof. George Benke, Georgetown University and The MITRE Corporation
"Generalized Rudin-Shapiro Systems"
Mar. 3 Prof. John Benedetto, Mathematics Dept, University of Maryland
"Wavelets, Sampling, and an Auditory Model"
For more information, or to join the seminar email announcement list,
please contact me at the address below.
Chris Heil
[email protected]
--------------------------- Topic #10 -----------------------------------
From: Karsten Urban, RWTH Aachen, Germany
Date: Fri, 5 Feb 93 14:41:54 +0100
Subject: Looking for papers
Hello wavelet-digest-readers,
is there someone who can tell me, where I can find the following two papers and
if there is an english version of these? I would be very happy if someone
could send me these preprints:
P.G. Lemarie : "Ondelettes Vecteurs a Divergence Nulle"
P.G. Lemarie : "Analyses Multi-Resolutions Non-Orthogonales et Ondelettes
a Divergence Nulle"
Thank you very much,
Karsten Urban
Institut fuer Geometrie und Praktische Mathematik
RWTH Aachen
Templergraben 55
W-5100 Aachen
Germany
e-mail: [email protected]
-------------------- End of Wavelet Digest -----------------------------
|
| 1409.22 | Wavelet Digest, Vol.2 Nr. 3 | STAR::ABBASI | i think iam psychic | Sat Feb 20 1993 21:21 | 394 |
| From: US2RMC::"[email protected]" "MAIL-11 Daemon" 20-FEB-1993 21:20:36.91
To: star::abbasi
CC:
Subj: Wavelet Digest, Vol. 2, Nr. 3.
Wavelet Digest Saturday, February 20, 1993 Volume 2 : Issue 3
Today's Editor: Wim Sweldens
Today's Topics:
1. New Journal, Books and Workshop on wavelets.
2. International conference on wavelets, Taormina, Italy.
3. Looking for software.
4. Looking for information about a book.
5. Workshop on Wavelets in Vienna.
6. Question.
7. Post-Doctoral Positions at AFIT.
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject,
to unsubscribe, e-mail with "unsubscribe" followed by your e-mail
address.
Archive site:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet/archive.
Gopher server: bigcheese.math.scarolina.edu.
Current number of subscribers: 2501
--------------------------- Topic #1 -----------------------------------
From: Charles Chui, Department of Mathematics, Texas A&M University.
Subject: New Journal, Books and Workshop on wavelets.
(1) New Journal Announcement
A new "wavelet" journal is to be published by Academic Press is 1993.
"Applied and Computational Harmonic Analysis: Time-Frequency and Time-
Scaled Analysis, Wavelets, Numerical Algorithms, and Applications"
(ACHA)
Editors-in-Chief: Charles K. Chui
Ronald R. Coifman
Ingrid Daubechies
Associate Editors: David Donoho
Alexander Grossmann
Wolfgang Hackbusch
Stephane G. Mallat
Yves F. Meyer
Vladimir Rokhlin
Alan S. Willsky
is an interdisciplinary journal that publishes research work in
harmonic analysis with special emphasis on the subjects of
wavelets-waveform analysis, signal processing and numerical algorithms.
The workd wavelets is considered in its broadest sense, covering methods
for construction of special purpose waveforms and their relations to
signal processing, time-frequency and phase-space analysis, subband
coding, fast numerical computations, and other related areas.
This journal aims to provide a common link between
engineers, scientists, and mathematicians. Authors are encouraged to
provide software whenever available .
Manuscripts should be wirtten clear, concise, and grammatically
correct English and should
be submitted in quadruplicate (one original and three
photocopies), including four sets of original figures or
good-quality glossy prints, to:
Applied and Computational Harmonic Analysis
Journal Division
Academic Press
1250 Sixth Avenue
San Diego, CA 92101 USA
(2) Book Series on Wavelets
Academic Press has founded a book series "Wavelet Analysis and
Its Applications" in 1991 with C.K. Chui as Series editor. The first two
volumes "An Introduction to Wavelets" and "Wavelets: A Tutorial of
Theory and Applications" were published in 1992. The objective of this
series is to publish high quality monographs and edited volumes on
all subjects related to wavelets: time-frequence and time-scale analysis,
numerical algorithms, multigrid methods, and various areas in applied
and computational harmonic analysis. Potential authors and editors
are encouraged to contact the series editor at [email protected].
(3) Wavelet Workshop
In conjunction with its 1993 Annual Meetings in Philadelphia, SIAM
will host a one-day workshop entitled "Introduction to Wavelets and
Applications" on July 11 (Sunday) at the conference site. Further
information will be available from the SIAM News and later issues of this
digest.
--------------------------- Topic #2 -----------------------------------
From: L. Puccio - Department of Mathematics, University of Messina
Subject: International conference on wavelets, Taormina, Italy.
INTERNATIONAL CONFERENCE ON WAVELETS
Theory, Algorithms, and Applications
Taormina (Italy) - October 14-20, 1993
In collaboration with "Accademia Peloritana dei Pericolanti",
"Consiglio Nazionale delle Ricerche - C.N.R.", "Gruppo Nazionale
per l' Informatica Matematica del C.N.R. - G.N.I.M.", the University
of Messina and its Department of Mathematics will organize an
International Conference on Wavelets at Taormina (Sicily, Italy)
from October 14 to 20, 1993.
ORGANIZERS
L. Puccio - Department of Mathematics, University of Messina;
L. Montefusco - Department of Mathematics, University of Bologna;
A. Mammano - Department of Mathematics, University of Messina;
C.K. Chui - Center of Approximation Theory, Texas A&M University, USA.
MAIN TOPICS
Wavelet theory, applications to approximation theory, linear algebra,
differential and integral equations; multigrid methods; continuous
and discrete wavelet transform; multiresolution analysis; signal
and image processing; medical imaging; frames; wavelet-based fractal
analysis; scalar and parallel algorithms for wavelet problems; general
applications of wavelets to different scientific fields; ...
SCIENTIFIC COMMITTEE
C.K. Chui - Texas A&M University, USA;
R. Coifman - Yale University, USA;
I. Daubechies - Rutgers University, USA;
I. Galligani - University of Bologna, Italy;
A. Mammano - University of Messina, Italy;
Y. Meyer - University of Paris-Dauphine, France;
L. Montefusco - University of Bologna, Italy;
L. Puccio - University of Messina, Italy.
INVITED SPEAKERS (preliminary list)
C.K. Chui - Texas A&M University, USA;
W. Dahmen - Institute fur Geometrie und Praktische Math.,Aachen,Germany;
M. Farge - LMD/CNRS Paris, France;
A. Grossmann - CPT/CNRS Luminy Marseille, France;
S. Mallat - Courant Institute New York, USA;
C. Micchelli - IBM T.J. Watson Research Center, USA;
L. Montefusco - University of Bologna, Italy;
D. Trigiante - University of Firenze, Italy;
M.V. Wickerhauser - Washington University, USA.
SCIENTIFIC ACTIVITIES
They will be held at Jolly Hotel in Taormina.
We expect to have 12 invited hour lectures, as well as several sessions
of contributed papers in formats of twenty-five minutes talks, posters,
and software exhibition.
CALL FOR PAPERS
Titles and abstracts of contributed papers (including posters and
software exhibition) must be received by JUNE 15, 1993. The abstracts
should be typed double spaced, not to exceed one page, and they will be
directly reproduced in a booklet to be handed out to all the partici-
pants at the conference.
PROCEEDINGS
We will publish all invited papers as well as some selected contributed
papers. All papers will be refereed and carefully edited.
REGISTRATION
Registration fee is Lit. 320.000 UNTIL JUNE 15, 1993. Late
registration is Lit. 370.000. Information about hotel accomodation
and registration will be included in the next announcement. Please,
fill out the enclosed information form to request for registration
material BEFORE MARCH 30, 1993. The registration fee will cover six
lunches, all coffee breaks, the abstract book and the edited volume of
conference papers.
CONFERENCE COORDINATORS
L. Montefusco,
Dipartimento di Matematica - Universita' di Bologna,
Piazza di Porta S. Donato, 5
40127 Bologna, Italy.
Tel. : 39-51-354439
Fax : 39-51-354490
E-mail : MONTELAU AT DM.UNIBO.IT
L.Puccio,
Dipartimento di Matematica - Universita' di Messina,
Via Salita Sperone, 31 - Contrada Papardo
98166 Messina, Italy
Tel. : 39-90-6763066
Fax : 39-90-393502
E-mail : GINA AT IMEUNIV.BITNET
SECRETARIAT
G. Cusumano, M. Chui, V. Calderone, M. Cotronei, D. Lazzaro, M.Piraccini
Tel. : 39-90-6763068 Fax : 39-90-393502
ADDRESS
INTERNATIONAL CONFERENCE ON WAVELETS
Dipartimento di Matematica - Universita' di Messina
Via Salita Sperone, 31 - Contrada Papardo
98166 Messina, Italy.
E-mail : GINA AT IMEUNIV.BITNET
REGISTRATION FORM
FAMILY (OR LAST) NAME _______________________________________
FIRST NAME __________________________________________________
INSTITUTION __________________________________________________
ADDRESS ______________________________________________________
PHONE ________________________________________________________
FAX __________________________________________________________
E-MAIL _______________________________________________________
- Wishes to receive further information: YES NO
- Intends to participate: YES NO
- Intends to submit a research paper: YES NO
If yes, specify (talk, poster or software demonstration) ___________
Please return BEFORE MARCH 30, 1993 to:
INTERNATIONAL CONFERENCE ON WAVELETS
DIPARTIMENTO DI MATEMATICA - UNIVERSITA' DI MESSINA
VIA SALITA SPERONE, 31 - CONTRADA PAPARDO
98166 MESSINA (ITALY)
Tel.: 39-90-6763068 Fax: 39-90-393502
E-mail : GINA AT IMEUNIV.BITNET
--------------------------- Topic #3 -----------------------------------
From: Moshe Olshansky
Subject: Looking for software.
Hello!
Does anybody know of a public domain (or even commercial) software
doing FFT, convolutions, etc. tuned to have a good performance on
i486 (80486) microprocessor?
Any help will be appreciated.
Moshe Olshansky
< olshansk@vnet > or < [email protected] >
--------------------------- Topic #4 -----------------------------------
From: Arnaldo Manuel Batista
Subject: Looking for information about a book
Dear Readers of the Wavelet Digest;
Could anyone give me any information about the following book:
MULTIRESOLUTION SIGNAL DECOMPOSITION: TRANSFORMS, SUBBANDS, AND
WAVELETS By Ali N. Akansu and Richard A. Haddad, Academic Press Inc
Nov 1992 isbn 0-12-047140-x
Readership? Contents? Point of views?
Many thanks in advance
From: Arnaldo Batista
Emial : [email protected]
Note from the editor: for more information, see also
Wavelet Digest, vol 1, nr 2.
--------------------------- Topic #5 -----------------------------------
From: Hans Georg Feichtinger, Math. Dept., Univ. of Vienna, Austria (Europe)
Subject: Workshop on Wavelets
Workshop on Wavelets at the Math.Dept., Univ. of Vienna, Austria (Europe)
From March 4th to 10th or 11th an informal workshop at
on WAVELETS and related topics. A larger group of European PhD students and
younger colleagues (mainly from Austria, France, Germany and Poland) are coming
together to present and listen to introductory talks about wavelet theory,
their ideas and interest in that field, and possible applications.
The actual version of the program can be obtained from 131.130.22.36 via
anonymous FTP as file "program?.txt" (? is any number), in directory /pub.
Potential participants are urged to contact the department
through FAX INT+43+1+310 6347 as soon as possible because there is limited
space. Direct Email can be sent to Hans G. Feichtinger =
[email protected] (or [email protected]).
Modest accomodations are possible e.g. at
PENSION FALSTAFF, TEL. INT + 43 / 222-349127
Hotel Michelbeuren: TEL. INT+43/222/432465 ,
FAX INT + 43 + 1 + 4324 65 33
SINGLE ROOM WITH SHOWER AND BR.: around 40$, double approx. 75$ -
A.O.Prof. H.G.FEICHTINGER ******************************************
UNIVERSITY OF VIENNA Email: [email protected]
or: [email protected]
DEPARTMENT OF MATHEMATICS phone: INT+43-1-4088311/4
STRUDLHOFGASSE 4 anoymous FTP through 131.130.22.36
A-1090 VIENNA / WIEN Secr.: INT+43-1-3191366/201
A U S T R I A FAX: INT+43-1-310 63 47
--------------------------- Topic #6 -----------------------------------
From: John Sasso, Jr., Rensselaer Polytechnic Institute, Troy, NY
Subject: Question
As part of my research (on the mathematics side) I managed to show, in
considerable detail, how an orthonormal wavelet basis
psi(t) = sqrt(2)*SUM{(-1)^k*h[1-k]*phi(2t-k)}
is derived from a multireolutional analysis (which is orthogonal).
(I'd like to thank Chris Brislawn & I.G. Rosen, from Los Alamos Lab, for
their report - which helped out considerably). From this derivation, I have
a much greater understanding of multiresolution analysis and how one is
able to derive orthonormal wavelet bases from it (I plan to submit a full
report to the math dept. here).
Now, I have another hurdle to overcome - the regularity and approximation
properties of orthonormal wavelet bases. I have some understanding of the
concept behind vanishing moments and its connection to the degree to which
the associated wavelet basis approximates a function. However, the math
behind vanishing moments appears very complicated (I have a background
in real analysis and introductory functional analysis). Nonetheless, I
have to go about and describe, in considerable detail, the connection between
vanishing moments and its correspondence with wavelets. So my question is:
can anyone provide me with any references (wavelet-related or not) that
which describe vanishing moments, in a rather "readable" format? The
report I intend to present to the math dept. is intended to be easy-to-
follow, but neglects none of the mathematics involved (I'm still dealing with
mathematicians, but the intended audience is seniors and grads). Any help
on this (difficult) matter of vanishing moments & wavelets would be
very much appreciated!
Thank-you very much, -- John
John Sasso, Jr.
Rensselaer Polytechnic Institute, Troy, NY
Senior, Computer Science '93
E-mail: [email protected]
--------------------------- Topic #7 -----------------------------------
From: Bruce Suter, Air Force Institute of Technology.
Subject: Post-Doctoral Positions at AFIT.
Post-Doctoral Research Positions
The Air Force Institute of Technology (AFIT) is inviting qualified applicants
for two post-doctoral research positions. One appointment is available in
each of the Department of Electrical and Computer Engineering and the Departmentof Mathematics and Statistics, beginning in the fall of 1993. The successful
candidates will participate in the development of extensions to the theory of
variable-length signal processing in the context of wavelet and Fourier analysesand/or applications of wavelet analyses to achieve more powerful and flexible
tools for discrimination, detection, speaker identification, co-channel speaker
separation, noise reduction,anti-jamming, etc. Computational facilities are of
the highest caliber and are continually being expanded.
Minimum requirements are (1) U.S. citizenship; (2) a recent PhD in a discipline
appropriate for the position sought - electrical engineering or computer sciencefor one position - mathematics, applied mathematics,or statistics for the other
position; and (3) expertise in Fourier analysis, wavelet analysis, and signal
processing. Salary is commensurate with qualifications.
AFIT is located in southwestern Ohio, just outside Dayton, offering the
advantages of affordable housing, good schools, and possible cooperation with
the Air Force Laboratories at Wright-Patterson Air Force Base.
Applicants should: (1) send a curriculum vitae, including a list of publications(2) send reprints of best publications and (3) have three people send letters ofrecommendation to one of the following, as appropriate for the position sought:
Dr Bruce W. Suter, Dept. of Electrical and Computer Engineering, Air Force
Institute of Technology, AFIT/ENG, 2950 P Street, Wright-Patterson AFB, Ohio
45433-7765.
or
Major Gregory T. Warhola, Ph.D., Dept. of Mathematics and Statistics, Air Force
Institute of Technology, AFIT/ENC, 2950 P Street, Wright-Patterson AFB, Ohio
45433-7765.
AFIT is an equal opportunity, affirmative action employer.
-------------------- End of Wavelet Digest -----------------------------
|
| 1409.23 | wavelet Digest, Vol. 2, Nr. 4 | STAR::ABBASI | i am therfore i think | Sat Mar 20 1993 20:40 | 400 |
| From: US2RMC::"[email protected]" "MAIL-11 Daemon" 19-MAR-1993 19:32:07.69
To: star::abbasi
CC:
Subj: Wavelet Digest, Vol. 2, Nr. 4.
Wavelet Digest Friday, March 19, 1993 Volume 2 : Issue 4
Today's Editor: Wim Sweldens
[email protected]
Today's Topics:
0. The Wavelet Book Digest
1. Special Issue: International Journal of Optical Computing
2. Conference Announcement:
Interaction Between Operator Theory, Wavelet Theory and Control Theory
3. Looking for IBM PC software
4. Wavelet Compression Replies
5. Question: Approximate Schemes for Dense Matrix Algebra
6. Preprints available: wavelets and pseudodifferential equations
7. Looking for VLSI Architectures for 1-D wavelet transform.
8. Question: wavelets in spectrum analysis of time series.
9. Question: Numerical Recipes software
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
To unsubscribe, e-mail with "unsubscribe" followed by your e-mail
address.
To change address, unsubscribe and resubscribe.
Archive site:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet/archive.
Gopher server: bigcheese.math.scarolina.edu.
Current number of subscribers: 2540
--------------------------- Topic #0 -----------------------------------
Note from the editor:
The Wavelet Book Digest
One of the next issues of the Wavelet Digest will feature a summary of
books on wavelets (on the market or to be published).
Therefore we would like to ask you to send us (before April 19)
- announcements of books which have not appeared earlier in the digest,
- reviews on wavelet books (or references to reviews),
- your impressions if you use or used any of these books in a course.
Thank you very much in advance for your collaboration,
Wim Sweldens.
--------------------------- Topic #1 -----------------------------------
From: Yehoshua Zeevi, Technion - Israel Institute of Technology
Subject: Special Issue: International Journal of Optical Computing
Call for Papers
Special Issue of the
INTERNATIONAL JOURNAL OF OPTICAL COMPUTING
Recent advances in mathematical theory related to the representation of signals
and images by Wavelet-type or Gabor-type functions has generated significant
interest in application of waveforms that are localized in both space and
frequency to signal and image processing, pattern recognition, data compression
and analysis of phenomena exhibted by PDE. Optical methods have also been
considered as a viable means to accelerate the computations required in such
applications by capitalizing on its inherent processing paralellism. The inten-
tion of this special issue is to bring together updated research results and to
look into possible future directions in the area of optical processing based on
the limited extent waveforms, such as using the Gabor and Wavelet transforms,
and other related methods. Both theoretical and and experimental studies in the
algorithms/architecture developments, supporting new material and device con-
cepts, as well as system applications are welcome.
To well present the state-of-the-art in theory, implementation, of the wavelets
and related concepts, we solicit contributions of papers describing original
work. The special issue is scheduled for publication in Novemeber 1993. The
deadline for submission is April 29.
Guest Editors
Yao Li Yehoshua Y. Zeevi
NEC Research Institute Department of Electrical Engineering
Physical Sciences Division Technion - Israel Institute of Technology
4 Independence Way Technion City, Haifa 32000
Princeton, NJ 08540, USA Israel
Tel. (609) 951-2623 Tel. 972 4 294728
FAX (609) 951-2496 FAX 972 4 323041
e-mail [email protected] e-mail [email protected]
--------------------------- Topic #2 -----------------------------------
From: Xingde Dai, University of North Carolina at Charlotte (UNCC)
Subject: Conference Announcement
Conference on the Interaction Between Operator Theory,
Wavelet Theory and Control Theory
When Where. The Conference will be held on May1-2, 1993 at
the University of North Carolina at Charlotte (UNCC). The
University locates at east-north of Charlotte city,North
Carolina.
The program (Four one hour talks and more than 15 twenty
minute talks) will be held in 110 Architecture Bldg. (ARCH
110) on the campus of UNCC, beginning at 9:00 AM May 1 and
lasting until late afternoon Sunday, May 2.
Local Transportation. We will start picking people up from the
Holiday Inn by 8:00AM (the Residence Inn is only a 3 minutes
walk from the Holiday Inn).
Hotel. We have reserved blocks of rooms at the Holiday Inn
(phone 704-547-0999) and the Residence Inn (704-547-1122)
at University Executive Park.The University Executive Park is
about one mile from the UNCC campus. Participants are asked
to make their own reservations directly with the hotel. The
conference rate at the Holiday Inn is $45.00 + tax per room,
the rate at Residence Inn is $71 + tax per two bedroom
suite.Reservations should be made by March 30. When making
reservations, be sure to indicate that you are attending the
Conference (Math Conference). There will be a motorcycle race
at the Charlotte Speedway that weekend, so please make your
hotel reservation as early as possible.
Airport Transportation: call The University Shuttle
at 704-553-2424 or 800-951-2424. The fare is \$9.00 per
person from the airport to the University Executive Park.
The following people will give one hour talks:
Charles K. Chui, Texas A\&M University;Ciprian Foias, Indiana
University;David R. Larson, Texas A\&M University;Victor
Wickerhauser, Washington University.
About 15-20 people will give 20 minutes talks.
This conference will provide an opportunity for researchers
to meet and to have discussions. We plan to emphasize the
CONNECTIONS of operator theory with wavelet theory and
control theory.
Support. We have a modest amount of funds from AFOSR, NSA
and UNCC which we can use to cover part of the expenses of
graduate students and 20-minute speakers.
A small registration fee will be collected so we can run the
conference.
Questions: Further questions can be directed to Xingde Dai
(phone (704) 547-2858).e-mail address
([email protected]).
If you are interested in the conference please send us your
mailing address.
--------------------------- Topic #3 -----------------------------------
From: Arnaldo Batista, University of Sussex
Subject: Looking for IBM PC software
Dear Readers,
I'm looking for IBM PC software (commercial or public domain) for the
computation of the Wavelet Transform, or that includes this.
Many thanks in advance for any information.
Email : [email protected]
--------------------------- Topic #4 -----------------------------------
From: Licinda Woudberg, University of Otago, New Zealand
Subject: Wavelet Compression Replies
% Note for the editor: This is a summary of the replies on the
% submission "Looking for compression software", Topic #5 of
% Wavelet Digest Vol. 1, Nr. 10.
Hello,
Firstly I would like to appologise for taking so long to
reply. I have managed to find some information and contacts
regarding Wavelet compression - techniques and software, and similar
programs.
The following is a reply I received from Victor Wickerhauser
(Washington University). Victor has written a number of papers on
Wavelet compression (mainly using the Best Basis alogorithm) partly
as a result of a FBI project to digitise finger prints for computer
recognition. I am sure you can appreciate the quality of information
that must be retained through a lossy compression. The reply is
regarding wavelet compression papers by Victor. pic.tar.Z can be
obtained through anonynous ftp from ceres.math.yale.edu
[130.132.23.22] (I think!)
From: IN%"[email protected]" 11-DEC-1992 14:43:54.76
> But you can get a much better and more recent paper from wuarchive
> (see below), namely dsp.ps.Z (compress'ed postscript, 300-400dpi
> fonts). pic.tar.Z is a highly preliminary paper which discusses
> how much > distortion it costs to discard a certain fraction of the
> coefficeints.
Some of Victor's software (the binaries) is also available through
ftp from wuarchive (wuarchive.wustle.edu [128.252.135.4]). It is
called WPLab and is good for examinign how wavelets work. To obtain
the code involves entering into a non-disclosure agreement. The
binaries are avaliable for both the NeXT (in Objective-C) and for the
Suns.
The following is a reply I received from Eero P. Simoncelli, who has
also written some papers on Wavelet compression.
From: IN%"[email protected]" 21-JAN-1993 05:06:20.08
> Dear Licinda,
>
> I saw your message on the wavelet digest a while back. I wrote a
> public domain wavelet coder called EPIC that is very efficient.
> Source code is in C. It currently only works on grayscale (8bit)
> square images, but I have used it to compress color images by
> converting them from RGB to YIQ and encoding the three images
> separately.
>
> I'm including the README file below. Hope you'll find it useful.
>
> Eero.
% Note from the editor: I didn't include the readme file here, but it can be
% found in Topic #3, Wavelet Digest Vol. 1 , Nr 2.
I recently attended a workshop called "Wavelets Down Under" (in
Australia!) which was very useful. The software is around, but
exactly where for 'your' purposes I am unsure of.
I hope this has been of help to people. Thank you Victor for your
help, and thank you Eero.
Licinda.
(Miss) Licinda Woudberg
Dept. of Computer Science
University of Otago
Dunedin 9001
NEW ZEALAND
NeXTmail: [email protected]
email: [email protected] or [email protected]
--------------------------- Topic #5 -----------------------------------
From: John D. McCalpin, University of Delaware
Subject: Approximate Schemes for Dense Matrix Algebra
Approximate Schemes for Dense Matrix Algebra
We are looking for one or more mathematicians interested in
collaborating on a project involving the application of new approximate
schemes for dense matrix algebra (multi-grid or wavelet transform
based) to a problem in optimal data assimilation using various versions
of the Kalman filter.
The matrices are very large, O(10**4) to O(10**5), and dense, but
hierarchically band dominant. For the mathematician, the project
would involve the derivation of error bounds for the approximate
scheme(s) for a class of matrices that differ slightly from those
studied in the recent literature, and assistance in developing
efficient parallel algorithms. From the dynamical side, we wish to
investigate the properties of the Kalman filter (i.e. its loss of
optimality) for various types and degrees of degradation of the
information content of the error covariance array.
We are interested in pursuing funding through both the NSF
math/geosciences collaboration initiation program (NSF 92-127) and
through the new round of the NSF HPCC program (as part of a larger
group of investigators).
John D. McCalpin, University of Delaware
Ichiro Fukumori, JPL/NASA
John D. McCalpin [email protected]
Assistant Professor [email protected]
College of Marine Studies, U. Del. [email protected]
--------------------------- Topic #6 -----------------------------------
From: Reinhold Schneider, Fb Mathematik THD, Darmstadt
Subject: Preprints available
Preprints available:
"Wavelet Approximation Methods for Pseudodifferential Equations I:
Stability and Convergence"
authors: Dahmen, W., RWTH Aachen
Proessdorf, S., IAAS Berlin
Schneider, R., TH Darmstadt,
Abstract:
This is the first part of two papers which are concerned with generalized
Petrov-Galerkin schemes for elliptic periodic pseudodifferential equations
in R^n covering classical Galerkin methods, collocation and quasiinterpolation
These methods are based on a general setting of multiresolution analysis.
In this part we develop a general stability and convergence theory for such
a framework which recovers and extends many previously studied cases. The
key to the analysis is a local principle due to the second author. Its
applicability relies here on a sufficiently general version of a so called
discrete commutator property. These results establish important prerequisites
for developing and analysing in the second part methods for thefast solution
of the resulting linear systems. These methods are based on compressing the
stiffness matrices relative to wavelet bases for the given multiresolution
analysis.
"Wavelet Approximation Methods for Pseudodifferential Equations II:
Matrix Compression and Fast solution"
authors: Dahmen, W. , RWTH Aachen
Proessdorf, S. , IAAS Berlin
Schneider, R. , TH Darmstadt
Abstract:
This is the second part of two papers which are concerned with genralized
Petrov-Galerkin schemes for elliptic periodic pseudodifferential equations
in R^n. This setting covers classical Galerkin methods, collocation and
quasiinterpolation. The numerical methods are based on a general framework
of multiresolution analysis, i.e., of sequences of nested spaces which are
generated by refinable functions. In this part we analyse compression
techniques for the resulting stiffness matrices relative to wavelet type
bases. We will show that, although the usual stiffness matrices are
generally not sparse, the order of the overall computational work which is
needed to realize a certain order of accuracy is of the form
${\cal O} (N (\log N )^b )$, where $N$ is the number of unknowns and
$b \geq 0 $ is some real number (e.g. for fixed acuracy $b=0$).
Preprints are avaiable from: (after begining of April 1993)
Dr. Reinhold Schneider
Fb Mathematik THD
Schlossgartenstr.7
D-W 6100 Darmstadt
Germany
email: [email protected]
--------------------------- Topic #7 -----------------------------------
From: Ram Narayan, University of Texas.
Subject: Looking for VLSI Architectures for 1-D wavelet transform.
Status: OR
Hello!
Please let me know any publications or preferably thesis reports
on VLSI Architectures for 1-D wavelet transform. Any help would be
appreciated.
Thanking You,
Ram, UTEP, Elpaso.
[email protected]
--------------------------- Topic #8 -----------------------------------
From: Jianping Huang, Texas A&M University
Subject: Looking for wavelets in spectrum analysis.
Hi
I am looking for information on application of wavelet transform
to spectrum analysis of nonstationary time series. I would appreciate
if someone working in this area could tell me where can find more
information and references. Thanks.
best regards,
Jianping Huang
CSRP, Texas A&M University, College Station, TX 77843
[email protected]
--------------------------- Topic #9 -----------------------------------
From: Guru Prasad, Yale University
Subject: Question concerning Numerical Recipes software
I got through ananymous ftp, thanks to W. Press, software in FORTRAN
for 1-d (vector) and 2-d (matrices) wavelet transforms. It has 2 options
i) D4 wavelets or
ii) D4, D12 or D20 wavelets
When I try using these programs to wavelet transform matrices, I get
different results from D4 of (i) which used subroutine DAUB4 explicitly
and from D4 option of (ii) where user has to preset the wavelets he wants.
I am puzzeled about this unable to figure out some thing wrong
in my using the code or understanding or bug in one of the options
Any comments from whoever experienced with these softwares?
Thanks,
Guru
[email protected]
-------------------- End of Wavelet Digest -----------------------------
|
| 1409.24 | wavelet digest , vol. 2 , Nr 6 | STAR::ABBASI | checkmate! | Wed Apr 21 1993 22:30 | 971 |
| From: US2RMC::"[email protected]" "MAIL-11 Daemon" 21-APR-1993 19:19:15.92
To: star::abbasi
CC:
Subj: Wavelet Digest, Vol. 2, Nr. 6.
Wavelet Digest Wednesday, April 21, 1993 Volume 2 : Issue 6
Today's Editor: Wim Sweldens
[email protected]
Today's Topics:
1. Symposium: Applications of Subbands and Wavelets
2. List of recent wavelet papers.
3. Abstract: Wavelet Electrodynamics, Gerald Kaiser
4. Advanced Conference Program: SPIE93
5. Question: Visualise the result of a wavelet transform
6. Question: Image proc. with analytic wavelets
7. Response: Implementing C. Chui's semi-orthogonal wavelets (WD 2.5, #9)
8. Question: Has anyone a Real Wavelet function ?
9. Question: MRA/Wavelets on discrete sets
10. AGU Chapman/ EGS Richardson Memorial Conference
11. New wavelet book (2) J. Benedetto and M. Frazier
12. Correction on WD 2.5 #5: Abstract from Wolfgang Dahmen's talk.
13. Contents Numerical Algorithms
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
To unsubscribe, e-mail with "unsubscribe" followed by your e-mail
address as subject. To change address, unsubscribe and resubscribe.
Archive site:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet/archive.
Gopher server: bigcheese.math.scarolina.edu.
Current number of subscribers: 2592
--------------------------- Topic #1 -----------------------------------
From: A.N. Akansu, New Jersey Institute of Technology
Subject: Symposium: Applications of Subbands and Wavelets
New Jersey Institute of Technology
Department of Electrical and Computer Engineering
Center for Communications and Signal Processing Research
presents
A One Day Symposium
(3rd in a Series)
on
APPLICATIONS OF SUBBANDS AND WAVELETS
March 18, 1994 (Friday)
Organizing Committee
A.N. Akansu, NJIT
R. Ansari, Bellcore
M.J.T. Smith, Georgia Tech.
R.N. Smith, Rome Labs.
P.P. Vaidyanathan, CalTech
M. Vetterli, Columbia U.
* The theories of subband and wavelet transforms and their linkages have been
extensively studied in the fields of engineering and appplied mathematics.
The advantages of these signal analysis and processing tools have been
utilized in engineering problems of several disciplines. This symposium will
provide the forum for presenting the state-of-the-art and recent technological
advances in this active field.
* This symposium will consist of a set of invited talks and the
presentations of accepted papers. The proceedings of the symposium will be
published and provided to the attendees at the meeting.
* Original and unpublished material of subband and wavelet applications
are solicited for presentation at this symposium. All submissions will be
reviewed.
* Please mail SIX copies of your paper (10 pages maximum)
by OCTOBER 1, 1993
to
A.N. Akansu
NJIT ECE Dept.
University Heights
Newark, NJ 07102 USA
* The authors will be notified of acceptance by DECEMBER 15, 1993.
--------------------------- Topic #2 -----------------------------------
From: "Jerzy B. Usowicz" <[email protected]>
Subject: List of recent wavelet papers.
Part 1:
(1) The Binomial QMF-Wavelet Transform for Multiresolution Signal Decompo-
sition - A.N. Akansu, R.A. Haddad, and H. Caglar
IEEE Trans. Signal Processing, Vol.41, No.1, Jan. 1993, pp.13-19
(2) Time-Frequency Design and Processing of Signals Via Smoothed Wigner
Distributions - W. Krattenthaler and F. Hlawatsch
as above, pp.278-287
(3) Analysis and Synthesis of Feedforward NEural Networks Using Discrete
Affine Wavelet Transformations - Y.C. Pati and P.S. Krishnaprasad
IEEE Trans. Neural Networks, Vol.4, No.1, Jan. 1993, pp.73-85
(4) Wavelet Decomposition of Harmonizable Random Processes - P.W. Wong
IEEE Trans. Information Theory, Vol.39, No.1, Jan. 1993, pp.7-18
(5) The Wavelet Transform of Stochastic Process with Stationary Increments
and Its Application to Fractional Brownian Motion - E. Masry
as above, pp.260-264
(6) Non-Parametric Estimation of the Diffusion Coefficient by Wavelet
Methods - V. Genoncatalot, C. Laredo, D. Picard
Scandinavian Journal of Statistics, Vol.19, No.4, 1992, pp.317-336
(7) Inequalities of Littlewood-Paley Type for Frames and Wavelets - C.K.
Chui, X.L. Shi
SIAM J. on Mathematical Analysis, Vol.24, No.1, Jan. 1993, pp.263-
(8) Compactly Supported Wavelets and Boundary Conditions (in French)
P. Auscher
Journal of Functional Analysis, Vol.111, No.1, January 1993, pp.29-44
(9) An Analysis of cardinal Spline-Wavelets - C.K. Chui, J.Z. Wang
J. Approximation Theory, Vol.72, No.1, Jan. 1993, pp.54-69
(10) Using Wavelets to Solve the Burgers Equation - A Comparative Study
R.L. Schult, H.W. Wyld
Physical Review A, Vol.46, No.12, 1992, pp.7953-7958
(11) All Regular Wavelet Basis for L2(R) Come from a Regular Multi-
Resolution Analysis - P. Auscher
Comptes Rendus de l'Academie des Sciences Serie I Mathematique
Vol.315, No.12, Dec. 3 1992, pp.1227-1230
(12) Optical Wavelet Matched Filters for Shift-Invariant Pattern Recognition
Y.L. Sheng, D. Roberge, H. Szu, T.W. Lu
Optics Letters, Vol. 18, No.4, February 15 1993, pp.299-302
(13) A Wavelet Based Space-Time Adaptive Numerical Method for Partial
Differential Equations - E. Bacry, S. Mallat, G. Papanicolaou
RAIRO Mathematical Modelling and Numerical Analysis, Vol.26, No.7,
1992, pp.793-834
Part 2:
[1] Wavelet-Like bases for the Fast Solution of 2nd-Kind Integral Equations,
B. Alpert, G. Beylkin, R. Coifman, V. Rokhlin
SIAM J. on Scientific Computing, Vol.14, No.1, January 1993, pp.159-184
[2] Wavelets and Multigrid
W.L. Briggs, V.E. Henson
SIAM J. on Scientific Computing, Vol.14, No.2, March 1993, pp.506-
[3] Orthonormal Bases of Compactly Supported Wavelets. 2. Variations on a
Theme
I. Daubechies
SIAM J. on Mathematical Analysis, Vol.24, No.2, March 1993, pp.499-519
[4] Orthonormal Bases of Compactly Supported Wavelets. 3. Better Frequency
Resolution
A. Cohen, I. Daubechies
SIAM J. on Mathematical Analysis, Vol.24, No.2, March 1993, pp.520-527
[5] Coherent Structures in Random Media and Wavelets
G. Berkooz, J. Elezgaray, P. Holmes
Physica D, Vol.61, Nos.1-4, 1992, pp.47-58
[6] The Wavelet Transform for the Analysis of Remotely Sensed Images
T. Ranchin, L. Wald
Int. J. Remote Sensing, Vol.14, No.3, Feb. 1993, pp.615-620
[7] Financial Immunization, market Incompleteness and Wavelets
P. Courty, J.M. Lasry, J.M. Morel
C R ACAD SCI SER I MATH, 316(4)93, pp.399-
[8] Singularity Spectrum of Fractal Signals from Wavelet Analysis - Exact
Results
E. Bacry, J.F. Muzy, A. Arneodo
J. Stat. Phys. Vol.70, No.3-4, Feb. 1993, pp.635-674
[9] 2-D Wavelet Transforms - Generalisation on the Hardy Space and Applica-
tion to Experimental Studies
T. Dallard, G.R. Spedding
European Journal of Mechanics B Fluids, Vol.12, No.1, 1993, pp.107-
[10] Looking at Atomic Orbitals Through Fourier and Wavelet Transforms
P. Fischer, M. Defranceschi
Int. J. Quantum Chem., 45(6)93, pp.619-636
[11] A New Look at Rainfall Fluctuations and Scaling Properties of Spatial
Rainfall Using Orthogonal Wavelets
P. Kumar, E. Foufoulageorgiu
Journal of Applied Meteorology, Vol.32, No.2, Feb. 1993, pp.209-222
[12] Scaling Functions for n-Dimensional Wavelets
P.G. Lemarierieusset
C R ACAD SCI SER I MATH, `16(2)93, pp.145-148
[13] B-Spline Signal Processing: Part I - Theory
Michael Unser, Akram Aldroubi, Murray Eden
IEEE Trans. on Signal Processing, Vol.41, No.2, Feb. 1993, pp.821-833
[14] B-Spline Signal Processing: Part II - Efficient Design and Applications
Michael UNser, Akram Aldroubi, Murray Eden
IEEE Trans. on Signal Processing, Vol.41, No.2, Feb. 1993, pp.834-848
--------------------------- Topic #3 -----------------------------------
From: Gerald Kaiser, University of Massachusetts at Lowell
Subject: Abstract: Wavelet Electrodynamics
\magnification=1200
\baselineskip=3 ex
\hsize=16 true cm \vsize=20 true cm
\centerline{\bf WAVELET ELECTRODYNAMICS}
\centerline{(To appear in Proc. of {\sl Wavelets and Applications\/}
conference, Toulouse, 1992)}
\vskip 4ex
\centerline{Gerald Kaiser}
\centerline{Department of Mathematics}
\centerline{University of Massachusetts at Lowell}
\centerline{Lowell, MA 01854, USA}
\centerline{e--mail: kaiserg@ woods.ulowell.edu}
\vskip 6ex
\def\v#1{{\bf#1}}
\centerline{\bf ABSTRACT}
\vskip 4ex
\noindent A wavelet representation of electromagnetic waves (solutions of
Maxwell's equations) is constructed as follows: (a) Using the previously
defined {\sl Analytic--Signal transform,\/} solutions are extended
from real spacetime $\v R^4$ to analytic vector fields on a double tube
domain ${\cal T}\subset \v C^4$ in complex spacetime. (b) The evaluation
maps $e_z \, (z\in {\cal T})$ on the space of such analytic solutions are {\sl
conformal wavelets.\/} They can all be obtained by applying conformal
transformations of spacetime to a single ``basic'' wavelet. The basic wavelet,
in turn, is {\sl uniquely determined\/} by analyticity considerations. (c)
The eight real parameters $z\in {\cal T}$ have a complete geometric and
physical interpretation: They amount to specifying a location $\v x$, a time
$t$, a scale $s$ and a velocity $\v v$ for the wavelet $e_z$. When
$\v v =\v 0$, $e_z$ is a spherical wave which implodes toward
$\v x$, builds up to a ball of radius $\sqrt{3}\,|s|$ at time $t$, then
explodes away from $\v x$. Wavelets with $\v v\ne \v 0$ are {\sl
Doppler--shifted\/} versions of the above. (d) An arbitrary solution can be
decomposed into either stationary or moving wavelets. This leads to a
representation of solutions which is analogous to their Fourier
representation, but with the plane waves replaced by localized wavelets.
Such representations may be useful in the efficient analysis of radiation
emitted by moving localized sources. There also exist decompositions
suitable for radiation due to accelerating sources. The wavelet
representation of electromagnetic fields is closely related to the relativistic
coherent--state representations for Klein--Gordon and Dirac fields
developed in earlier work.
\bye
--------------------------- Topic #4 -----------------------------------
From: Andrew Laine, University of Florida
Subject: Advanced Conference Program: SPIE93
Conference on Mathematical Imaging:
Wavelet Applications in Signal and Image Processing
Part of SPIE's 1993
International Symposium on Optical and Applied Science and Engineering,
San Diego Marriot Hotel & Marina and San Diego Convention Center, San Diego, CA
Two Days: July 15 and 16, 1993
General Chair: Andrew Laine, University of Florida, Gainesville
Email: [email protected]
Program Committee: Alan C. Bovik, Univeristy of Texas, Austin
Charles K. Chui, Texas A&M University
Bjorn Jawerth, University of South Carolina
Arun Kumar, Southwestern Bell Technology Resources
Jorge L. C. Sanz, IBM Almaden Research Center
Steven L. Tanimoto, University of Washington
Michael A. Unser, National Institutes of Health
Day 1 - Thursday, July 15.
9:00 Keynote Address Ronald Coifman (40 minutes)
Yale University
"Adapted waveform analysis, wavelet packets, and LCT as a
tool for signal and image processing"
9:40 Session 1. MATHEMATICAL DEVELOPMENTS.
Ronald Coifman, chair
Irregular periodic sampling of images and their derivatives
M.Zibulski, V.A.Segalescu and Y.Y.Zeevi
Technion-Israel Institute of Technology, Israel
Construction of wavelet analysis over Abelian groups
M. Holschneider, Ctr. de Physique Theorique Luminy, France
Generalized sampling theory and applications to
multiresolutions and Wavelets of L2
Akram Aldroubi, Michael Unser, National Institutes of Health
10:40 - 11:00am Coffee Break (20 minutes)
Signal extrapolation based on Wavelet representation
Xiang-Gen Xia, C.-C.Jay Kuo and Zhen Zhang
University of Southern California
An extension to the karhunen-loeve transform for wavelet and perfect
reconstruction filterbanks
Michael Unser, National Institutes of Health
11:40 Lunch Break (100 minutes)
1:20 Session 2. MEDICAL IMAGING.
Steven Tanimoto, chair
A Multiscale Method for tomographic reconstruction
M. Bhatia, W.C. Karl, A.S. Willsky,
Massachusetts Institute of Technology
Fast Updating in MRI via Wavelet localization
T. Olson, J. Weaver, D. Healy and J. DeStefano
Department of Mathematics, Dartmouth College
Local Inversion of the Radon transform in even dimensions using
wavelets"
David Walnut, Mathematics Dept, George Mason University.
C.A. Berenstein, Univ. of Maryland/College Park
2:20 Session 3. MULTISCALE EDGE DETECTION.
Bjorn Jawerth, chair
Edge localization in images by Wavelet transform
M. Sun C.C. Li and R.J. Sclabassi
Presbyterian-Univ. Hospital and University of Pittsburgh
Recognition of 2-D objects from the wavelet transform
zero-crossing representation
W. Boles, Q.M. Tieng Queensland Univ. of Technology, Austrailia
A non-orthogonal wavelet edge detector with four filter-coefficients
H.J. Kim and C.C. Li University of Pittsburgh
3:20 Coffee Break (20 minutes)
3:40 Session 4. GABOR TRANSFORMS AND APPLICATIONS.
Alan Bovik, chair
Gabor-wavelet pyramid for the extraction of image flow
V.C. Chen, Naval Research Laboratory
T.R. Tsao, Vitro Corporation
Selecting the projection functions used in an Iterative
Gabor Expansion
R.N. Braithwaite,University of California at Riverside
M.P. Beddoes, University of British Columbia
Selection of multiresolution features
M.M. Rizki, Wright State University
M.A. Zmuda, L.A.Tamburino,Wright-Patterson AFB
The Gabor transform: theory and computation
J.Yao, University of Massachusetts at Lowell
5:00 Session 5. IMAGE FUSION.
Michael Unser, chair
Wavelet multiresolution image fusion
A.E. Iverson, Science Applications International Corporation
Efficient data fusion using wavelet transforms: the case of
SPOT satellite images
T. Ranchin, Centre D'energetique-Groupe
Teledetection & Modelisation
A multiresolution image registration procedure using spline pyramids
M. Unser, A. Aldroubi and C.R. Gerfen
National Institutes of Health
DAY 2 - Friday, July 16
8:20am Session 6. IMAGE COMPRESSION AND CODING.
Bjorn Jawerth, chair
Embedded image coding using zerotrees of wavelet coefficients
J.M. Shapiro, David Sarnoff Research Center
High rate image compression using spline-wavelet packets
Q. Liu, A.K. Chan, C.K. Chui, E. Pettit, and D. Rhiness
Texas A&M University
Local cosine transform: A method for the reduction of the blocking
effect in JPEG.
G. Aharoni, A. Averbuch, Tel Aviv University, Israel
R. Coifman, Yale University
M. Israeli, Technion, Israel Institute of Technology
Lattice quantization in the wavelet domain
W.C. Powell, Multimidia Systems Group, Microsoft Corp.
S.G. Wilson, University of Virginia
Optimal thresholding in wavelet image compression
J.B. Borger, F.O.Zeppenfeldt
National Aerospace Laboratory, the Nethelands
A. Koppes, University of Nijmegen, Nethelands
Wavelet and subband coding of images: a comparative study
F. Hartung, Institute for Communication Engineering, Germany
J.H. Husoy, Rogaland University Center, Norway
10:20 Coffee break
10:40 Session 7. TEXTURE ANALYSIS AND SEGMENTATION.
Michael Unser, chair
Wavelets for segmentation based image compression
B. Deng, B. Jawerth and Wim Sweldens
University of South Carolina
Texture segmentation using wavelet packets
Y. Lin, T. Chang and C.-C.J. Kuo
University of South California
Edge preserved image smoothing and Segmentation by using wavelet
A. Laine and S. Song, University of Florida
11:40 Lunch break (80 minutes)
1:00pm Sessions 8. FRAMES and OVERCOMPLETE REPRESENTATIONS.
Charles Chui, chair
Matrix approach to frame analysis of Gabor-type image representation
M. Zibulski, Y.Y. Zeevi
Technion-Israel Institute of Technology
Local frames
A. Teolis, J.J. Benedetto, University of Maryland
Portraits of frames: overcomplete representations with applications
to image processing
A. Aldroubi, National Institutes of Health
2:00 Session 9. HIGH-SPEED PROCESSING.
Jorge L. C. Sanz, chair
A perfectly invertible, fast, and complete wavelet transform for
finite length sequences: the discrete periodic wavelet transform
N. Getz, University of California Berkeley
Fast orthogonal transform algorithms for multiresolution time-
sequency signal decomposition and processing
A. Drygajlo, Swiss Federal Institute of Technology Lausanne
High-performance wavelet engine
F.Taylor, J.Mellot, E.Strom, I.Koran, University of Florida
M.Lewis, The Athena Group Inc.
Optical Harr wavelet transform for image features extraction
Y. Yan, W. Wang, Z. Wen and M. Wu
Tsinghua University, P.R.China
3:20 Coffee break (20 minutes)
3:40 Session 10. NOISE REDUCTION AND TRANSIENT DETECTION.
Alan Bovik, chair
Detection of signal in noise using wavelet receiver
Y.C. Chen, Naval Research Laboratory
Identification of transients in noisy series
R. Carmona, University of California at Irvine
Detection of anomalies in an image wavelet analysis
M. Allam, J. Zhang, University of Wisconsin-Milwaukee
4:40 Session 11. FEATURE DETECTION IN RADAR AND RADIO SIGNALS.
Arun Kumar, chair
Application of wavelet analysis to radar resolution performance
C.C. Sung, University of Alabama in Huntsville
W. Friday, U.S. Army Missile Command RD&E Center
G.A. Larson, Nichols Research Crop.
Quantifying features in the dynamic spectra of radio pulsars:
localization of fringes using a 2-d Wavelet transform
R.S. Foster, Naval Research Laboratory
Speckle reduction in synthetic aperture radar imagery using wavelet
T. Ranchin,
Centre D'energetique-Groupe Teledetection & Modelisation
STANDBY PAPER: included in the proceeding, but may not be presented:
Wavelet packets algorithm and its application in signal Detection
Guanghui Zhang, Huazhong Univernity, P.R.China.
Zailu Huang, Institute of Automation, P.R.China.
For additional information please contact:
SPIE, P.O. Box 10, Bellingham, WA 98227-0010
Telephone: (206) 676-3290 Telex: 46-7053
Telefax: (206) 647-1445; OPTO-LINK (206) 733-2998
Internet: [email protected] or
[email protected]
--------------------------- Topic #5 -----------------------------------
From: [email protected] (Xuemei Chen)
Subject: Question
I used matlab to visualise the result of wavelet transform on a set of data
on a time-frequency plane. Basically I tried the function imagesc, with the
image gray levels representing the WT amplitudes. However I found no way to
control the axis scaling. I don't like the YDir to be inversed, and I would
prefer the origin to be (0, 0).
PCOLOR seems to satisfy these two requirments but I don't like its grid to be
shown. The image and pcolor both use built-in funcs.
So I have no way to control the output.
The automatic thresholding of these image utils is also a problem,
for which I can not compare the WT results of different data.
Could anybody who is implementing WT methods advise me as to a better visual
representation of the WT, references, publically available software, or solution
within the matlab? Thanks a lot!
-P.S. One way to see the amplitude for each level clearly is to plot
the amplitude along the time axis for each level. But this is not efficient as
I may need 10 such plot if I have 1024 data, which is hard to handle. Hence my
concern here is about a good method to represent all the information
that WT can provide in a complete and efficient way.
Xuemei Chen
[email protected]
--------------------------- Topic #6 -----------------------------------
From: John Sasso, Jr., Rensselaer Polytechnic Institute, Troy, NY
Subject: Question: Image proc. with analytic wavelets
Hello,
I'm trying to find a way to perform the 2-D wavelet transform on images
using an analytic wavelet function (i.e. Mexican hat, Meyer, etc..) rather
than using the Daubechies coefficient matrix (which is commonly done).
Given such a wavelet (orthonormal or not), which is one dimensional, how
would I go about such a procedure on, say, a 512x512 grayscale image???
Do I still follow the same multiresolution decomposition as prescribed by
S. Mallat? The wavelets that I work with a mostly nonorthogonal - thus
require a dual in order to perform an inverse transformation. Any help
on this matter would be greatly appreciated.
John Sasso, Jr.
Rensselaer Polytechnic Institute
Troy, NY
e-mail: [email protected]
--------------------------- Topic #7 -----------------------------------
From: Michael Unser, National Institutes of Health
Subject: Response: Implementing C. Chui's semi-orthogonal wavelets (WD 2.5, #9)
Response to Topic #9, Wavelet digest Volume 2 : Issue 5
I can give you some advice of the implementation of the B-spline wavelets
which are the same as the Chui-Wang semi-orthogonal spline wavelets. We
have had quite some experience with those wavelets (having constructed them
independently) and we have successfully applied them to image processing
(texture analysis, in particular).
The implementation rule that insures reversibility is simple : you should
use boundary conditions that are compatible between the different scales
and residual signals. The safer approach that works for all wavelet types
is to use a simple periodic signal extension.
In image processing, one usually prefers periodic symmetrization which
avoids border artifacts. This approach certainly works for B-spline
wavelets of odd degree (which are symmetrical), as well as for the
Battle-Lemarie wavelets. Implementation details (including how to handle
the boundaries) can be found in [1], and [2] (for a tabulated filter-bank
example).
Unfortunately, this approach does not work directly for the
anti-symmetrical B-spline wavelets of even degree (or odd order, depending
how they are defined) described by Chui and Wang [3]. The problem is that
filtering a symmetrical signal (at the boundary) with an anti-symmetrical
template yields an anti-symmetric signal; in other words, for the wavelet
transform to be reversible, you would have to use some form of
anti-symmetric boundary conditions for the residues. I let you work out the
details.
If you want to avoid these problems just stick with symmetrical wavelets !
I can send you reprints of [1] and [2] if you give me your postal address.
References :
[1] M. Unser, A. Aldroubi and M. Eden, "A family of polynomial spline
wavelet transforms", Signal Processing, vol. 30, pp. 141-162, January 1993.
[2] M. Unser, A. Aldroubi and M. Eden, "On the asymptotic convergence of
B-spline wavelets to Gabor functions", IEEE Trans. Information Theory, vol.
38, pp. 864-872, March 1992.
[3] C.K. Chui and J.Z. Wang, "On compactly supported spline wavelets and a
duality principle", Trans. Amer. Math. Soc., vol. 330, pp. 903-915, 1992.
Michael Unser
BEIP, Bldg 13, Room 3W13
National Institutes of Health
Bethesda, MD 20892, USA
Email : [email protected]
--------------------------- Topic #8 -----------------------------------
From: Richard Favero, University Of Sydney
Subject: Question: Has anyone a Real Wavelet funciton?
Hi all,
I am using wavelets for speech analysis.
I am in the process of implementing a sampled continuous wavelet transform.
I have coded two wavelets (Morlet and a Hanning based). I require the phase
information to be indicated on the output as Vetterli and Herley showed in
the article IEEE Trans on Sig.Proc Vol 40 No 9. They showed how the magnitude
and phase info for a burst sine can be shown on the one diagram.
I would like a formula for a "real" wavelet (something as a function of t).
Vetterli's derivation is a discrete one so it does not fit in well with the
code I have put together.
B.T.W How have other people who require sub octave resolution implemented
a wavelet transform. I am using a fast convolution of filters approach which
is reasonably quick but are there better ways. (The Fast DWT works on an
octave basis. Is there a way to do the DWT on a sub octave approach).
Richard Favero
University Of Sydney
[email protected]
--------------------------- Topic #9 -----------------------------------
From: Richard Bartels, University of Waterloo, Canada
Subject: MRA/Wavelets on discrete sets
I am interested in the transcription of the B-wavelet results in
Chui's book to the case in which the inner product is defined
on a discrete set, which I take to be the natural setting for
a finite sampling of data. That is,
+inf ---- N -----
Integral f(t)g(t) dt is replaced by Sum f(t )g(t )
-inf i=0 i i
Transcriptions from the results by Lyche and Morken, which provide
constructions of minimally supported B-wavelets on arbitrary knot sets,
would be even more interesting.
Any and all references gratefully accepted. e-mail to [email protected]
-Richard Bartels
--------------------------- Topic #10 -----------------------------------
From: [email protected] (GANG From Paris)
Subject: AGU Chapman/ EGS Richardson Memorial Conference:
Nonlinear variability in geophysics 3
AGU Chapman/ EGS Richardson Memorial Conference
NONLINEAR VARIABILITY in GEOPHYSICS 3:
Scaling and multifractal processes
Institut d'Etudes Scientifiques de Carg se
10-17 September 1993
Conference description
A third conference to develop confrontation between theories and
experiments on scaling/multifractal behaviour of geophysical fields.
Subjects covered will include climate, clouds, earthquakes, atmospheric
and ocean dynamics, tectonics, precipitation, hydrology, the solar cycle
and volcanoes.
Areas of focus will include new methods of data analysis (especially for
the reliable estimation of multifractal and scaling exponents), applied to
rapidly growing data bases from in situ networks and remote sensing.
The corresponding modeling, prediction and estimation techniques will also
be emphasized as will the current debates about stochastic and
deterministic dynamics, fractal geometry and multifractals, self-organized
criticality and multifractal fields.
Mini-course: on the 10 and 11 September 1993 there will be an optional
short courses on multifractals (multifractal processes, multifractal phase
transitions, Generalized Scale InvarianceI).
Conveners
L. Knopoff (UCLA, Los Angeles, USA)
S. Lovejoy (McGill U., Montreal, Canada)
D. Schertzer (Universit . P. et M. Curie, Paris, France)
Local Organizing Committee:
M.F. Hanseler, (Institut d'Etudes Scientifiques de Cargese,
Paris and Cargese)
P. Ladoy, (Meteo France)
Y. Tessier, (Universit. P. et M. Curie, Paris, France)
Scientific committee
C. Barton (USA)
L. De Cola (USA)
K. Fraedrich (Germany)
R. Glazman (USA)
P. Hubert (France)
Y.Y. Kagan (USA)
S.S. Moiseev (Russia)
C. Nicolis (Belgium)
A. Osborne (Italy)
R. Pierrehumbert (USA)
Sponsored by
American Geophysical Union
Centre National de la Recherche Scientifique
European Geophysical Society
Meteo-France
US Department of Energy
US Geological Survey
Location
The conference will be held at the Institut d'Etudes Scientifiques de
Cargse (France). Carg se is a pleasant little village of considerable charm
and historic interest located 50 km north of Ajaccio, on the west coast of
Corsica. The Institute itself is situated by the sea, about 2 km south of
the village.
Travel
A group ticket for 60 seats has been organized on regular flight from
Paris (Orly West) to Ajaccio on September 10 at 11:55 and back again
on September 17 at 16:00. One way price is 665 FF. Reservations are
binding 14 days before departure. Otherwise there are regular flights
to Ajaccio from Paris, Lyon, Marseille, Nice, London, Frankfort, Munchen,
Roma, MilanoI. There are ferries from Marseille, Nice and Toulon, and from
Genova to Bastia (and then train to Ajaccio). Between Ajaccio and Cargese
special bus services are arranged on 10/17 September (80 FF one way).
Otherwise you may take a taxi (420 FF one way).
Accommodation
Lodging is available in the village of Cargese, either in hotels (from 250
FF per day) or in flats (from 800 FF per week). There is also, as well as
camping available (20 FF per day, including breakfast).
Breaks & Meals
Coffee/tea/fresh juices is served twice a day during the breaks lunch meals
are served at the Center). Both services are included in the conference
fee. Evening meals can be taken at different restaurants of the village
(100-130 FF).
Social Events
Besides hiking, swimming and snorkeling in the Menasina bay of the center,
an ice breaker reception, a Corsican barbecue, an excursion to the
oceanographic centre as well as a boat excursion along the multifractal
coast of Corsica is foreseen.
Abstracts
Contributions should be summarized as abstracts in the "extended" EGS
format (16.5 cm following the abstract should include the name and full
address (inc. fax, e-mail, etc.) of the speaker, a statement concerning the
preference for oral or poster presentation and the equipment in addition to
standard.
Publication
All abstracts will be included in the programme booklet, which will be
distributed to the participants. The organizers intend to publish a book
proceedings afterwards.
Conference Fee
The fees for the conference (12-17 September) are $320, -regular and
$180 -for students. The fees for the two-day mini-course
(10 and 11 September) are $80, -regular and $ 40, - for students,
respectively. These fees include the conference material, the programme
booklet, the refreshments during the breaks, lunch and the reception.
Fees can be paid at the conference or in advance to the EGS Office by
Eurocheque, bank transfer (Deutsche Bank, Sort Code 260 700 72,
Account 05_11 33 222) credit card (American Express,
Eurocard/Mastercard/Access). Financial support may be awarded upon specific
request (a limited number of grants are available)
Participation
The conference will be limited to 80 participants. Pre-registration
(indicate name, institution, address inc. fax and e-mail, date of arrival,
preference for accommodation, accompanying person(s)I) and extended
abstracts should arrive before 31 May 1993 and should be sent to:
EGS Office
Postfach 49
Max-Plank-Srr. 1
3411 Katlenburg-Lindau
FRG
Tel.: +49 5556 1440
Fax: +49 5556 4709
Tx: 965 515 cop d
SPAN: LINMPI::EGS
EARN: [email protected]
Internet: [email protected]
Additional information may be obtained on [email protected]
Tribute to Lewis Fry Richardson
With this conference the European geophysical community wishes to pay
tribute to Lewis Fry Richardson, on the 40th aniversary of his death.
He was a pioneer of scaling ideas and fractality. He was the first
to suggest numerical integration of the equations of motion of the
atmosphere, to introduce anondimensional number associated with fluid
convective stability (the Richardson number), to suggest that turbulent
cascades are the fundamental mechanism for driving the atmosphere
(large eddies break up into smaller ones), and as a founder of cascade
and scaling theories of atmospheric dynamics, to introduce the Richardson
dividers method of successively increasing the resolution of fractal
curves.
His pioneering book on "Weather prediction by numerical processes" appeared
in 1922.
Deadline 31 May 1993
% Intention to participate
% Receipt of Abstracts in the extended EGS format (16.5 x 22.0 cm)
--------------------------- Topic #11 -----------------------------------
From: John J. Benedetto, University of Maryland.
Subject: New wavelet book (2)
Note from the editor:
The following is an ascii version of the announcement in WD 2.5 #1.
This book will appear in late spring of 1993 (probably by this June).
The publisher is CRC Press, Inc.
WAVELETS: Mathematics and Applications
John J. Benedetto and Michael Frazier, Editors
TABLE of CONTENTS
Introduction
John J. Benedetto and Michael Frazier
Core Material
[Chapter 1] Construction of Orthonormal Wavelets
Robert S. Strichartz
[Chapter 2] An Introduction to the Orthonormal Wavelet Transform
on Discrete Sets
Michael Frazier and Arun Kumar
[Chapter 3] Gabor Frames for L^2 and Related Spaces
John J. Benedetto and David F. Walnut
[Chapter 4] Dilation Equations and the Smoothness of Compactly
Supported Wavelets
Christopher Heil and David Colella
[Chapter 5] Remarks on the Local Fourier Bases
Pascal Auscher
Wavelets and Signal Processing
[Chapter 6] The Sampling Theorem, Phi-Transform and Shannon Wavelets
for R, Z, T, and Z_N
Michael Frazier and Rodolfo Torres
[Chapter 7] Frame Decompositions, Sampling, and Uncertainty Principle
Inequalities
John J. Benedetto
[Chapter 8] Theory and Practice of Irregular Sampling
Hans Feichtinger and Karlheinz Gr"ochenig
[Chapter 9] Wavelets, Probability, and Statistics: Some Bridges
Christian Houdre
[Chapter 10] Wavelets and Adapted Waveform Analysis
Ronald R. Coifman and Victor Wickerhauser
[Chapter 11] Near Optimal Compression of Orthonormal Wavelet Expansions
Bjorn Jawerth, Chia-chang Hsiao, Bradley Lucier,
and Xiangming Yu
Wavelets and Partial Differential Operators
[Chapter 12] On Wavelet-Based Algorithms for Solving Differential Equations
Gregory Beylkin
[Chapter 13] Wavelets and Nonlinear Analysis
Stephane Jaffard
[Chapter 14] Scale Decomposition in Burgers' Equation
Frederic Heurtaux, Fabrice Planchon, and Victor Wickerhauser
[Chapter 15] The Cauchy Singular Integral Operator and Clifford Wavelets
Lars Andersson, Bjorn Jawerth, and Marius Mitrea
[Chapter 16] The Use of Decomposition Theorems in the Study of Operators
Richard Rochberg
--------------------------- Topic #12 -----------------------------------
From: Editor
Subject: Correction on WD 2.5 #5: Abstract from Wolfgang Dahmen's talk.
By mistake the abstract of Wolfgang Dahmen's talk was not included
in the announcement of the wavelet minisymposium at the Dutch
Mathematical Conference in Amsterdam. It is included here (latex version).
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\begin{document}
\begin{center}
{\large {\bf Computing inner products of wavelets}}\\
W. Dahmen (joint work with C.A. Micchelli)
\end{center}
Employing wavelet-type bases for Galerkin discretizations of elliptic
boundary value problems leads to very good preconditioners and hence
to very efficient conjugate gradient solvers
for the resulting linear systems. Practical implementations of such
concepts, however, require the computation of inner products of wavelets.
It is pointed out that essentially all computational task needed for setting
up the linear systems can be reduced to the computation of quantities of
the form
\begin{equation}
\label{1}
\int\limits_{\R^s}\varphi_0(x)\prod_{j=1}^m (D^{\mu^j}\varphi_j)(x-\alpha^j)dx~,
\end{equation}
where $\alpha^j\in \Z^s, ~\mu^j\in \Z^s_+$ and the $\varphi_j$'s are
are refinable functions, i.e., they satisfy two-scale relations
$$
\varphi_j(x)=\sum_{\alpha \in \Z^s}a^j_{\alpha}\varphi_j(2x-\alpha )
$$
for some masks $\{a^j_{\alpha}\}_{\alpha \in \Z^s}$. One should note that
for differential operators with variable coefficients one may indeed have
to handle more than two factors in (\ref{1}) and that it is very important
to admit different refinable functions occuring in such integrals.
It is shown that the exact (up to roundoff) computation of these integrals
can be reduced to an eigenvector/moment problem whose size depends only
on the support of the scaling functions $\varphi_j$. The starting point is
the observation that (\ref{1}) may be viewed as the restriction of derivatives
of a certain refinable function of $ms$ variables to lattice points.
The main issue is to show that the above mentioned moment conditions,
which come from polynomial reproduction properties of scaling functions,
determine certain eigenvectors uniquely. Using results about convergence
of stationary subdivision schemes one can prove that this is indeed the case
when the initial scaling functions $\varphi_j$ are stable. One should note
that when $s$ is larger than one, i.e., when dealing with partial differential
equations the uniqueness problem is much more involved than for $s=1$.
If time permits
some comments on current implementations will be made.
\end{document}
--------------------------- Topic #13 -----------------------------------
From: [email protected] (Daniel Baltzer)
Subject: Contents Numerical Algorithms
Contents NUMERICAL ALGORITHMS,
Editor-in-Chief: Claude Brezinski,
Laboratoire d'Analyse Numerique et d'Optimisation, UFR IEEA - M3,
Universite de Lille 1, France, fax: +33 - 20 43 49 95, e-mail:
[email protected]
Numerical Algorithms, Volume 4 (1993), issues 1,2:
Computing the real roots of a polynomial by the exclusion algorithm, J.-P.
Dedieu and J.-C. Yakoubsohn
On the computation of a versal family of matrices, L. Stolovitch
Model reduction techniques for sampled-data systems, F.D. Barb and M.Weiss
Automatic solution of regular and singular vector Sturm-Liouville problems,
M.Marletta
An efficient Total Least Squares algorithm based on a rank-revealing
two-sided orthogonal decomposition, S. van Huffel and H. Zha
On the design of an upwind scheme for compressible flow on general
triangulations, Th. Sonar
Fast orthogonal decomposition of rank deficient Toeplitz matrices, P.C. Hansen
A homotopy algorithm for a symmetric generalized eigenproblem, K.Li and T.-Y. Li
A note on the implementation of the successive overrelaxation for linear
complementarity problems, W. Niethammer
Requests for sample copies and orders are to be sent to J.C. Baltzer AG,
fax: +41-61-692 42 62, e-mail: [email protected]
-------------------- End of Wavelet Digest -----------------------------
|
| 1409.25 | wavelet digest , vol 2 Nr 7 | STAR::ABBASI | i drink milk and proud of it too | Sun May 09 1993 00:17 | 363 |
| From: US2RMC::"[email protected]" "MAIL-11 Daemon" 8-MAY-1993 22:26:43.67
To: star::abbasi
CC:
Subj: Wavelet Digest, Vol. 2, Nr. 7.
Wavelet Digest Saturday, May 8, 1993 Volume 2 : Issue 7
Today's Editor: Wim Sweldens
[email protected]
Today's Topics:
1. Wavelet software for S.
2. Chinese translation of "wavelet"
3. Response to WD Vol 2.6 #9
4. Response to WD Vol 2.6 #8
5. CFP: First IEEE International Conference on Image Processing
6. Question
7. Preprint: Parameterizations for Daubechies wavelets
8. Looking For A Multiresolution Codebook
9. Preprint: Wavelets on Closed Subsets of the Real Line
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
To unsubscribe, e-mail with "unsubscribe" followed by your e-mail
address as subject. To change address, unsubscribe and resubscribe.
Archive site:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet/archive.
Gopher server: bigcheese.math.scarolina.edu.
Current number of subscribers: 2602
--------------------------- Topic #1 -----------------------------------
From: G. P. Nason, School of Mathematical Sciences, University of Bath
Subject: Wavelet software for S.
WAVELET TRANSFORMS AND THRESHOLDING IN 1 AND 2 DIMENSIONS FOR S
Version 2.1
WHAT: S software to compute the 1- and 2-dimensional wavelet transform
using Mallat's pyramidal algorithm with Daubechies compactly supported
orthonormal wavelets and incorporating an implementation of the
thresholding ideas of Donoho and Johnstone.
Package consists of:
* S functions that compute the 1- and 2-dimensional
discrete wavelet transform, and inverse. (Uses
dynamically loaded C code)
* S functions to perform thresholding
* S functions to plot and interpret sets of wavelet coefficients
* Full on-line S help
WHERE: Obtain via anonymous FTP from
gdr.bath.ac.uk
Userid "anonymous" and your email address as the password.
Software is in directory /pub/masgpn as the file "wavelet.shar.Z"
Unpacking instructions in "README.Z". The transfer should be
done using the BINARY transfer mode of ftp and then the files
should be uncompressed using the UNIX command "uncompress".
Extra draft documentation is in "dws.ps.Z" - a compressed PostScript
file.
ALSO available from the statlib archive (ftp: lib.stat.cmu.edu)
userid "statlib", your email address as password. The software
is in the "S" directory as "wavethresh".
CONTACT: [email protected] for further information
--------------------------- Topic #2 -----------------------------------
From: Wei-Chang Shann, National Central University, Taiwan, R.O.C.
Subject: Chinese translation of "wavelet"
Chinese readers,
I would like to propose a (probably new) Chinese translation of "wavelet",
different from that we read on usual English-Chinese dictionaries. The
proposed translation sounds ling2 po1. Here Po1 means wave, we all know.
Ling2 has several meanings, two concerned are (1) interlaced, like in
ling2 luan4 (a mess); (2) approaching, like in ling2 chen2.
For a very short explanation in Chinese, Please ftp dongpo.math.ncu.edu.tw
(140.115.25.3), login as anonymous, cd pub/shann/wavelets, get nameb5.ps
(if you read traditional Chinese) or namegb.ps (if you read simplified
Chinese). Print it on any PostScript printer.
Wei-Chang Shann, Associate Professor
Dept of Mathematics, National Central University, Chung-Li, Taiwan, R.O.C.
(03)425-6704 ext. 166 (work); (03)425-7379 (work.FAX)
[email protected]
--------------------------- Topic #3 -----------------------------------
From: Akram Aldroubi, National Institutes of Health, Bethesda MD
Subject: Response to WD Vol. 2 Nr. 6 # 9
Response to the question of Richard Bartels Wavelet Digest Vol. 2 # 6
Dear Colleagues,
We have some work on the problem of multiresolutions and wavelets
for discrete sequences (l_2) with the appropriate discrete inner product
([1] and [2]).
I can send you the reprints if you give me your postal address.
[1] A. Aldroubi, M. Unser and M. Eden, Discrete spline filters for
multiresolutions and wavelets of l2.
[2] A. Aldroubi, M. Unser and M. Eden, Asymptotic properties of least
square spline filters and application to multi-scale decomposition of
signals, ISITA'90, (1990), pp. 271-274.
Akram Aldroubi
Rm. 3W13, Bldg. 13
BEIP/NCRR
National Institutes of Health
Bethesda, MD 20892
E-mail: [email protected]
Tel: (301) 496-4426
Fax: (301)496-6608
--------------------------- Topic #4 -----------------------------------
From: Anil A. Bharath, Imperial College London
Subject: Response to WD Vol 2.6 #8
Response to Richard Favero's question, Item 8 of Wavelet Digest vol. 2, Issue 6
Richard,
In order to see how sub-octave resolution might be obtained, have
a look at the excellent paper by Shensa (IEEE Transactions on Signal Proc-
essing, vol. 40, no. 10, October, 1992). In addition, I have found the
paper by Olivier Rioul and Pierre Duhamel (IEEE Transactions on Information
Theory, vol. 38, no. 2, March 1992) to be very useful in implementing sub-
octave decompositions using a combination of the Mallat and "a trous"
algorithms, with families of wavelets. The formulations refer to
sub-octave frequency domain partitioning as a "voice" of the decomposition.
As for the matter of analytic forms for continuous wavelets, I can provide
a few pointers if you e-mail me directly; a common choice which is not
*strictly* admissible as a wavelet (but which is easy to work with,
and which you mentioned in your question) is the Morlet Wavelet
psi(t) = exp^(-t*t/2).e^(j omega_0 t)
where omega_0 must be made sufficiently large that FT{psi(t)}(0)
is very small. I have found this choice quite useful for analyzing
asymptotic signals.
Anil A. Bharath,
Centre for Biological and Medical Systems,
Level 1,
Mech. Eng. Building
Imperial College
London SW7 2BT
E-Mail: [email protected]
--------------------------- Topic #5 -----------------------------------
From: Alan C. Bovik, U. Texas, Austin
Subject: CFP: First IEEE International Conference on Image Processing
FIRST IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING
November 13-16, 1994
Austin Convention Center, Austin, Texas, USA
PRELIMINARY CALL FOR PAPERS
Sponsored by the Institute of Electrical and Electronics En-
gineers (IEEE) Signal Processing Society, ICIP-94 is the inaugur-
al international conference on theoretical, experimental and ap-
plied image processing. It will provide a centralized, high-
quality forum for presentation of technological advances and
research results by scientists and engineers working in Image
Processing and associated disciplines such as multimedia and
video technology. Also encouraged are image processing applica-
tions in areas such as the biomedical sciences and geosciences.
SCOPE:
1. IMAGE PROCESSING: Coding, Filtering, Enhancement, Restoration,
Segmentation, Multiresolution Processing, Multispectral Process-
ing, Image Representation, Image Analysis, Interpolation and Spa-
tial Transformations, Motion Detection and Estimation, Image Se-
quence Processing, Video Signal Processing, Neural Networks for
image processing and model-based compression, Noise Modeling,
Architectures and Software.
2. COMPUTED IMAGING: Acoustic Imaging, Radar Imaging, Tomography,
Magnetic Resonance Imaging, Geophysical and Seismic Imaging, Ra-
dio Astronomy, Speckle Imaging, Computer Holography, Confocal Mi-
croscopy, Electron Microscopy, X-ray Crystallography, Coded-
Aperture Imaging, Real-Aperture Arrays.
3. IMAGE SCANNING DISPLAY AND PRINTING: Scanning and Sampling,
Quantization and Halftoning, Color Reproduction, Image Represen-
tation and Rendering, Graphics and Fonts, Architectures and
Software for Display and Printing Systems, Image Quality, Visual-
ization.
4. VIDEO: Digital video, Multimedia, HD video and packet video,
video signal processor chips.
5. APPLICATIONS: Application of image processing technology to
any field.
PROGRAM COMMITTEE:
GENERAL CHAIR: Alan C. Bovik, U. Texas, Austin
TECHNICAL CHAIRS: Tom Huang, U. Illinois, Champaign and
John W. Woods, Rensselaer, Troy
SPECIAL SESSIONS CHAIR: Mike Orchard, U. Illinois, Champaign
EAST EUROPEAN LIASON: Henri Maitre, TELECOM, Paris
FAR EAST LIASON: Bede Liu, Princeton University
SUBMISSION PROCEDURES
Prospective authors are invited to propose papers for lecture or
poster presentation in any of the technical areas listed above.
To submit a proposal, prepare a 2-3 page summary of the paper in-
cluding figures and references. Send five copies of the paper
summaries to:
John W. Woods
Center for Image Processing Research
Rensselaer Polytechnic Institute
Troy, NY 12180-3590, USA.
Each selected paper (five-page limit) will be published in the
Proceedings of ICIP-94, using high-quality paper for good image
reproduction. Style files in LaTeX will be provided for the con-
venience of the authors.
SCHEDULE
Paper summaries/abstracts due: 15 February 1994
Notification of Acceptance: 1 May 1994
Camera-Ready papers: 15 July 1994
Conference: 13-16 November 1994
CONFERENCE ENVIRONMENT
ICIP-94 will be held in the recently completed state-of-the-art
Convention Center in downtown Austin. The Convention Center is
situated two blocks from the Town Lake, and is only 12 minutes
from Robert Meuller Airport. It is surrounded by many modern
hotels that provide comfortable accommodation for $75-$125 per
night.
Austin, the state capital, is renowned for its natural hill-
country beauty and an active cultural scene. Within walking dis-
tance of the Convention Center are several hiking and jogging
trails, as well as opportunities for a variety of aquatic sports.
Live bands perform in various clubs around the city and at night
spots along Sixth Street, offering a range of jazz, blues,
country/Western, reggae, swing and rock music. Day temperatures
are typically in the upper sixties in mid-November.
An exciting range of EXHIBITS, VENDOR PRESENTATIONS, and SOCIAL
EVENTS is being planned. Innovative proposals for TUTORIALS, and
SPECIAL SESSIONS are invited.
For further details about ICIP-94, please contact:
Conference Management Services
3024 Thousand Oaks Drive
Austin, Texas 78746
Tel: 512/327/4012; Fax:512/327/8132
email: [email protected]
--------------------------- Topic #6 -----------------------------------
From: Sanjai Bhargava, COMSAT Labs, Clarksburg, MD 20871
Subject: Question
I would like to get some reference on localized cosine transforms.
Any help would be appreciated.
Thanks
Sanjai Bhargava
COMSAT Labs.
(301)-428-4502 ( phone ) 22300 Comsat Drive
(301)-428-9287 ( FAX ) Clarksburg, MD 20871
e-mail: [email protected]
--------------------------- Topic #7 -----------------------------------
From: Jean-Marc Lina, Atlantic Nuclear Services Ltd.
Subject: Preprint: Parameterizations for Daubechies wavelets
PARAMETERIZATIONS FOR DAUBECHIES WAVELETS
Jean-Marc LINA and Michel MAYRAND
Atlantic Nuclear Services Ltd. and
Laboratoire de Physique Nucl\'eaire, Universit\'e de Montr\'eal
(plain TeX, 10 pages, 3 figs)
Abstract
Two parameterizations are presented for the Daubechies wavelets.
The first one is based on the correspondence between the set of
multiresolution analysis with compact support orthonormal basis
and the group $SU_I(2,\complexe [z,z^{-1}])$ developped by Pollen.
In the second parameterization, emphasis is put on the regularity
condition of the Daubechies wavelets and a solitonic cellular
automata algorithm is introduced to solve the orthonormality
conditions characterizing the Daubechies wavelets.
send your request to [email protected]
--------------------------- Topic #8 -----------------------------------
From: Daniel Leung, Hong Kong University of Science & Technology
Subject : Looking For A Multiresolution Codebook
Hello,
I am looking for a multiresolution codebook for VQ of wavelet
coefficients of an image.
Many thanks in advance for any information
Email : [email protected]
--------------------------- Topic #9 -----------------------------------
Subject: Preprint: Wavelets on Closed Subsets of the Real Line
The following preprint is available:
Wavelets on Closed Subsets of the Real Line
Lars Andersson, Nathan Hall, Bjorn Jawerth and Gunnar Peters
Abstract:
We construct orthogonal and biorthogonal wavelets on a given closed
subset of the real line. We also study wavelets satisfying certain types of
boundary conditions. We introduce the concept of ``wavelet probing'',
which is closely related to our construction of wavelets. This
technique allows us to very quickly perform a number of different
numerical tasks associated with wavelets.
University of South Carolina, Industrial Mathematics Initiative,
Research Report 1993:2
To appear in "Topics in the Theory and Applications of Wavelets",
Larry L. Schumaker and Glenn Webb eds., Academic Press.
The postscript file is available through anonymous ftp to
maxwell.math.scarolina.edu, file /pub/imi_93/imi93_2.ps.
-------------------- End of Wavelet Digest -----------------------------
|
| 1409.26 | Wavelet Digest, Vol. 2, Nr. 8. | STAR::ABBASI | | Fri May 28 1993 16:17 | 453 |
| From: US2RMC::"[email protected]" "MAIL-11 Daemon" 28-MAY-1993 15:20:19.34
To: star::abbasi
CC:
Subj: Wavelet Digest, Vol. 2, Nr. 8.
Wavelet Digest Friday, May 28, 1993 Volume 2 : Issue 8
Today's Editor: Wim Sweldens
[email protected]
Today's Topics:
1. CFP: Electronic Transactions on Numerical Analysis
2. Preprint available: Biorthogonal Wavelets and Multigrid
3. Question: Wavelets in spherical coordinates
4. Question: PDE's and wavelets
5. Question regarding a proof in Daubechies' book
6. Preprint available: Dip Bounds for the 2D Wavelet Transform
7. Survey of wavelet routines in MATLAB
8. Preprint available: Wavelets for the fast solution of ODE's
9. Wavelet transform course announcement
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
To unsubscribe, e-mail with "unsubscribe" followed by your e-mail
address as subject. To change address, unsubscribe and resubscribe.
Archive site:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet/archive.
Gopher server: bigcheese.math.scarolina.edu.
Current number of subscribers: 2645
--------------------------- Topic #0 -----------------------------------
Notes from the editor:
1. We have been informed that some of the e-mail sent to the Wavelet Digest
was lost. If you have submitted something which did not appear,
please resend your message. We apologize for this inconvenience.
2. We are still working on the compilation of the "Wavelet Book Digest",
a summary of books on wavelets. Therefore we would like to ask you
again to send us:
- announcements of books which have not appeared earlier in the digest,
- reviews on wavelet books (or references to reviews),
- your impressions if you use or used any of these books in a course.
Thank you very much for your collaboration.
--------------------------- Topic #1 -----------------------------------
From: L. Reichel, Kent State University
Subject: CFP: Electronic Transactions on Numerical Analysis
Call for Papers
Electronic Transactions on Numerical Analysis
Scope:
Electronic Transactions on Numerical Analysis (ETNA) is an electronic journal
for the publication of significant new and important developments in numerical
analysis and scientific computing. Papers of the highest quality that deal with
the analysis of algorithms for the solution of continuous models and numerical
linear algebra are appropriate for ETNA, as are papers of similar quality that
discuss implementation and performance of such algorithms. New algorithms for
current or new computer architectures are appropriate provided that they are
numerically sound. However, the focus of the publication should be on the
algorithm rather than on the architecture. The journal is published by the Kent
State University Library in conjunction with the Institute of Computational
Mathematics at Kent State University. Mathematical Reviews will receive all
papers accepted for publication in the journal and review them as appropriate.
ETNA is registered with the Library of Congress and has ISSN 1068-9613.
Dissemination:
On a quarterly basis, accepted manuscripts will be posted in a directory which
is publicly accessible through Internet. The titles and abstract of these
manuscripts will be e-mailed to registered departments and individuals and
posted on public bulletin boards such as NA-digest. An individual who wishes
to obtain a copy of a current or back manuscript can get a copy through
anonymous FTP or by using a netlib-type mailer. We also plan to install
Gopher. All manuscripts will be available in Post Script format. The first
issue of ETNA will appear September 1, 1993. Funds made available by the
Kent State University Library and the Kent State University make free
subscription possible for at least three years. After this time period we
may have to charge an annual fee from institutional subscribers. Since the
operating costs for the journal are low, we envision that this fee will not
be above $100 for institutional subscribers. Everybody at the subscribing
institution will have access to ETNA by FTP, a netlib-type mailer or Gopher.
In addition, articles in ETNA can be obtained through interlibrary loan from
Kent State University Library.
To register to receive ETNA's quarterly titles and abstract lists, please send
an e-mail message to [email protected]. The subject of the message should be:
ETNA registration. Titles and abstracts of papers published in ETNA will be
e-mailed quarterly to the return addresses of all such requests. Inquiries for
further information should also be e-mailed to [email protected].
Submission, Acceptance and Refereeing:
Authors will normally submit papers for publication via e-mail, and they will
be required to submit their manuscript in LaTeX or TeX using macros we provide.
Requests for macros can be sent by e-mail to [email protected]. All papers will
be refereed. As soon as a paper has been accepted for publication in ETNA, it
will be entered into the ETNA data base. There are no annual page limitations,
and, therefore, we are in a position to publish accepted manuscripts faster
than many other journal. Manuscripts can be submitted NOW by sending them to
the address [email protected].
Current Editorial Board:
L. Reichel Kent State University
editor-in-chief [email protected]
R.S. Varga Kent State University
editor-in-chief [email protected]
A. Ruttan Kent State University
managing editor [email protected]
G.S. Ammar Northern Illinois University
J.W. Demmel University of California, Berkeley
J.J. Dongarra University of Tennessee
I.S. Duff Rutherford Appleton Laboratory
M. Eiermann University of Karlsruhe
J.A. George University of Waterloo
G.H. Golub Stanford University
W.B. Gragg Naval Postgraduate School
M.H. Gutknecht Swiss Federal Institute of Technology
V. Mehrmann Technical University of Chemnitz-Zwickau
D.C. Sorensen Rice University
G.W. Stewart University of Maryland
O.B. Widlund New York University
--------------------------- Topic #2 -----------------------------------
From: Angela Kunoth, Freie Universitat Berlin.
Subject: Preprint: Biorthogonal Wavelets and Multigrid
The following preprint is available:
Biorthogonal Wavelets and Multigrid
Stephan Dahlke and Angela Kunoth
Abstract:
We will be concerned with the solution of an elliptic boundary value
problem in one dimension with polynomial coefficients. In a Galerkin
approach, we employ biorthogonal wavelets adapted to a differential
operator with constant coefficients, and use the refinement equations
to set up the system of linear equations with exact entries (up to
round-off). For the solution of the linear equation, we construct
a biorthogonal two-grid method with intergrid operators stemming
from wavelet-type operators adapted to the problem.
Institut fuer Geometrie und Praktische Mathematik, RWTH Aachen,
Bericht Nr.84, April 1993.
To appear in: Proceedings of the 9th GAMM-Seminar "Adaptive Methods:
Algorithms, Theory and Applications", W.Hackbusch, G.Wittum (eds.),
NNFM series, Vieweg.
There is as well an extended version "A Biorthogonal Wavelet Approach
for Solving Boundary Value Problems",
Institut fuer Geometrie und Praktische Mathematik, RWTH Aachen,
Bericht Nr.85, April 1993.
Send your request to : [email protected]
or [email protected]
--------------------------- Topic #3 -----------------------------------
From: [email protected] (Mingui Sun)
Subject: Question: wavelets in spherical coordinates.
I am considering to compute the wavelet transform of
the electrical potentials on the surface of the head
which is modeled as a sphere. Are there any orthogonal or
non-orthogonal wavelet transforms defined in the polar (2-D) or
spherical (3-D) coordinates? Let me know please. Thanks.
Mingui Sun
[email protected]
--------------------------- Topic #4 -----------------------------------
From: Yuhong Yi, Climate System Research Program, Texas A&M University
Subject: Question: PDE's and wavelets
Hi, all netter,
I would like to get some information and references on solving the partial
differential equations(PDE) by using of wavelet transform.
Any help would be appreciated.
Thanks.
Yuhong Yi
Climate System Research Program
Texas A&M University
College Station, Tx 77843-3150
Tel: 409-845-0177 Fax: 409-862-4132
Email: [email protected] or [email protected]
--------------------------- Topic #5 -----------------------------------
From: Samir Chettri, Faculty of Technology, Kanazawa University, Japan
Subject: Question regarding a proof in Daubechies' book
Hello wavelet-folks:
Proposal: Why don't we change the wavelet digest name to WAVELETTERS.
Now that we have gotten the jokes out of the way, on to more serious
matters. I'm reading Daubechies book 10 LECTURES ON WAVELETS.
On page 153-154 is a proof of
~
int[ dx x^^l f(x) ] = 0 for l = 0,1, ...., m
(i.e., regularity of wavelets).
I've followed the proof except for one part of equation 5.5.4 (page 154)
The second term of 5.5.4 is written as
/
' | l ~ l
C | dy (1 + |y| ) | f (2 y) |
|
/
|y|>delta
The first and second term of 5.5.4 (only the second term is written above)
is obtained from the second term of 5.5.2. I understand how the first
term of 5.5.4 was obtained. However it is not clear to me how the
second term (as written above) was obtained.
Could someone kindly help me.
Sincerely,
Samir Chettri
[email protected]
Note from the editor:
Thank you very much for your suggestion. Actually, the name "Waveletter"
has been suggested to us by several people already and personally I feel
it is a much better name then "Wavelet Digest". We are currently considering
to restructure the setup of the digest and this will most likely be one of
the changes.
--------------------------- Topic #6 -----------------------------------
From: Jack K. Cohen, University of Colorado
Subject: Preprint available: Dip Bounds for the 2D Wavelet Transform
Quantitative Dip Bounds for the Two-Dimensional Discrete Wavelet Transform
Jack K. Cohen and Tong Chen
Abstract:
An analysis of the discrete wavelet transform of dipping segments
with a signal of given frequency band leads to a \Em{quantitative}
explanation of the known division of the two-dimensional wavelet
transform into horizontal, vertical and diagonal emphasis panels.
The results must be understood in a ``fuzzy'' sense: since wavelet
mirror filters overlap, the results stated can be slightly violated
with violation tending to increase with shortness of the wavelet
chosen.
The specific angles that delimit the three wavelet panels can be
stated in terms of the Nyquist frequency $F_t$ in the first dimension
and the Nyquist frequency $F_x$ in the second dimension. The angle
$\theta_{HV}=\arctan(F_t/F_x)$ approximately separates the dips
$\theta<\theta_{HV}$ that appear in the horizontal panel from those
greater dips that appear in the vertical panel. Similarly, the
angles $\arctan(F_t/2F_x)$, $\arctan(2F_t/F_x)$ approximately bound
the dips appearing in the diagonal panel. These results are simple
and probably have been observed by other researchers, but we haven't
found a prior reference for them.
The entire paper is available from our anonymous ftp site: 138.67.12.63,
in the directory pub/papers/wavelets.
There are 2 versions Dip300dpi.ps.Z and Dip400dpi.ps.Z to
accommodate different printing devices.
Jack K. Cohen,
University of Colorado
[email protected]
--------------------------- Topic #7 -----------------------------------
From: Marten D. van der Laan, University of Groningen, The Netherlands
Subject: Survey of wavelet routines in MATLAB
SURVEY of wavelet routines in MATLAB
Via Usenet-News I asked for Matlab-routines for wavelet analysis. I got some
responses which are, in my opinion, interesting for the digest-readers:
#1 From: Serge J. Belongie ([email protected])
I have put together and debugged some m-files to implement
the forward and inverse DWT with DAUB4 coefficients. The
input data has to be a power of 2. The 1-D version seems
to be working fine, though the 2-D version still has some minor
problems to be worked out. These m-files are very elementary
as far as wavelet analysis goes, and are most likely a tiny
subset of the toolbox you used, but I thought I'd mention it
to you anyway since they are a simple introduction to a really
neat concept.
#2 From: Srinivas Palavajjhala ([email protected])
The following are some programs I have written in MATLAB.
wtrans - does wavelet packet decomposition and/or decomp. and
reconstruction at each scale using Daubechies Wavelets and
Coifman Wavelets.
mra - does the multiscale plot.
reconph - is reconstruction of high freq part.
reconpl - is reconstruction of low freq part.
#3 From: Marten van der Laan ([email protected])
I use a toolbox written by J.C. Kantor ([email protected]). This toolbox
contains routines for the Fast Wavelet Transform (forward and inverse) using
Daubechies wavelets, a routine for iterating the scaling function \phi and
wavelet \psi, and routines for signal compression using wavelets. This
toolbox has a full TeX documentation.
I tried to contact the author, but I didn't succeed. Anyone who is
interested can contact me.
Myself, I wrote some routines for plotting the wavelet-transformed data in the
time-frequency plane. These routines require Matlab 4.0! I'm currently
fixing some bugs and improving some other things. Again, if you are
interested, contact me.
#4 From: Neil Getz ([email protected])
Neil has written a paper:
"A Fast Discrete Periodic Wavelet Transform"
Memorandum N0. UCB/ERL M92/138
Abstract:
We extend the discrete wavelet transform (DWT) to functions on
the discrete circle to create a fast and complete discrete periodic
wavelet transform (DPWT) for bounded periodic sequences. In so doing
we also solve the problem of non-invertibility that arises in the
application of the DWT to finite dimensional sequences as well as provide
the proper theoretical setting for previous incomplete solutions to the
invertibility problem. We show how and prove that the same filter
coefficients used with the DWT to create orthonormal wavelets on compact
support in l^inf(Z) may be incorporated through the DPWT to create an
orthonormal basis of discrete periodic wavelets. By exploiting transform
symmetry and periodicity we arrive at easily implementable, fast, and
recursive synthesis and analysis algorithms. We include Matlab functions
for DPWT experimentation.
This paper includes MATLAB routines. Please contact the author.
NOTE
I compared the different implementations 1D-discrete wavelet transforms
by Belongie (#1), Kantor (#3) and Getz (#4). Except for some differences in
the lowest frequency components the transformed data were similar.
Also the reconstruction errors (wavelet transform followed by the inverse
wavelet transform) were similar in all three cases.
I only did a very small test with sine input and white noise input, 512
samples. Reconstruction error was of order e-14.
If anyone has some related information, please drop me a message.
Marten D. van der Laan Email: [email protected]
Dept. of Computing Science
University of Groningen
P.O. Box 800
9700 AV GRONINGEN
The Netherlands
--------------------------- Topic #8 -----------------------------------
From: Wim Sweldens, University of South Carolina
Subject: Preprint available: Wavelets for the fast solution of ODE's.
Title: Wavelet multiresolution analyses adapted for the fast solution of
boundary value ordinary differential equations
Authors: Bjorn Jawerth and Wim Sweldens
Abstract:
We present ideas on how to use wavelets in the solution
of boundary value ordinary differential equations. Rather than
using classical wavelets, we adapt their construction so that
they become (bi)orthogonal with respect to the inner
product defined by the operator. The stiffness matrix in
a Galerkin method then becomes diagonal and can thus be
trivially inverted. We show how one can construct an
O(N) algorithm for various constant and variable coefficient
operators.
Status: Industrial Mathematics Initiative, Report 1993:3
To appear the in proceedings of the sixth Copper Mountain
Multigrid Conference, April 1993.
The postscript or tex (without figures) file of this paper can be
retrieved by using anonymous ftp to maxwell.math.scarolina.edu,
directory /pub/imi_93, files imi93_3.tex or imi93_3.ps.
--------------------------- Topic #9 -----------------------------------
From: Lynn Orlando Lynn, UCLA
Subject: Wavelet transform course announcement
UCLA Extension announces a new short course
Wavelet Transform: Techniques and Applications
August 9-11, 1993 at UCLA
For many years, the Fourier Transform (FT) has been used in a wide
variety of application areas, including multimedia compression of
wideband ISDN for teleRcommunications; lossless transform for
fingerprint storage, identification, and retrieval; an increased
S/N ratio for target discrimination in oil prospect seismic
imaging; inRscale and rotationRinvariant pattern recognition in
automatic target recognition; and inRheart, tumor, and biomedical
research.
This course describes a new technique, the Wavelet Transform (WT),
that is replacing the windowed FT in the applications mentioned
above. The WT uses appropriately matched bandpass kernels, called
mother wavelets, thereby enabling improved representation and
analysis of wideband, transient, and noisy signals.
This course is taught by Harold Szu, Research Physicist and
President of INNS, and John D. Villasenor of UCLA's School of
Engineering and Applied Science.
To request a brochure, call (310) 825-1047; FAX (310) 206-2815; or
send your external mailing address to:
[email protected]
Please forward this message to interested colleagues.
-------------------- End of Wavelet Digest -----------------------------
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% Subject: Wavelet Digest, Vol. 2, Nr. 8.
% From: [email protected] (Wavelet Digest)
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|
| 1409.27 | Wavelet Digest, Vol. 2, Nr. 14. | STAR::ABBASI | white 1.e4 !! | Tue Oct 05 1993 13:08 | 621 |
| Wavelet Digest Tuesday, October 5, 1993 Volume 2 : Issue 14
Today's Editor: Wim Sweldens
[email protected]
Today's Topics:
1. Electronic Journal of Differential Equations (EJDE)
2. Mathematica wavelet packets programs.
3. Wavelet Papers and MATLAB Software from Rice University.
4. Windows 3.1 implementation of the wavelet fingerprint compression.
5. Questions
6. Preprint available: Wavelet Function, Scaling function and DWT
7. Question: Wavelets on closed surfaces in 3D Euclidean Space?
8. ACHA: A new wavelet journal, first issue.
9. Question: Calculating the coefficients for the Chui wavelets?
10. CFP: "Time-frequency, Wavelets and Multiresolution", Lyon, France
11. Preprints available
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
To unsubscribe, e-mail with "unsubscribe" followed by your e-mail
address as subject. To change address, unsubscribe and resubscribe.
Archive site, preprints, references and back issues:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directories /pub/wavelet and /pub/imi_93.
Gopher and Xmosaic server: bigcheese.math.scarolina.edu.
Current number of subscribers: 2887
--------------------------- Topic #1 -----------------------------------
From: Julio G. Dix, Southwest Texas State University.
Subject: Electronic Journal of Differential Equations (EJDE)
ELECTRONIC JOURNAL OF DIFFERENTIAL EQUATIONS (EJDE)
Mathematicians at Southwest Texas State University and
at the University of North Texas have collaborated to
establish a new journal, the ELECTRONIC JOURNAL OF
DIFFERENTIAL EQUATIONS (EJDE). The EJDE will be a strictly
electronic journal dealing with all aspects of differential
equations. Articles will be submitted as TeX files, sent to
referees electronically, and then disseminated electronically,
free of charge.
Although the time between submission and dissemination
will be greatly reduced, only original research of high
quality will be accepted. Each article will be subject to as
rigid a peer review process as is applied by the finest of
today's printed journals. The EJDE is calling for papers
now. There are no page charges.
The EJDE can be accessed via ftp (login: ftp), gopher, and
telnet (login: ejde) to "ejde.math.swt.edu" or to
"ejde.math.unt.edu". Examples illustrating these options are:
1. "telnet ejde.math.swt.edu", login: "ejde" . (It may be
necessary to set your terminal to emulate a VT100.)
2. "telnet e-math.ams.com", login: "e-math", password: "e-math",
select "Mathematical Publications", then "Other Mathematical
Publications", and then "Electronic Journal of Differential
Equations".
3. "ftp ejde.math.swt.edu", login: "ftp", and "cd pub".
4. Provided that the gopher-client software is loaded on the
reader's computer."gopher ejde.math.unt.edu".
Readers can transfer the TeX and Postscript files to
their own computers and then read them or print hard copies.
A free subscription to the abstracts of new articles in
the EJDE is available by sending an e-mail message to
"[email protected]". Suggestions and comments should be sent
to "[email protected]" or to "[email protected]".
Identical copies of the EJDE will be originated and
maintained at Southwest Texas State University and at the
University of North Texas. For posterity and for interlibrary
loan, a hard copy exists in the libraries at both institutions.
The Managing Editors of EJDE are Alfonso Castro, Julio
Dix, Gregory Passty, and Ricardo Torrejon.
The Editorial Board consists of
P. Bates (Brigham Young University), A. Bloch (Ohio State University),
J. Bona (Pennsylvania State University), K. J. Brown (Heriot-Watt University),
L. Caffarelli (Institute for Advanced Study), C. Castillo-Chavez (Cornell),
C. Chui (Texas A & M University), M. Crandall (University of California at
Santa Barbara), E. Di Benedetto (Northwestern University), G. B. Ermentrout
(University of Pittsburgh), J. Escobar (Indiana University), L. C. Evans
(University of California at Berkeley), J. Goldstein (Louisiana State
University), C. Groetsch (University of Cincinnati), I. Herbst (University
of Virginia), C. Kenig (University of Chicago), R. Kohn (Courant Institute),
A. Lazer (Miami University), J. Neuberger (University of North Texas),
P. H. Rabinowitz (University of Wisconsin), R. Shivaji (Mississippi State
University), R. Showalter (University of Texas), H. Smith (Arizona State
University), P. Souganidis (University of Wisconsin), N. Walkington
(Carnegie-Mellon University),
--------------------------- Topic #2 -----------------------------------
From: Mladen Victor Wickerhauser, Washington University.
Subject: Mathematica wavelet packets programs.
Here are some old Mathematica programs to generate individual wavelet packets
and to calculate inner products with individual wavelet packets. The one
example I have provided is intended to be a template: change the relevant
parameters, or the sequence of filters, to get any wavelet packet you wish.
I hereby put this code into the public domain.
Cheers, Victor
Professor Mladen Victor Wickerhauser <[email protected]>
Department of Mathematics, Campus Box 1146, One Brookings Drive,
Washington University in Saint Louis, Missouri 63130 USA
Telephone: USA+(314)935-6771; Facsimile: USA+(314)935-5799
(* ----------------->8 Cut here 8<---------------------*)
(* Wave Packets and Modulated Pulses *)
(* M. Victor Wickerhauser *)
(* 21 May 1990 *)
(* FILTER COEFFICIENTS *)
(* We start with some standard conjugate quadrature filters: *)
stdH[10] = {0.160102397974, 0.603829269797, 0.724308528438,
0.138428145901, -0.242294887066, -0.032244869585,
0.077571493840, -0.006241490213, -0.012580751999,
0.003335725285};
std[12] = { 0.111540743350, 0.494623890398, 0.751133908021, 0.315250351709,
-0.226264693965, -0.129766867567, 0.097501605587, 0.027522865530,
-0.031582039318, 0.000553842201, 0.004777257511, -0.001077301085};
coif[12] = {1.15875967387, -.02932013798, -.04763959031,
.273021046535, .574682393857, .294867193696, -.0540856070917,
-.0420264804608, .0167444101633, .00396788361296,-.00128920335614,
-.000509505399};
sr15 = Sqrt[15.0];
coif[6] = {(sr15-3.0)/32.0,(1.0-sr15)/32.0,(3.0-sr15)/16.0,(sr15+3.0)/16.0,
(sr15+13.0)/32.0,(9.0-sr15)/32.0};
mirrorFilter[ filter_ ] :=
Block[{len},
len = Length[filter];
Table[ (-1)^n filter[[len+1-n]], {n,1,len}]
]
(* CONVOLUTION AND DECIMATION *)
(* Define the convolution decimation operators convolveDecimate[] as a
function of two arguments: filter list, and vector list: *)
convolveDecimate[filter_, vector_]:=
Block[{vectorLength,filterRange},
vectorLength = Length[vector];
filterRange = Length[filter];
Table[
Sum[ filter[[j]] vector[[Mod[2i+j-3,vectorLength]+1]],
{i,1,filterRange}
],
{i,1,vectorLength/2}
]
]
(* The adjoint of this operation is similarly defined: *)
antiConvolve[filter_, vector_]:=
Block[{tempVec, newLength, filterRange, vectorLength},
vectorLength = Length[vector];
filterRange = Length[filter];
newLength = 2 vectorLength;
tempVec = Table[0,{i,1, newLength}]; (* initialize output *)
Do[
tempVec[[ Mod[2j+i-3,newLength]+1 ]] += vector[[j]] filter[[i]],
{j,1,vectorLength},
{i,1,filterRange}
];
tempVec
]
(* MAKING WAVELETS AND WAVELET PACKETS *)
(* Now we can anticonvolve a wavelet of length 512: *)
H = coif[6]; G = mirrorFilter[H];
w1 = antiConvolve[ H,
antiConvolve[ H,
antiConvolve[ H,
antiConvolve[ H,
antiConvolve[ H,
antiConvolve[ G,{1,0,0,0,0,0,0,0}]
]
]
]
]
];
ListPlot[w1,PlotRange->{-.05,.05},PlotJoined->True]
(* Wavelet packets are similarly made with other sequences of filters: *)
wp1 = antiConvolve[ H,
antiConvolve[ G,
antiConvolve[ H,
antiConvolve[ G,
antiConvolve[ G,
antiConvolve[ G,{1,0,0,0,0,0,0,0}]
]
]
]
]
];
ListPlot[wp1,PlotRange->{-.05,.05},PlotJoined->True]
--------------------------- Topic #3 -----------------------------------
From: Ramesh Gopinath <[email protected]>
Subject: Wavelet Papers and MATLAB Software from Rice University.
Wavelet Papers and MATLAB Software from Comp. Math. Lab, Rice University.
Announcing the availability of rice-wlet-tools-1.1, a collection of MATLAB
"mfiles" and "mex" files for twoband and M-band filter bank/wavelet analysis
from the DSP group and Computational Mathematics Laboratory (CML) at Rice
University, Houston, TX.
The programs have been tested on Sparcstations running SUNOS with MATLAB 4.1.
However, the "mex" code is generic and should run on other platforms (you may
have to tinker the Makefiles a little bit to make this work). There are
several utility routines all of them callable from matlab. All the C files
(leading to the mex files) can also be directly accessed from other C or
Fortran code. A collection of of papers and tech. reports from the DSP group
(to be expanded soon to all CML reports) is also available.
You could obtain this distribution of software and papers by anonymous ftp
from cml.rice.edu OR by telnet from dsp.rice.edu or cml.rice.edu. Telnet
should be preferred since it would to insulate the user from any future
system changes at cml or dsp.
1. ANONYMOUS FTP: cml.rice.edu (128.42.62.23)
In directories /pub/dsp/software and /pub/dsp/papers
2. TELNET: dsp.rice.edu (128.42.4.62) or cml.rice.edu (128.42.62.23)
This method of access automatically installs all the files and compiles them
so that you are ready to go.
%telnet dsp.rice.edu 5555 | sed '1,3d' | csh -fsb OPTIONS
OPTIONS is a list of options. You may (for convenience) want to add
alias riceget "telnet dsp.rice.edu 5555 | sed '1,3d' | csh -fsb"
to you .cshrc file so that you may access the software as follows:
%riceget software
for software
%riceget papers
for all papers from the group (as unix compressed postscript files), etc.
%riceget help
would give you a list of available OPTIONS
Report problems/bugs and installation info on non-SUN/non-unix platforms
send mail to [email protected] (or [email protected])
ramesh gopinath
dept. of ece., rice university, houston, tx-77251-1892
tel. off.(713)-527-8750 x3569 (or) x3508
tel. home. (713)-794-0274
--------------------------- Topic #4 -----------------------------------
From: Mladen Victor Wickerhauser, Washington University.
Subject: Windows 3.1 implementation of the wavelet fingerprint compression.
This is to announce a free Windows 3.1 implementation of the wavelet-based
FBI standard fingerprint compression algorithm. It can be obtained by
anonymous ftp from:
wuarchive.wustl.edu,
in the directory:
doc/techreports/wustl.edu/math/software
as the file:
wsqwin.zip
This is an archive containing the program and a read.me file which is
partially reproduced below. Use PKUNZIP to unpack the archive once you
download it.
software/wsqwin.zip
TI: WSQ -- the FBI/Yale/Los Alamos [W]avelet-packet [S]calar [Q]uantization
fingerprint compression algorithm, for Windows 3.1 or higher.
AU: He Ouyang and M. Victor Wickerhauser
IN: Washington University in St. Louis
SO: Executable is available by anonymous ftp only
ST: Public software
AB: This is an implementation of the WSQ algorithm as described in ``IAFIS
Use of the Wavelet Scalar Quantization (WSQ) Compression/Decompression
Algorithm,'' J. J. Werner, 8 December 1992. The implementation is designed
for well-equipped IBM-type desktop computers using the Windows 3.1 operating
system. It reads .BMP files and writes standard-format compressed files, and
vice versa. Results are displayable on a high-resolution video monitor. The
quality factor can be set under menu control. Computations are performed in
integer arithmetic -- no floating-point coprocessor is needed.
DE: fingerprints, wavelets, compression, decompression
SC:
MA: wuarchive.wustl.edu
FN: doc/techreports/wustl.edu/math/software/wsqwin.zip
TL: ZIP archive containing executable WSQWIN.EXE and READ.ME
DA: 8 September 1993
Professor Mladen Victor Wickerhauser <[email protected]>
Department of Mathematics, Campus Box 1146, One Brookings Drive,
Washington University in Saint Louis, Missouri 63130 USA
Telephone: USA+(314)935-6771; Facsimile: USA+(314)935-5799
--------------------------- Topic #5 -----------------------------------
From: Vinod Raghavan, School of Chemical Engineering, Oklahoma State University
Subject: Questions
Dear Waveletters,
I need lots of help. First I want to know how to pad a finite length
signal before the decomposition. I have tried the circular convolution
technique and for the Daubechies family of wavelets, it works fine. The
problem creeps up when I am dealing with a signal like a step function.
The decomposition coefficients come out real wierd, more so when I go
down a number of levels. But, if I repeat the same padding procedure
while reconstructing it, I get perfect reconstructions.
My problems is that I am using the decomposition coefficients for Pattern
Recognition purposes and can't afford to get poor coefficients. Perfect
reconstruction is secondary to me , but is important. I tried padding it
using Mallat's procedure in " A theory for multi-resolution signal
decomposition: a wavelet representation" in IEEE Transactions on Pattern
Recognition and Machine Intelligence,Vol.11, No.7 July 1989, but then
the decomposition somehow screws up. Does any one else have this same
problem?
What kind of padding should I use to avoid poor coefficients and
still get perfect reconstruction?
I think this is a critical issue, but none of the articles I have read talk
about this. Have I missed any article?
My second problem: Is there any article in English that deals with the
construction of the Meyer,Battle-Lemarie and other wavelets(other than the
Daubechies and the Biorthogonal wavelets developed by Daubechies) explicitly?
Is there any article where the coefficients for these wavelets are tabulated
for some orders so that I can compare my values with these. As far as I
know only Daubechies has done that. That benefits non-mathematicians like me
a lot.
I would appreciate any help.
Vinod Raghavan.
423 Engineering North, School of Chemical Engineering
Oklahoma State University, Stillwater,OK 74078 USA
Email:[email protected]
--------------------------- Topic #6 -----------------------------------
From: Qu Jin, McMaster University, Canada.
Subject: Preprint available: Wavelet Function, Scaling function and DWT
WAVELET FUNCTION, SCALING FUNCTION AND DISCRETE WAVELET TRANSFORM
Q.Jin, Z.Q.Luo and K.M.Wong
Department of Electrical and Computer Engineering
McMaster University, Hamilton, Ont. Canada L8S 4L7
ABSTRACT:
In this paper, we discuss the very general condition for a function to be a
scaling function and a wavelet function. The necessary and sufficient condition
for the functions to form a multiresolution wavelet decomposition and
reconstruction is presented. Through the investigation of
multiresolution analysis and filter bank theory, the interrelationship between
continuous wavelet transform and discrete wavelet transform is established.
A copy of this paper is available by send your address to
[email protected]
or write to Dr. Qu Jin of the address above.
--------------------------- Topic #7 -----------------------------------
From: Dr. Roland Klees, GeoForschungsZentrum, Potsdam, Germany
Subject: Question: Wavelets on closed surfaces in 3D Euclidean Space?
Dr. Roland Klees
GeoForschungsZentrum (GFZ)
P.O.Box 600751
14407 Potsdam, Germany
Phone: ++49 331-310-243, Fax: ++49 331-310-648
e-mail: [email protected]
Question: wavelets on closed surfaces in three-dimensional Euclidean Space?
I look for literature on
1. wavelets on closed surfaces ...
2. solution of boundary integral equations (2D) using wavelets
3. wavelet transform for fast solution of linear systems
4. time series analysis with wavelets
Many Thanks
Roland Klees
--------------------------- Topic #8 -----------------------------------
From: Charles Chui, Texas A&M University.
Subject: ACHA: A new wavelet journal, first issue.
ANNOUNCEMENT: A new wavelet journal.
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS
Time-Frequency and Time-Scale Analysis, Wavelets,
Numerical Algorithms, and Applications (ACHA)
The first issue will appear in Nov. - Dec. 1993. This interdisciplinary
journal is published by Academic Press, Inc. with Charles K. Chui,
Ronald Coifman, and Ingrid Daubechies as editors-in-chief. The other
editors of this journal are: David Donoho, Alexander Grossmann,
Wolfgang Hackbusch, Stephane G. Mallat, Yves F. Meyer, Vladimir
Rokhlin, Alan S. Willsky, Alain Arneodo, Pascal Auscher, Guy Battle,
Gregory Beylkin, Albert Cohen, Wolfgang Dahmen, Marie Farge,
Patrick Flandrin, Leslie F. Greengard, Moshe Israeli, Stephane Jaffard,
Bjorn Jawerth, Iain M. Johnstone, Pierre Gills Lemarie-Rieusset,
W.R. Madych, Charles A. Micchelli, Xianliang Shi, Ph. Tachmichian,
Bruno Torresani, P. P. Vaidyanathan, Martin Vetterli,
Jianzhong Wang, Guido L. Weiss, and M. Victor Wickerhauser.
SUBSCRIPTION RATES:
Institutional Personal
In the U.S.A. and Canada: $184 $74
All other countries: $221 $93
ADDRESS: Academic Press, Inc. 1250 Sixth Ave, San Diego, CA 92101, U.S.A.
AUTHOR INFORMATION:
ACHA is an interdisciplinary journal. It publishes high-quality
papers in all areas related to the applied and computational aspects
of harmonic analysis in a broad sense: theoretical mathematical
developments which are relevant to applications, physics papers which
provide some type of the "new" harmonic analysis tools, electrical
engineering ideas, applied mathematics applications: all are welcome,
and the list is far from exhaustive.
Authors should bear in mind the interdisciplinary nature of the journal
while staying true to their own field and background. In order to reach as
wide an audience as possible, each author is asked to provide a paragraph
(or two) describing, in more general and clear terms, the goal of the paper.
Original papers only will be considered.
Submission of manuscripts. Manuscripts should be written in clear, concise,
and grammatically correct English and should be submitted in quadruplicate (one
original and three photocopies), including four sets of original figures or
good-quality glossy prints, to:
Applied and Computational Harmonic Analysis
Editorial Office
1250 Sixth Ave., 3rd Floor
San Diego, CA 92101
Detailed information for authors can be obtained from this same address.
TABLE OF CONTENTS of Vol. 1 No. 1:
A Wavelet Auditory Model and Data - John J. Benedetto and Anthony Teolis
Bessel Sequences and Affine Frames - Charles K. Chui and Xianliang Shi
Wavelets on the Interval and Fast Wavelet Transforms - Albert Cohen,
Ingrid Daubechies, and Pierre Vial
Unconditional Bases are Optimal Bases
for Data Compression and for Statistical Estimation - David L. Donoho
Diagonal Forms of Translation Operators for the Helmholtz Equation
in Three Dimensions - V. Rokhlin
Fast Numerical Computations of Oscillatory Integrals Related to
Acoustic Scattering I - B. Bradie, R. Coifman, and A. Grossmann
This is the first issue of the 1993-94 volume, with three issues to be
published in 1994. Starting 1995, the journal will publish 4 issues per year.
ABSTRACTS of the above papers can be obtained via ftp in the following
steps:
a) ftp wavelet1.math.tamu.edu
b) USER: achasite
c) password: abstract
d) mget acha11.abstract
--------------------------- Topic #9 -----------------------------------
From: Vinod Raghavan, School of Chemical Engineering, Oklahoma State University
Subject: Question: Calculating the coefficients for the Chui wavelets?
Could some one tell me how to calculate the reconstruction coefficients for
the Chui family of wavelets?
I was able to calculate the scaling function and wavelet coefficients for
that family, but in the computation of the reconstruction coefficients,
there is an infinite length polynomial that has to be dealt with. Infinite
sequences are something I not comfortable with.
I am looking forward to some enlightening mails from math gurus around...
Vinod Raghavan
School of Chemical Engineering
Oklahoma State University
Stillwater,OK 74078
[email protected] Wed Sep 22 08:41:18 1993
--------------------------- Topic #10 -----------------------------------
From: [email protected]
Subject: CFP: "Time-frequency, Wavelets and Multiresolution", Lyon, France
"Time-frequency, Wavelets and Multiresolution :
Theory, Models and Applications"
March 9-11, 1994, Lyon, FRANCE.
Announcement and Call for Papers
The French Group of Research in Signal and Image Processing (GdR
TDSI) of CNRS (National Center for Scientific Research) organizes two
"Thematic days" devoted to "Time-frequency, wavelets and multiresolution".
During these days (March 9-10, 1994), invited speakers will present
tutorials on both the theoretical basis and the state of the art in the
domain. The conferences will mainly be presented in French.
A workshop (one day and a half) with contributed papers will
accompany this event. Papers concerning both theoretical aspects or
applications showing the specific interest of these methods are equally
welcome. Particular emphasis will be focused on the convergence of
different approaches : signal / image, time-frequency / time-scale,
wavelets / filter banks, pyramids / multiresolution...
Papers for the workshop can be written and presented either in
French or in English. In any case, the full text of the papers will be
published in the Proceedings and offered to each participant. The number of
participants will be limited.
Papers will be selected by the Program Committee on the basis of a
2-page abstract.
Chairman :
R. Goutte, INSA Lyon, FRANCE
Program Committee :
F. Peyrin, R. Prost, A. Baskurt, P. Flandrin, J.M. Chassery, M. Barlaud
Prelminary list of invited speakers:
J.P. Antoine, M. Barlaud, A. Cohen, P. Flandrin, F. Hlawatsch, M. Kunt, M.
Unser...
Working Calendar :
Deadline for submission of abstracts : October 31, 1993
Selection and replies to authors : December 1st, 1993
Receipt of full papers: January 15, 1994
Registration fees:
Member GdR TDSI Non-Member
Workshop 500 FF 500 FF
Thematic + Workshop 500 FF 800 FF
Days
For further informations, please contact :
Workshop TOM (Time-frequency, Wavelets and Multiresolution)
URA CNRS 1216, Bat 502, INSA Lyon
69621 Villeurbanne Cedex, FRANCE
Tel : (33) 72 43 82 27 Fax : (33) 72 43 85 26
e-mail : [email protected]
--------------------------- Topic #11 -----------------------------------
From: Andreas Rieder, Computational Mathematics Laboratory, Rice University.
Subject: Preprints available
The following preprints are available:
1) A WAVELET MULTILEVEL METHOD
FOR DIRICHLET BOUNDARY VALUE PROBLEMS
IN GENERAL DOMAINS
R. Glowinski, A. Rieder, R.O. Wells, X. Zhou
Abstract:
We present a multilevel method for the efficient solution
of the linear system arising from a Wavelet-Galerkin
discretization of a Dirichlet boundary value problem via
a penalty/fictitious domain formulation. The presence of
the penalty term requires a modified coarse grid correction
process in order to guarantee a convergence rate which is
independent of the discretization step size.
Numerical experiments described in the paper confirm the
theoretical results.
Key words: wavelets, multilevel methods, penalty/fictitious
domain formulation, Galerkin methods
Subject classification: AMS(MOS) 65F10, 65N30
Technical Report of the Computational Mathematics Laboratory,
No. CML TR93-06, Rice University, Houston, 1993
2) A WAVELET APPROACH TO ROBUST MULTILEVEL SOLVERS
FOR ANISOTROPIC ELLIPTIC PROBLEMS
A. Rieder, R.O. Wells, X. Zhou
Abstract:
A wavelet variation of the "Frequency decomposition multigrid
method, Part I" (FDMGM) of Hackbusch [Numer. Math. 56, pp.229-245,
1989] is presented.
Our modification allows a deeper analysis of this method. Indeed,
the orthogonality and the multiresolution structure of wavelets
yield the robustness of the additive as well as of the multiplicative
version of the FDMGM relative to any intermediate level. Aspects of
robustness of the multilevel scheme are discussed. Numerical
experiments confirm the theoretical results.
The wavelet version of the FDMGM presented here involves wavelet
packets which have been used before this primarily in signal
processing. As a by-product of our analysis we yield strong Cauchy
inequalities for the wavelet packet spaces with respect to
H^1-inner products.
Key words: wavelets, wavelet packets, robust multilevel methods,
anisotropic problems, Galerkin methods
Subject classification: AMS(MOS) 65F10, 65N30
Technical Report of the Computational Mathematics Laboratory,
No. CML TR93-07, Rice University, Houston, 1993
Compressed postscript copies of these reports are available from
the ftp site cml.rice.edu (128.42.62.23). The files are pub/reports/9306.ps.Z
(first report) or pub/reports/9307.ps.Z (second report).
(login: anonymous, password: your email-address)
Andreas Rieder, Computational Mathematics Laboratory, Rice University,
Houston, Texas 77251, USA
email: [email protected]
|
| 1409.28 | Wavelet Digest Volume 2 : Issue 15 | STAR::ABBASI | only 57 days to graduate! | Wed Oct 20 1993 02:06 | 442 |
| Wavelet Digest Wednesday, October 20, 1993 Volume 2 : Issue 15
Today's Editor: Wim Sweldens
[email protected]
Today's Topics:
1. Question: matrix multiplication
2. Short course: Wavelets for Engineering Applications.
3. Need help finding wavelet algorithms.
4. Question: Wavelets to divide 2D freq. space in polar coordinates ?
5. Contents: Advances in Computational Mathematics
6. Preprint available
7. Preprints available
8. Reply: Question Coeff. for the Chui wavelets, WD 2.14 # 9.
9. Mathematica wavelet programs available.
10. New Wavelet Toolbox in Matlab
11. Workshop: Wavelets in Chemical Engineering
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
To unsubscribe, e-mail with "unsubscribe" followed by your e-mail
address as subject. To change address, unsubscribe and resubscribe.
Archive site, preprints, references and back issues:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directories /pub/wavelet and /pub/imi_93.
Gopher and Xmosaic server: bigcheese.math.scarolina.edu.
Current number of subscribers: 2975
--------------------------- Topic #0 -----------------------------------
From: Editor
Subject: Retrieving old issues & new gopher feature
Some problems occurred during the sending of the previous
digest (WD 2.14). Please accept our apologies in case you did not
receive this issue. You can always retrieve it using anonymous ftp
or gopher (cf supra).
Our gopher server also has a new feature that will facilitate searching
through old issues. If you choose the first item from the wavelet
digest information, you will be prompted for a keyword. The server
will then retrieve items (nicely cut out from previous digests)
which are relevant to this keyword. The resulting listing will be
ordered on the basis of keyword frequency and other factors, so
ideally the more relevant articles will appear near the beginning
of the list.
Try it out with keywords such as "fingerprint" or "matlab". Also, more
complex combinations of keywords may provide worthwhile results.
Special thanks go to Brygg Ullmer, a computer science major from the
University of Illinois in Urbana Champaign, who maintains the gopher
server and was able to add this new feature in only a few minutes.
--------------------------- Topic #1 -----------------------------------
From: Jan Pajchel
Subject: Question: matrix multiplication
I should be very much obliged if you could help me with the
following problem:
Consider the computation of a sequence of matrices
Dr=Ar*Br
where Ar,Br are given complex matrices, assumed to be dense and
of large dimension /4094*4096/. They represent the discrete signal
response to an isolated point target in the image.
Can wavelets be used for:
- compression, multiplication and finally decompression ?
- is any software available to try this on real data ?
Sincerely yours:
Jan Pajchel
[email protected]
--------------------------- Topic #2 -----------------------------------
From: Marilyn Grossman, Texas A&M University
Subject: Short course: Wavelets for Engineering Applications.
"Wavelets for Engineering Applications," January 12-15, Texas A&M
University System in College Station, Texas.
Applications-oriented short course featuring Charles K. Chui, author
of the popular book, "An Introduction to Wavelets," and two other
experts of the wavelets research team at Texas A&M University, A.K. Chan
(electrical engineer) and P.K. Yuen (computer scientist). Designed to
build on your basic understanding of wavelet theory, this course will
take you through the concept of time-frequency analysis, the
significance of the integral wavelet transform, and real-time
preprocessing of a continuous signal using cardinal splines. You will
learn how to construct wavelets, how to use decomposition and
reconstruction algorithms, and how to use wavelet packets for frequency
domain fine-tuning in our hands-on laboratory sessions.
409/862-4615 or email: [email protected]
--------------------------- Topic #3 -----------------------------------
From: Ken Tew <[email protected]>
Subject: Need help finding wavelet algorithms.
In particular I need to get information on wavelet algorithms
that handle large data sets. Currently, the algorithms I've seen
transform an input vector into an equal sized vector of wavelet
coefficients. What I need to have is a matrix of wavelet
coefficients where the columns are wavelet coeffients of sub-vectors of
the input vector. For example, given a vector representing a MINUTE
of data; the output would be a matrix where each column contains the
wavelet coefficients for each SECOND of the input data.
Any books or papers you could recommend on this would be greatly
appreciated.
Thank you. Ken Tew
--------------------------- Topic #4 -----------------------------------
From: [email protected] (Zhong-Lin Lu)
Subject: Question: Wavelets to divide 2D freq. space in polar coordinates ?
Question on how to construct wavelets to divide 2D frequency space in
polar coordinates??
I am interested in learning from our netters on the above question. I am
primarily interested in dividing a two dimensional frequency space in
polar coordinates: one octave bands in the radial direction and 4 or 5
regions in the angular direction. I would appreciate any reference to
articles/books and any kind of suggestions. I am relatively inexperienced
in wavelet theory.
Thanks for your attention.
--------------------------- Topic #5 -----------------------------------
From: J.C. Baltzer AG, Science Publishers
Subject: Contents Advances in Computational Mathematics
Advances in Computational Mathematics
Editors-in-Chief:
John C. Mason, Applied & Computational Mathematics Group, Royal Military
College of Science Shrivenham, Swindon, SN6 8LA, England
E-mail: [email protected]
and
Charles A. Micchelli, Mathematical Sciences Department, IBM Research
Center, P.O. Box 218, Yorktown Heights, NY 10598, USA
E-mail: [email protected]
Contents Volume 1, issues 3 & 4
W. Dahmen, S. Prossdorf and R. Schneider:
Wavelet approximation methods for pseudodifferential equations II: Matrix
compression and fast solution, pp. 259 - 335
K.A. Cliffe, T.J. Garratt and A. Spence:
Eigenvalues of the discretized Navier Stokes equation with application to
the detection of Hopf bifurcations, pp. 337 - 356
J. Williams and Z. Kalogiratou:
Least squares and Chebyshev fitting for parameter estimation in ODEs, pp.
357 - 366
C.T.H. Baker and C.A.H. Paul:
Parallel continuous Runge Kutta methods and vanishing lag delay
differential equations, pp. 367 - 394
Please request a FREE SPECIMEN COPY from [email protected]!
Advances in Computational Mathematics is an interdisciplinary journal of
high quality, driven by the computational revolution and emphasising
innovation, application and practicality. This journal is of interest to a
wide audience of mathematicians, scientists and engineers concerned with
the development of mathematical principles and practical issues in
computational mathematics.
Publication areas of Advances in Computational Mathematics include
computational aspects of algebraic, differential and integral equations,
statistics, optimization, approximation, spline functions and wavelet
analysis. Submissions are especially encouraged in modern computing aspects
such as parallel processing and symbolic computation and application areas
such as neural networks and geometric modelling.
All contributions should involve novel research. Expository papers are also
welcomed provided they are informative, well written and shed new light on
existing knowledge. The journal will consider the publication of lengthy
articles of quality and importance. From time to time special issues
devoted to topics of particular interest to the reader will be published
with the guidance of a guest editor. Ideas for special issues can be
communicated to the Editors-in-Chief.
Software of accepted papers is tested and made available to the readers on
Netlib. Short communications, a problems section and letters to the
Editors-in-Chief are also featured in the journal at regular intervals.
Advances in Computational Mathematics is being published quarterly.
Authors are cordially invited to submit their manuscripts in triplicate to
John C. Mason, Applied & Computational Mathematics Group, Royal Military
College of Science, Shrivenham, Swindon SN6 8LA, UK, E-mail:
[email protected]
All manuscripts will be refereed. The decision for publication will be
communicated by John C. Mason. After acceptance of their paper, authors are
invited to send a diskette with the TEX (or LATEX or AMS-TEX) source of
their paper together with a hard copy including the letter of acceptance to
John C. Mason. For papers concerning software an ASCII diskette is needed.
Personal subscriptions; subscriptions in developing countries and how to
subscribe
A personal subscription to Advances in Computational Mathematics is
available at Sfr. 130.00/US$ 97.00 per volume including postage. The
personal subscription is meant for private use only and may not be made
available to institutes and libraries. Subscriptions must be prepaid
privately and ordered directly from J.C. Baltzer AG, Science Publishers,
Wettsteinplatz 10, CH-4058 Basel, Switzerland.
For institutions in developing countries, a subscription is available at
the special price of Sfr. 130.00/US$ 97.00 per volume including postage.
Subscriptions must be prepaid and ordered directly from J.C. Baltzer AG,
Science Publishers, Wettsteinplatz 10, CH-4058 Basel, Switzerland.
In the United States please send your order to: J.C. Baltzer AG, Science
Publishers, P.O. Box 8577, Red Bank, NJ 07701-8577.
>From all other countries please send your order to: J.C. Baltzer AG,
Science Publishers, Wettsteinplatz 10, CH-4058 Basel, Switzerland.
J.C. Baltzer AG, Science Publishers, Wettsteinplatz 10, CH-4058 Basel,
Switzerland, fax: +41 - 61 - 692 42 62, e-mail: [email protected]
J.C. Baltzer AG, Science Publishers
Asterweg 1A, 1031 HL Amsterdam, The Netherlands
tel. +31-20-637 0061 fax. +31-20-632 3651
e-mail: [email protected]
--------------------------- Topic #6 -----------------------------------
From: J.R.Klauder and R.F.Streater.
Subject: Preprint available
The following new preprint is available by E-mail from [email protected]
Wavelets and the Poincare Half-plane, by J.R.Klauder and R.F.Streater.
Abstract:We transform a square-integrable signal into an analytic function
in the upper half-plane, on which $SL(2,{\bf R})$ acts. We show that this
analytic function is determined by its scalar products with the discrete
family of functions obtained by acting with $SL(2,{\bf Z})$ on a certain
vector, provided that the spin of the representation is less that 3.
R.F.Streater.
--------------------------- Topic #7 -----------------------------------
From: Silvia Bertoluzza, IAN-CNR Pavia (Italy)
Subject: Preprints available
Preprint can be requested for the following papers:
"Some Remarks on Wavelet Interpolation"
by S. Bertoluzzza, G. Naldi
Abstract: In this paper we present some results regarding wavelet
interpolation. After discussing the characterization of interpolating
scaling functions we give two examples of construction of an interpolation
operator in the framework of a multiresolution analysis, the first obtained
by constructing an interpolating scaling function in a given multiresolution
analysis, and the second based on the autocorrelation function of a
compactly supported Daubechies wavelet. For both examples we give an
estimate on the order of the interpolation error.
--
"A wavelet Collocation Method for the Numerical Solution of PDE's"
Abstract:
We present a wavelet collocation method for the numerical solution
of partial differential equations. Such a method is based on the use of the
autocorrelation functions of I. Daubechies's compactly supported wavelets.
We study from the theoretical point of view
the application of such a method to the solution of an elliptic constant
coefficients partial differential equation on the line, proving stability and
an error estimate. Due to the particular
form of the trial functions we chose, we remark that we get the same
linear system which arises in the application of the wavelet Galerkin method
using
Daubechies wavelets. However, due to the higher regularity and to the better
approximation properties, the collocation method has higher order.
In the last section we will present results of such a method tested
on two simple elliptic boundary value problems on the interval.
---
Point of contact for preprint:
Silvia Bertoluzza,
IAN-CNR,
v. Abbiategrasso 209
27100 Pavia (Italy)
E-mail: [email protected]
--------------------------- Topic #8 -----------------------------------
From: Jaideva C. Goswami, Dept. of Electrical Engineering, Texas A&M Univ.
Subject: Reply: Question Coeff. for the Chui wavelets, WD 2.14 # 9.
Note: The equation numbers are from the book, "An Introduction to Wavelets",
by C. K. Chui.
For Chui-Wang wavelets, the reconstruction sequences ({p_k},{q_k}) are
finite but the decomposition sequences ({a_k},{b_k}) are infinite with
exponential decay. Symbols of the decomposition sequences are related
to those of reconstruction sequences through (5.4.12). More explicit
relation is given by (6.5.1). Euler-Frobenius Laurent series that appears
in (6.5.1) is easy to compute for any order of spline (see 6.1.13). Then
the decomposition sequence can be obtained by comparing the corresponding
coefficients of powes of "z" on the both sides of (6.5.1). For this
purpose, use of symbolic computation package such as, MACSYMA, MAPLE, is
advisable.
Another simpler way of computing ({a_k},{b_k}) is given in Prof. Chui's
forthcoming book, "Wavelets: For Time-Frequency Analysis".
Jaideva C. Goswami
Dept. of Electrical Engineering
Texas A&M University
College Station, TX 77840 [email protected]
--------------------------- Topic #9 -----------------------------------
From: Jack K. Cohen <[email protected]>
Subject: Mathematica wavelet program available.
I have put Mathematica packages and notebooks for construction of
the Meyer, Battle-Lemarie and Daubechies wavelets and
related functions in our anonomous ftp:
hilbert.mines.colorado.edu or 138.67.12.63
directory pub/wavelets.
All files are compressed, use `binary mode' in ftp.
The *.m.Z files are the packages.
The *.ma.Z files are NeXT-specific notebooks
Using nb2tex from mathsource, I converted the notebooks to Tex:
The *.300dpi.ps.Z are plain Tex, 300 dots per inch
The *.400dpi.ps.Z are plain Tex, 400 dots per inch
The implementations are "vanilla", derived from the
material in Ten Lectures. I.e., nothing new here,
the purpose is to save other Mathematica users from
having to recreate the functions for the basic wavelets.
Authors: Jack K. Cohen, Tong Chen, Meng Xu
Colorado School of Mines in Golden
[email protected]
[email protected]
[email protected]
--------------------------- Topic #10 -----------------------------------
From: David Newland, Cambridge University Engineering Department
Subject: New Wavelet Toolbox in Matlab
NEW WAVELET TOOLBOX
As previously announced, the book Random Vibrations, Spectral and Wavelet
Analysis (John Wiley, New York, ISBN 0-470-22153-4 in the USA and Longman,
ISBN 0-582-21584-6 elsewhere) has an accompanying wavelet toolbox in
MATLAB*. This toolbox is now available separately from the book with an
accompanying descriptive manual. The expanded toolbox currently has 18
M-files for dilation and harmonic wavelet transforms and the presentation
of their results in signal analysis. There is also a short demonstration
program.
Harmonic wavelets have a simple analytical structure, a fast algorithm, are
orthogonal, and have many applications in signal analysis. Musical
wavelets are developments of harmonic wavelets which allow greater
frequency discrimination.
The programs compute the one-dimensional dilation wavelet transform and its
inverse,display the results of the transform, map the results as contour
and mesh diagrams, and compute two-dimensional dilation wavelet transforms
and their inverse. There are also programs to compute the harmonic wavelet
transform of a one-dimensional sequence, and its inverse and to compute the
harmonic wavelet map of a real sequence. This uses an algorithm based on
the FFT and, for most problems, is quicker than the dilation wavelet
transform. In addition there are programs to compute the musical wavelet
transform of a real signal and its inverse and to display the results of
this calculation in the form of a musical wavelet map. This has a
presentation similar to the musical stave and produces a diagram similar to
a sonogram.
Instructions on how to obtain the toolbox can be obtained by e-mail request
to [email protected].
D E Newland
*MATLAB is a registered trademark of The MathWorks Inc.
Professor David Newland
Cambridge University Engineering Department
Trumpington St
Cambridge, CB2 1PZ, UK
--------------------------- Topic #11 -----------------------------------
From: [email protected] (Xue-Dong Dai)
Subject: Workshop: Wavelets in Chemical Engineering
WASHINGTON UNIVERSITY in St. Louis, WORKSHOP
Sunday, November 7, 1993
1:00 - 5:45 PM Lopata Hall, Rm 101
Wavelets in Chemical Engineering
1:00 - 1:45 PM - Introduction and history of wavelet transforms
Srinivas Palavajjhala, Washington University
1:45 - 2:15 PM - Multiscale resolution, trend analysis
Amol Joshi, Aspen Technology
2:15 - 3:15 PM - Phase Plane Analysis, pattern recognition with demo.
Xue-dong Dai, Washington University
3:15 - 3:45 PM - Applications in process control
Srinivas Palavajjhala
3:45 - 4:15 PM - Data compression
Bhavik Bakshi, Ohio State
4:15 - 4:45 PM - Partial differential equations
Mike Nikolaou, Texas A&M
4:45 - 5:15 PM - Algorithms for wavelet transforms
Eric Goirand, Washington University
5:15 - 5:45 PM - "Wavelet's, etc. - what are they good for?"
Dr. M. V. Wickerhauser, Washington University
5:45 PM - Wrap-up
Contact: R. L. Motard, [email protected] or 314-935-6072
for hard copy and a map. FAX 314-935-7211.
|
| 1409.29 | Wavelet digest, vol. 2, Nr 16 | STAR::ABBASI | new, enhanced, and improved | Wed Nov 10 1993 22:45 | 509 |
| ps. also check issue 771 of electronic engineering TIMES newspaper (dated
November 8, 1993), page 35 for interestign article titled:
"MIT wavelets amplify modeling of electrons", it talkes about how
using waveletes improved modeling and predicting behavior of electrons
inside semiconductors as opposed to the case when say fourier
transforms are used.
\naser
---------------------------------------------------------------------------
From: US2RMC::"[email protected]" "MAIL-11 Daemon" 10-NOV-1993 22:02:43.69
To: star::abbasi
CC:
Subj: Wavelet Digest, Vol. 2, Nr. 16.
Wavelet Digest Wednesday, November 10, 1993 Volume 2 : Issue 16
Today's Editor: Wim Sweldens
[email protected]
Today's Topics:
1. Short course: Wavelet Electrodynamics
2. Preprint available: Application of wavelets to EEG
3. CFP: Visual Communications and Image Processing '94 (VCIP '94)
4. Looking for code in Daubechies book
5. Preprint available: Portrait of frames
6. Question: Wavelets for Image Compression
7. Computing line integrals of a 3D function described in wavelets?
8. Papers available:
9. Short course on wavelets filter banks and applications
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
To unsubscribe, e-mail with "unsubscribe" followed by your e-mail
address as subject. To change address, unsubscribe and resubscribe.
Archive site, preprints, references and back issues:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directories /pub/wavelet and /pub/imi_93.
Gopher and Xmosaic server: bigcheese.math.scarolina.edu.
Current number of subscribers: 3004
--------------------------- Topic #1 -----------------------------------
From: [email protected] (Jodi Nix)
Subject: Short course: Wavelet Electrodynamics
March 21, 1994 there will be a one day short course on Wavelet Electrodynamics
offered in conjunction with the Applied Computational Electromagnetics Society
(ACES) 1994 Symposium. The course and 10th Annual symposium will be at the
Doubletree Hotel and Convention Center in Monterey, CA. Course description:
Maxwell's equations are symmetric under space-time translations and dilations.
This is used to write an arbitary solution as a superposition of
electromagnetic wavelets. Possible applications include:
(a) Designing electromagnetic waves with initial data specified locally
and by scale.
(b) An economic description of electromagnectic pulses emanating from moving
or accelerating objects.
The course will consist of four 90 minute lectures. Notes will be provided.
Reference: G. Kaiser, A Friendly Guide to Wavelets, Birkhauser,
available 1/94.
If you would like additional information on the course and/or symposium please
contact Jodi Nix at 513-476-3550 or email: [email protected]
--------------------------- Topic #2 -----------------------------------
From: [email protected] (Leonard J. Trejo)
Subject: Preprint available: Application of wavelets to EEG
Preprint available -- Application of wavelets to feature extraction of EEG
(Request pre-print by e-mail message to [email protected])
Trejo, L. J. and Shensa, M. J. (1993b, in press). Linear and neural
network models for predicting human signal detection performance from
event-related potentials: A comparison of the wavelet transform with
other feature extraction methods. Proceedings of the 1993 Simulation
Technology Multiconference, San Francisco, CA, November 7-10. San
Diego: Society for Computer Simulation.
ABSTRACT
This report describes the development and evaluation of mathematical
models for predicting human performance from discrete wavelet
transforms (DWT) of event-related potentials (ERP) elicited by
task-relevant stimuli. The DWT was compared to principal components
analysis (PCA) for representation of ERPs in linear regression
and neural network models developed to predict a composite measure
of human signal detection performance. Linear regression models
based on coefficients of the decimated DWT predicted signal
detection performance with half as many free parameters as
comparable models based on PCA scores and were relatively more
resistant to model degradation due to over-fitting.
Feed-forward neural networks were trained using the backpropagation
algorithm to predict signal detection performance based on raw ERPs,
PCA scores, or high-power coefficients of the the DWT. Neural
networks based on high-power DWT coefficients trained with fewer
iterations, generalized to new data better, and were more resistant
to overfitting than networks based on raw ERPs. Networks based on
PCA scores did not generalize to new data as well as either the DWT
network or the raw ERP network.
The results show that wavelet expansions represent the ERP
efficiently and extract behaviorally important features for use in
linear regression or neural network models of human performance.
The efficiency of the DWT is discussed in terms of its decorrelation
and energy compaction properties. In addition, the DWT models
provided evidence that a pattern of low-frequency activity (1 to 3.5
Hz) occurring at specific times and scalp locations is a reliable
correlate of human signal detection performance.
--------------------------- Topic #3 -----------------------------------
From: Aggelos Katsaggelos <[email protected]>
Subject: CFP: Visual Communications and Image Processing '94 (VCIP '94)
ANNOUNCEMENT AND CALL FOR PAPERS
Visual Communications and Image Processing '94 (VCIP '94)
September 25-28, 1994, Bismark Hotel, Chicago, IL, USA
Cooperating Organizations:
IEEE Circuits and Systems Society
European Association for Signal Processing (EURASIP)
Conference Chair:
Aggelos K. Katsaggelos, Northwestern Univ.
Europe Liaison:
Prof. Jan Biemond, Delft Univ. of Technology,
Department of Electrical Engineering, [email protected]
Asia Liaison:
Dr. Toshi Koga, NEC Corp, Second Transmission Division
[email protected]
Visual communications and image processing have become engineering
areas that attract interdisciplinary research interest and lead to
significant developments for technology and science. This conference
is designed as a forum for presenting important research results in
these fields. Original and unpublished material of novel techniques and
new developments is solicited on the following and related topics:
A. Visual Communications
still image coding -- video sequence coding -- model-based image coding
very low bit rate coding -- motion estimation for video coding
TV, HDTV and super HDTV -- packet video -- picture archiving and
communications systems -- parallel processing hardware -- special topics
B. Image Processing and Analysis
image filtering, enhancement, and restoration -- feature extraction
image segmentation -- object recognition -- biomedical image processing
VLSI implementation and system architectures -- morphological image
processing -- fractals and wavelets -- special topics
C. Image Sequence Analysis
motion analysis -- filtering, restoration, interpolation -- multiscale
image sequence analysis -- computer vision topics in image sequence
analysis -- special topics
Best Student Paper Awards
At this meeting, two awards will be presented to the best papers
submitted by students. To qualify for these awards, each consisting of
a cash prize and a plaque, the candidate must be the principal author.
A letter from the student's adviser stating that the major work was
done by the student must accompany the final manuscript in order to be
considered by the award committee.
SPIE gratefully acknowledges Lockheed Corp. and NEC Corp. for
their generous sponsorship of these awards.
Young Investigator Award
An award will be given for the best paper submitted by a young
researcher who has graduated within five years from the date of the
meeting. A letter from the researcher requesting that his/her paper be
considered must be submitted with the final manuscript, certifying
he/she meets the qualifications for the award.
SPIE gratefully acknowledges Industrial Technology Research Institute
(ITRI) for generously sponsoring this award.
SUBMISSION OF SUMMARIES:
Summary Due Date: 24 January 1994* Manuscript Due Date: 20June 1994+
*Late submissions may be considered, subject to program time
availability and chairs' approval. However, due to the significant
number of submissions, chairs and committee will give first preference
to summaries sent by the due date.
+Proceedings of this conference will be published before the meeting
and available at the symposium. Manuscript due date must be strictly
observed.
Your summary should include the following:
1. SUMMARY TITLE
2. AUTHOR LISTING (principal author first)
Full names and affiliations as they will appear in the program.
3. CORRESPONDENCE FOR EACH AUTHOR
Mailing address, telephone, telefax, e-mail address.
4. SUBMIT TO: Visual Communications and Image Processing '94
5. INDICATE: Letter of topic (A, B, or C) and subtopic
6. PRESENTATION
Please indicate your preference for either "Oral Presentation" or
"Poster Presentation".
7. SUMMARY TEXT 500 words.
8. BRIEF BIOGRAPHY (principal author only) 50 to 100 words.
To be considered for acceptance, you may either:
* send summary via electronic mail to Internet [email protected]
(ASCII format);
* or fax one copy to SPIE at 206/647-1445;
* or mail four copies to SPIE to:
Visual Communications and Image Processing '94
SPIE, P.O. Box 10, Bellingham, WA 98227-0010
Shipping address: SPIE, 1000 20th Street, Bellingham, WA 98225
SPIE contact: Rosa Cays, Coordinator, Technical Programs and Education
Phone: 206/676-3290; Telex 46-7053
Note from the editor: this announcement was shortened for inclusion
in the digest, please contact the organizers for the long version.
--------------------------- Topic #4 -----------------------------------
From: Daniel K R McCormack <[email protected]>
Subject: Looking for code in Daubechies book
Hello,
I want to plot the scaling function for D4, D12 and D20 wavelets.
There is an algorithm given in Chp 6 section 5 of I. Daubechies book
"Ten Lectures on Wavelets" for calculating an approximation to the scaling
function. I would like to know if anyone has coded this
algorithm or any other similar algorithm. Any reference to this code
would be much appreciated.
Alternatively if someone could reference or give me the values
of the function evaluation for D4, D12 and D20 it would save me some time
and effort.
Many Thanks
Daniel McCormack.
--------------------------- Topic #5 -----------------------------------
From: [email protected]
Subject: Preprint available: Portrait of frames
Preprint of the paper below is available: if interested, send your postal
address by email to [email protected]
Portrait of frames
(To appear in the Proc. Amer. Math. Soc.)
By Akram Aldroubi
We introduce two methods for generating frames of a Hilbert space
$\cal H$. The first method uses bounded operators on $\cal H$. The
other method uses bounded linear operators on $l_2$ to generate
frames of $\cal H$. We characterize all the mappings that transform
frames into other frames. We also show how to construct all frames
of a given Hilbert space $\cal H$, starting from any given one. We
illustrate the results by giving some examples from multiresolution
and wavelet theory.
--------------------------- Topic #6 -----------------------------------
From: Ravindra Chandrashekhar <[email protected]>
Subject: Question: Wavelets for Image Compression
Dear Wavelet Experts,
I started researching Wavelets (Discrete Wavelet Transform, DWT) this
semester and have read several papers. There seems to be an abundance
of papers, books, and mathematical reviews. I have also reviewed some
of the publication listings under the wavelets directory on this network.
I am trying to do research on image compression. I have read articles by
O. Rioul, Shensa et al and am in the process of reviewing "Image Compression
Through Wavlet Transform Coding" by DeVore, Jawerth, and Lucier.
1) So far, I have obtained articles through my own research. Any
suggestions on "Must Read Articles" related to image compression that
I may have missed.
2) Can any one suggest articles related to image compression that have code
examples or sources of code? I reviewed the DWT in the Numerical Reciepies
Book. But I am open to suggestions on other code examples and articles.
I would be very happy to receive any suggestions.
My email address is [email protected]
Thanks!
Sincerely,
Ravindra Chandrashekhar
--------------------------- Topic #7 -----------------------------------
From: Peter Schroeder <[email protected]>
Subject: Computing line integrals of a 3D function described in wavelets?
Question: How to compute line integrals of a 3D function described in
wavelets?
Consider a scalar function f(x,y,z)=f(\vex{x}) (sufficiently smooth)
defined on a lattice [0..127]^3. Assume further that we have already
transformed it into the wavelet basis. We would now like to compute
\exp(-\int_{t_0}^{t_1} f(\vec{x}_0+s(\vec{x}_1-\vec{x}_0)) ds)
or in words, the exponential of -f() integrated along some ray from a
starting point to some endpoint in the original volume. Why? Well, if f()
represents the total scattering cross section of some material the above
integral will give us optical depth in the given medium along a line of
sight. This is only a subproblem of a larger problem.
Now, obviously this is easy in the 1D case since path integration is
trivial but things become a lot less straightforward in higher D. In
particular there is no hope of somehow precomputing the above \exp()
integrals for all possible choices of (x,y,z)_{t_0} (x,y,z)_{t_1} since
that is a 6 dimensional object and even if we can do the wavelet transform
quickly in one dimension the cost of doing it along all dimensions is going
to be totally overwhelming. Knowing this it seems we need some lazy scheme
to evaluate it, i.e. somehow take advantage of the sparse structure of the
original wavelet representation of f() and directly compute the above
function for a given (t_0 t_1). Of course the \exp() should help *a lot* in
this task as well since the tails will contribute very little and we should
be able to take advantage of this in the multi resolution framework.
Presumably this would be helped considerably if we knew something about
objects of the form
\exp(-\int_{t_0}^{t_1} \phi(\vex{x}_0+s(\vec{x}_1-\vec{x}_0)))
and similarly for \psi. Or in words, \exp()s of path integrals through
wavelet bases (product bases) in 3D. Does anybody have any experience with
this? References? War stories?
Thanks for any help you might have!
Peter ([email protected])
--------------------------- Topic #8 -----------------------------------
From: Bruno Torresani <[email protected]>
Subject: Papers available:
The following papers are available by anonymous ftp at
cpt.univ-mrs.fr (139.124.7.5)
login as "anonymous" and enter your e-mail address as password.
path:
/pub/preprints/93/wavelets
The following preprints are available
---
92-P.2811.tar.Z (Plain TeX)
\centerline{\bf N-DIMENSIONAL AFFINE WEYL-HEISENBERG WAVELETS}
\centerline{\bf C. Kalisa, B.Torresani}
{\bf Abstract} : Multidimensional coherent states systems
generated by translations, modulations, rotations and dilations are
described. Starting from unitary irreducible representations of
the $n$-dimen\-sional Weyl-Heisenberg group, which are not
square-integrable,
one is led to consider systems of coherent states labeled by the
elements of
quotients of the original group. Such systems can yield a resolution
of the
identity, and then be used as alternatives to usual wavelet of
windowed
Fourier analysis. When the quotient space is the phase space of the
representation,
different embeddings of it into the group provide different
descriptions of the phase space.
September 1992;
to appear in "Annales de l'institut Henri Poincar\'e, Physique
Th\'eorique".
---
93-P.2910.tar.Z (latex, with postscript figures)
\centerline{\bigbf PYRAMIDAL ALGORITHMS FOR}
\centerline{\bigbf LITTLEWOOD-PALEY DECOMPOSITIONS}
\centerline{M.A. Muschietti, B. Torr\'esani }
\noindent{\bf Abstract} : It is well known that with any usual
multiresolution
analysis of $L^2({\bf R})$ is associated a pyramidal algorithm for
the
computation of the corresponding wavelet coefficients.
It is shown that an approximate pyramidal
algorithm may be associated with more general Littlewood-Paley
decompositions.
Accuracy estimates are provided for such approximate algorithms.
Finally, some
explicit examples are studied.
May 1993; To appear in SIAM J. Math. An.
---
93-P.2932.tar.Z (latex, with postscript figures)
\centerline{\bf WAVELETS ON DISCRETE FIELDS}
\centerline{\bf K.Flornes, A.Grossmann, M.Holschneider,
B.Torr\'esani}
\begin{abstract}
An arithmetic version of continuous wavelet analysis is described.
Starting
from a square-integrable representation of the affine group of $\Z_p$
(or $\Z$)
it is shown how wavelet decompositions of $\ell^2(\Z_p)$ can be
obtained.
Moreover, a redefinition of the dilation operator on $\ell^2(\Z_p)$
directly yields an algorithmic structure similar to that appearing
with
multiresolution analyses.
\end{abstract}
July 1993; To appear at Applied and Computational Harmonic Analysis.
---
93-P.2878.tar.Z latex (with postscript figures)
\centerline{\bigbf PHASE SPACE DECOMPOSITIONS:}
\centerline{\bigbf Local Fourier analysis on spheres}
\centerline{Bruno Torresani}
\noindent{\bf Abstract} : Continuous wavelet analysis
and Gabor analysis have proven
to be very useful tools for the analysis of signals in which local
frequencies can be extracted. Both techniques can be described
in the same footing using the theory of square-integrable group
representations or derived theories. It is shown here how the
same kind of techniques can be developed to construct
phase-space representation theormes for functions defined
on homogeneous spaces. As examples, the cases of the circle
and the spheres are carried in details, and some explicit expressions
are given in the one and two-dimensional cases.
March 1993;
Submitted to Applied and Computational Harmonic Analysis.
---
93-P.2870.tar.Z (Plain TeX, with postscript figures)
\centerline{\bf LOCAL FREQUENCY ANALYSIS WITH}
\centerline{\bf TWO-DIMENSIONAL WAVELET TRANSFORM}
\centerline{\bf Caroline Gonnet \hskip2cm Bruno Torresani}
{\bf Abstract} : An algorithm for the characterization of local
frequencies
in two-dimensional signals is described. The algorithm generalizes to
the 2D
situation a method previously proposed in the 1D context, based on
the analysis
of the phase of the continuous wavelet transform. It uses families of
wavelets
generated from a unique one by shifts, dilations and rotations.
January 1993; revised April 1993.To appear in Signal Processing
--------------------------- Topic #9 -----------------------------------
From: [email protected]
Subject: Short course on wavelets filter banks and applications
SHORT COURSE on WAVELETS FILTER BANKS and APPLICATIONS
at Wellesley College near Boston: June 24-27 1994
organized and taught by Gilbert Strang and Truong Nguyen , M I T
This is the first announcement of a short course on filter banks,
multirate signal processing, and wavelets. By sending an email
message to Gilbert Strang at MIT (( [email protected] ))
you will receive the second announcement directly. The 4-day course
will be given over the last weekend in June, 1994, to reduce air fares
and allow for sightseeing in Boston and New England. Wellesley College
has the most beautiful campus in this area ( 14 miles west of Boston ).
Room and board at the College is available to participants at reasonable cost.
The workshop course will be taught by Gilbert Strang and Truong Nguyen of MIT.
The topics will include
Digital Filters: Analysis and Design
Matrix Analysis: Toeplitz Matrices and Circulants
Multirate Signal Processing: Filtering, Decimation, Polyphase
Wavelet Transform: Pyramid Implementation
Daubechies Wavelets, Biorthogonal Wavelets, Vector Wavelets
Compression, Transient Detection, Radar Processing
Non-Destructive Evaluation, Digital Communications
Perfect Reconstruction: Orthonormal, Cosine-modulation
Spectral Factorization, Transmultiplexers
Quantization effects
This workshop course will NOT assume an extended background in
filter banks and wavelets. It is developed from an MIT graduate course
suggest problems and possible applications for discussion.
An email message to [email protected] mentioning "Workshop Course" will
bring further information early in 1994.
|
| 1409.30 | Wavelet Digest, Vol. 2, Nr 17 | STAR::ABBASI | only 21 days to go and counting... | Fri Nov 19 1993 20:17 | 558 |
| From: US2RMC::"[email protected]" "MAIL-11 Daemon" 19-NOV-1993 19:29:12.27
To: star::abbasi
CC:
Subj: Wavelet Digest, Vol. 2, Nr. 17.
Wavelet Digest Friday, November 19, 1993 Volume 2 : Issue 17
Today's Editor: Wim Sweldens
[email protected]
Today's Topics:
1. Book available: Wavelets: Mathematics and Applications
2. Answer to WD 2.16, topic #4.
3. CFP: Wavelets and Fractals
4. Announcing a UCLA Extension Short Course.
5. Two papers available
6. Ph.D. abstract
7. Book announcement: Multilevel adaptive methods
8. Looking for AWARE INC
9. Question: application of Wavelets in EEG?
10. Post-doctoral position available at University of Toronto
11. CFP: Workshop on Wavelets in Medicine and Biology.
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
To unsubscribe, e-mail with "unsubscribe" followed by your e-mail
address as subject. To change address, unsubscribe and resubscribe.
Archive site, preprints, references and back issues:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directories /pub/wavelet and /pub/imi_93.
Gopher: bigcheese.math.scarolina.edu.
Current number of subscribers: 3019
--------------------------- Topic #1 -----------------------------------
From: John Benedetto and Michael Frazier.
Subject: Book available: Wavelets: Mathematics and Applications
The book, Wavelets: Mathematics and Applications, edited
by John Benedetto and Michael Frazier and published by CRC Press,
is now available. The price is U.S.$64.95 / Outside U.S.$77.95.
The book contains 575 pages.
The book can be ordered at CRC Press by telephoning
1-800-272-7737 ( Monday-Friday, continental U.S.) or 1-407-994-0555,
or by writing:
CRC Press, Inc. 2000 Corporate Blvd., N.W. Boca Raton, Florida 33431.
The first part of the book is devoted to the fundamentals of
wavelet analysis including chapters on wavelet bases, fast computation,
the theory of frames, dilation equations, and local Fourier bases.
The second and third parts are devoted to applications of wavelets
in signal analysis and partial differential operators, respectively.
Each chapter provides an up-to-date introduction to the role of
wavelet theory in such topics as sampling theory, probability and
statistics, compression, turbulence, operator theory, numerical
analysis, nonlinear analysis, and harmonic analysis.
The specific topics are listed in the following table of contents.
[1] Construction of Orthonormal Wavelets, Robert S. Strichartz
[2] An Introduction to the Orthonormal Wavelet Transform on Discrete Sets
Michael Frazier and Arun Kumar
[3] Gabor Frames for L^2 and Related Spaces
John J. Benedetto and David F. Walnut
[4] Dilation Equations and the Smoothness of Compactly
Supported Wavelets, Christopher Heil and David Colella
[5] Remarks on the Local Fourier Bases, Pascal Auscher
[6] The Sampling Theorem, Phi-Transform and Shannon Wavelets
for R, Z, T, and Z_N, Michael Frazier and Rodolfo Torres
[7] Frame Decompositions, Sampling, and Uncertainty Principle
Inequalities, John J. Benedetto
[8] Theory and Practice of Irregular Sampling
Hans Feichtinger and Karlheinz Gr"ochenig
[9] Wavelets, Probability, and Statistics: Some Bridges, Christian Houdre
[10] Wavelets and Adapted Waveform Analysis,
Ronald R. Coifman and Victor Wickerhauser
[11] Near Optimal Compression of Orthonormal Wavelet Expansions
Bjorn Jawerth, Chia-chang Hsiao, Bradley Lucier, and Xiangming Yu
[12] On Wavelet-Based Algorithms for Solving Differential Equations
Gregory Beylkin
[13] Wavelets and Nonlinear Analysis, Stephane Jaffard
[14] Scale Decomposition in Burgers' Equation
Frederic Heurtaux, Fabrice Planchon, and Victor Wickerhauser
[15] The Cauchy Singular Integral Operator and Clifford Wavelets
Lars Andersson, Bjorn Jawerth, and Marius Mitrea
[16] The Use of Decomposition Theorems in the Study of Operators
Richard Rochberg
--------------------------- Topic #2 -----------------------------------
From: Herbert Homeier <[email protected]>
Subject: Answer to WD 2.16, topic #4.
With regard to Topic #4 of the wavelet digest of Nov 10, 1993, Vol. 2, Nr. 16,
(Looking for code in Daubechies book) the following message may be of interest.
Best regards
Herbert Homeier
In article 60515 [email protected] (Jack K. Cohen) writes:
> I've put up some packages and notebooks for constructing the
> classical wavelets: Daubechies, Meyer, Battle-Lemarie.
> There is nothing new here, my purpose is save others from
> having to rediscover this wheel (and maybe have someone tell
> me how to do it better!).
>
> The anonymous ftp site is: hilbert.mines.colorado.edu (138.67.12.63)
> The directory is pub/wavelets
Note from the editor:
for more information see Wavelet Digest 2.15, topic #9.
--------------------------- Topic #3 -----------------------------------
From: [email protected] (Christopher John Lennard)
Subject: CFP: Wavelets and Fractals
1) Title of Conference : Wavelets and Fractals
2) Date : May 20-22, 1994
3) Sponsors : The Institute of Mathematics and its Applications, Minnesota,
and
Department of Mathematics, University of Pittsburgh
4) Place of Meeting : University of Pittsburgh, Pittsburgh,
Pennsylvania
5) A General Statement on the Program :
Although the theories of wavelets and fractals have developed from
different motivations, they are mathematically interwoven and have aspects
of striking resemblance. This conference aims to bring mathematicians in
both fields together to present and discuss the many challenging problems
in which they have a mutual interest.
Topics will include wavelet analysis and sampling, two scale dilation
equations, fractals and tiling, multifractal structure, and self-similarity
in harmonic analysis.
6) Names of Principal Speakers (as of November 1993) :
C. Bandt (Greifwald), C. Chui (Texas A&M), J. Benedetto (Maryland),
C. Cavaretta (Kent State), K. Grochenig (Connecticut)
P. Jorgensen (Iowa), J. Lagarias (A T&T Bell Lab),
M. Lapidus (California, Riverside), D. Mauldin (North Texas),
R. Strichartz (Cornell), S. Pedersen (Wright State)
7) Deadline(s) for abstracts of contributed papers : To be announced.
8) Source of Further Information :
Ka-Sing Lau or Chris Lennard, Math and Stat Dept.,
Univ. of Pittsburgh, Pittsburgh, PA 15260.
email :[email protected], [email protected]
--------------------------- Topic #4 -----------------------------------
From: "Watanabe, Nonie" <[email protected]>
Subject: Announcing a UCLA Extension Short Course.
Announcing a UCLA Extension Short Course.
Wavelet Transform: Techniques and Applications
March 7-11, 1994 at UCLA
OVERVIEW
For many years, the Fourier Transform (FT) has been used in a wide variety of
application areas, including multimedia compression of wideband ISDN for
telecommunications; lossless transform for fingerprint storage,
identification, and retrieval; an increased S/N ratio for target
discrimination in oil prospect seismic imaging; in-scale and
rotation-invariant pattern recognition in automatic target recognition; and
in-heart, tumor, and biomedical research.
This course describes a new technique, the Wavelet Transform (WT), that is
replacing the windowed FT in the applications mentioned above. The WT uses
appropriately matched bandpass kernels, called mother wavelets, thereby
enabling improved representation and analysis of wideband, transient, and
noisy signals. The principal advantages of the WT are 1) its localized nature
which accepts less noise and enhances the SNR, and 2) the new problem-solving
paradigm it offers in the treatment of nonlinear problems. The course covers
WT principles as well as adaptive techniques, describing how WTs mimic human
ears and eyes by tuning up "best mothers" to spawn "daughter" wavelets that
catch multi-resolution components to be fed the expansion coefficient through
an artificial neural network, called a wavenet. This in turn provides the
useful automation required in multiple application areas, a powerful tool
when the inputs are constrained by real-time sparse data (for example, the
"cocktail party" effect where you perceive a desired message from the
cacophony of a noisy party).
Another advancement discussed in the course is the theory and experiment for
solving nonlinear dynamics for information processing; e.g., the
environmental simulation as a non-real-time virtual reality. In other words,
real-time virtual reality can be achieved by the wavelet compression
technique, followed by an optical flow technique to acquire those wavelet
transform coefficients, then applying the inverse WT to retrieve the virtual
reality dynamical evolution. (For example, an ocean wave is analyzed by
soliton envelope wavelets.)
Finally, implementation techniques in optics and digital electronics are
presented, including optical wavelet transforms and wavelet chips.
COURSE MATERIALS
Course notes and relevant software are distributed on the first day of the
course. These notes are for participants only, and are not for sale.
COORDINATOR AND LECTURER
Harold Szu, PhD
Research Physicist, Washington, D.C. Dr. Szu's current research involves
wavelet transforms, character recognition, and constrained optimization
implementable on a superconducting optical neural network computer. He is
also involved with the design of a sixth-generation computer based on the
confluence of neural networks and new optical data base machines. Dr. Szu is
also a technical representative to DARPA and consultant to ONR on neural
networks and related research, and has been engaged in plasma physics and
optical engineering research for the past 16 years. He holds five patents,
has published about 100 technical papers, plus two textbooks. Dr. Szu is an
editor for the journal Neural Networks and currently serves as the President
of the International Neural Network Society.
LECTURER AND UCLA FACULTY REPRESENTATIVE
John D. Villasenor, PhD
Assistant Professor, Department of Electrical Engineering, School of
Engineering and Applied Science, UCLA. Dr. Villasenor has been instrumental
in the development of a number of efficient algorithms for a wide range of
signal and image processing tasks. His contributions include application-
specific optimal compression techniques for tomographic medical images,
temporal change measures using synthetic aperture radar, and motion
estimation and image modeling for angiogram video compression. Prior to
joining UCLA, Dr. Villasenor was with the Radar Science and Engineering
section of the Jet Propulsion Laboratory where he applied synthetic aperture
radar to interferometric mapping, classification, and temporal change
measurement. He has also studied parallelization of spectral analysis
algorithms and multidimensional data visualization strategies. Dr.
Villasenor's research activities at UCLA include still-frame and video
medical image compression, processing and interpretation of satellite remote
sensing images, development of fast algorithms for one- and two-dimensional
spectral analysis, and studies of JPEG-based hybrid video coding techniques.
DAILY SCHEDULE
Monday (Szu)
Introduction to Wavelet Transform (WT)
- Formulation of small group projects using WT
Review of WT
- Historical: Haar 1910, Gabor 1942, Morlet 1985
- Definition of WT
Applications: Principles by Dimensionality, Functionality
- Signal processing: oil exploration, heart diagnosis
- Image processing: lossless compression, fingerprint
- Telecommunication: multi-medium wide-band ISDN
Discrete and Continuous Mathematics of WT
- Example: Haar WT and Daubechies WT
- Complexity Pyramid Theorem:
- Connection with continuous WT
- WT normalizations, causality conditions
Tuesday Morning (Villasenor)
Discrete Wavelet Transforms
- Background: motivation, multiresolution analysis, Laplacian pyramid coding
- Brief review of relevant digital signal processing concepts/notation
- Discrete wavelet transforms in one dimension: conceptual background, QMF
filter banks, regularity, examples
Tuesday Afternoon (Villasenor and Szu)
Computer Laboratory Demonstration
- Sound compression
- Adaptive speech wavelet code
- Image transforms using wavelets
Wednesday (Szu)
Adaptive Wavelet Transform
- Practical examples: ears, eyes
- Mathematics of optimization
- Applications: cocktail party effect, hyperacuity paradox
Examples: Superposition Mother Wavelets
- For phonemes
- For speaker ID
- For mine field
Nonlinear WT Applications: Soliton WT Kernel
- Practical examples: ocean waves, cauchy sea states
- Paradigms for solving nonlinear dynamics
Thursday (Villasenor)
Discrete Wavelet Transforms II
- Wavelet filter design: ensuring regularity, tradeoffs in filter length,
filter evaluation criteria
- 2D wavelet transforms and applications: extension of wavelets to two
dimensions, computational and practical considerations
- Image compression: techniques for coding of wavelet transforms, comparison
with JPEG, extension to video coding
- Future trends in image processing using wavelets
Friday (Szu)
Comparison
- Quadrature mirror filter vs. perfect inverse image filter
Regularity, Decimation, Sampling theorem
WT Implementation Issues
- Optical WT: Real-time image compression and transmission
- WT chips: WT butterfly
Advanced Applications in WT
- Virtual reality
- Environmental representation: surveillance planning
- Real-time techniques
Problem-Solving Methodology
- Four principles for creative research
Research Project Presentations
- Signal processing groups
- Image processing groups
- Implementation groups
Date: March 7-11 (Monday through Friday)
Time: 8 am-5 pm (subject to adjustment after the first class meeting),
plus optional evening sessions, times to be determined.
Location: Room G-33 West, UCLA Extension Building, 10995 Le Conte Avenue
(adjacent to the UCLA campus), Los Angeles, California
Course No. Engineering 867.121
Fee: $1495, includes course materials
To reserve a place in this course and/or request an application form, call
the UCLA Extension Short Course Program Office at (310) 825-3344; FAX (310)
206-2815.
--------------------------- Topic #5 -----------------------------------
From: P. S. Krishnaprasad, University of Maryland
Subject: Two papers available
Abstracts of two Papers in the 27th Annual Asilomar Conference on Signals, Systems and
computers, Nov 1 - Nov 3, 1993, Asilomar, CA.
If interested in a copy, send e-mail to [email protected]. Include your
address/affiliation.
P. S. Krishnaprasad Tel: (301)-405-6843 (work)
Department of Electrical Engineering &
Institute for Systems Research Fax: (301)-405-6707
A.V. Williams Building - Rm 2233 Internet: [email protected]
University of Maryland, College Park, MD 20742.
---
Orthogonal Matching Pursuit: Recursive Function Approximation
with Applications to Wavelet Decomposition
Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad
Abstract
In this paper we describe a recursive algorithm to
compute representations of functions with respect to nonorthogonal
and possibly overcomplete {\em dictionaries} of elementary
building blocks {\em e.g.\ \/} affine (wavelet) frames.
We propose a modification to the Matching Pursuit algorithm
of Mallat and Zhang (1992) that maintains full backward orthogonality
of the residual (error) at every step and thereby leads to improved
convergence.
We refer to this modified algorithm as Orthogonal Matching
Pursuit (OMP). It is shown that all additional computation required
for the OMP algorithm may be performed recursively.
---
A Fast Recursive Algorithm for System Identification and Model
Reduction Using Rational Wavelets
Y. C. Pati, R. Rezaiifar, P.S. Krishnaprasad, and W. P. Dayawansa
Abstract
In earlier work [Pati and Krishnaprasad 1992]
it was shown that rational wavelet frame decompositions
of the Hardy space H2 may be used to efficiently capture time-frequency
localized behavior of stable linear systems, for purposes of system
identification and model-reduction. In this paper we examine the
problem of efficient computation of low-order rational wavelet
approximations of stable linear systems.
We describe a variant of the Matching Pursuit algorithm
[Mallat and Zhang 1992] that utilizes successive projections onto
two-dimensional subspaces to construct rational wavelet approximants.
The methods described here are illustrated by means of
both simulations and experimental results.
--------------------------- Topic #6 -----------------------------------
From: [email protected] (Milos Doroslovacki)
Subject: Ph.D. abstract
PH.D. ABSTRACT
TITLE: Discrete-Time Signals: Uncertainty Relations, Wavelets, and Linear
System Modeling
AUTHOR: Milos Doroslovacki
ADVISOR: H. Howard Fan
GRANTING INSTITUTION: University of Cincinnati
ACCEPTANCE DATE: November 12, 1993
INFORMATION: Contact the author at Department of Electrical and Computer
Engineering, University of Cincinnati, Cincinnati,
Ohio 45221-0030, Tel: (513)556-6297, Fax: (513)556-7326,
Email: [email protected]
ABSTRACT
A generalized uncertainty relation of the Heisenberg's type for signals is
derived and optimal signals, which reach the uncertainty limit are found. The
requirement for convolution invariance between optimal signals offers, as
unique solutions, the classical uncertainty relation for continuous-time
signals and a new one for the discrete-time case. Optimal discrete-time signals
are obtained and related in a limit case to Gaussian continuous-time signals.
The discrete-time uncertainty relation is given for any point in the
time-frequency plane. Second moments in time and frequency involved in the
relation, and whose product gives a measure of joint time-frequency
localization, are physically interpreted. A few more uncertainty relations
involving ordinary second moments are found.
Discrete-time wavelets are presented in a systematic and consistent way.
Sufficient and necessary conditions for different types of orthogonality,
relations to continuous-time wavelets, shift invariance of wavelet
representations, and relations to binary subband decomposition/reconstruction
of signals are addressed. Second moments characterizing localization in time
and frequency are found for a discrete-time scaling function generated by an
optimal signal. In a limit case, discrete-time scaling functions and wavelets
generated by optimal signals, tend towards continuous-time Gaussian signals. It
is possible to diminish the asymmetry of orthonormal continuous-time wavelets
by using the second moment in time as a criterion for the choice of
discrete-time wavelet generating filters.
Theoretical settings for the modeling of discrete-time linear systems by
wavelets are derived in time-invariant and time-varying cases. System
identification minimizing the mean square output error is studied. Optimal
coefficients and the corresponding minimum of the error are found. Least mean
square adaptive filtering algorithms are derived for on-line filtering and
system identification. Theoretically and by simulations the advantages of using
wavelet-based filtering are shown: faster convergence, smaller output error,
easy interpretation of modeling error. In the time-varying case adaptive
coefficients can tend to constants. A system-identification adaptive approach
is built for on-line and simultaneous detection and range and velocity
estimation of targets in radar and sonar applications.
--------------------------- Topic #7 -----------------------------------
From: Ulrich Ruede <[email protected]>
Subject: Book announcement: Multilevel adaptive methods
BOOK ANNOUNCEMENT
I am pleased to announce the publication of my book:
MATHEMATICAL AND COMPUTATIONAL TECHNIQUES FOR
MULTILEVEL ADAPTIVE METHODS
Ulrich Ruede
Frontiers in Applied Mathematics, Vol. 13,
SIAM, Philadelphia, 1993, ISBN 0-89871-320-X
140 pages, includes 116 bibliographical references and an index
List price: $23 (SIAM members: $18.40)
In this monograph I attempt to present a unified approach to the
development of multilevel adaptive methods for the solution of partial
differential equations, including
* a rigorous mathematical foundation based on results in the theory
of function spaces and in approximation theory
* algorithmic aspects including the
multilevel adaptive relaxation method
* software develoment issues and data structures using the
principles of object oriented programming
Though the book does not explicitly use wavelets, the theoretical
foundation is closely related to wavelet techniques so that it
should be interesting to those using wavelets for the solution of
differential equations.
The book can be ordered directly from SIAM (e-mail: [email protected])
Ulrich Ruede
Institut fuer Informatik, Technische Universitaet
D-80290 Muenchen, Germany
e-mail: [email protected]
Fachbereich Mathematik, Technische Universitaet Chemnitz-Zwickau
D-09009 Chemnitz, Germany
e-mail: [email protected]
--------------------------- Topic #8 -----------------------------------
From: Arnaldo Batista ( [email protected])
Subject: Looking for AWARE INC
Dear Wavelet Readers:
I need to get in touch with the american company AWARE INC., but haven't
been able to get the adress here. Could you please give me the adress, Fax
No.and Email? If any of AWARE's staff is reading this message would you
please mail me information about AWARE's wavelet software and papers?
Many Thanks
Arnaldo Batista
University of Sussex
Biomedical Engineerig - EAPS No. 3
Brighton BN1-9QT
ENGLAND
FAX +44-273-678452 Tel. +44-273-606755 ext 2856
--------------------------- Topic #9 -----------------------------------
From: [email protected]
Subject: Question: application of Wavelets in EEG?
Dear Waveletters,
Is there anyone of You who is interested in the application of Wavelets
in EEG? Or could anyone suggest the papers where this topic appears?
Thanks a lot in advance.
[email protected]
--------------------------- Topic #10 -----------------------------------
From: [email protected]
Subject: Post-doctoral position available at University of Toronto
Our research group in the Department of Radiology at the University of Toronto
in Toronto, Canada has a post-doctoral position for someone interested in
studying the use of wavelets to encode spatial position in magnetic resonance
(MR) images. We hope that the position appeals to someone with a very good
understanding of wavelet theory and some experience at performing experiments.
Our group has access to several clinical and research MR systems in Toronto and
we are also part of a large medical imaging group. Anyone interested in
considering this post-doctoral position is encouraged to contact Dr. Michael
Wood by E-Mail at [email protected] or by fax at (416) 480-5714.
--------------------------- Topic #11 -----------------------------------
From: Akram Aldroubi, [email protected]
Subject: CFP: Workshop on Wavelets in Medicine and Biology.
Workshop on Wavelets in Medicine and Biology, November 1-2, 1994,
Baltimore, Maryland.
Chairs: Akram Aldroubi and Michael Unser,
Biomedical Engineering and Instrumentation Program,
National Institutes of Health.
The workshop will be part of the International Conference of the IEEE
Engineering in Medicine and Biology Society (EMBS), which is the largest
annual meeting in biomedical engineering. It will consist of tutorial
talks by wavelet specialists, and a series of contributed papers focusing
on new developments and applications in biology and medicine. This forum
will encourage interdisciplinary exchanges and identify promising
directions of research and applications.
Participation: If you are interested in attending the workshop, send an
email to us at [email protected] There will be a special call for
papers.
Main Speakers (tutorials): J. Benedetto, S Mallat, M. Vetterli.
Program committee: C. Berenstein, R. Coifman, R. DeVore, H. Eden, H.
Feichtinger, P. Flandrin, B. Jawerth, C. Karl, A. Laine, Z. Nashed, U.
Ruttiman, S. Schiff, L. Yaroslavsky.
-------------------- End of Wavelet Digest -----------------------------
|
| 1409.31 | a little note on wavlets | STAR::ABBASI | only 11 days left... | Sun Dec 05 1993 02:25 | 30 |
| From: [email protected] (Arnold G. Gill)
Newsgroups: sci.physics
Subject: Re: What are Wavlets?
Date: 3 Dec 93 12:59:21 -0700
In article <[email protected]>, [email protected] (Ron Stockermans) writes:
>
> What are wavelets?
> How do they differ from waves?
> What applications are there for Wavelet Theory?
In short, wavelets are an extension of Fourier theory, but
requiring the use of a kernel with special properties.
One problem with Fourier theory is that it does a time spectral
analysis well, but loses *all* information on spatial positions.
Wavelet theory tries to remedy this by the use of kernels which give
good spatial as well as spectral resolution. The wavelet is localized
in space, but can be scaled both in size and position, giving the
combined time-space resolution.
As for uses, there are many in signal analysis, especially where
the exact time/position of a signal is as important as the signals
spectral composition. I have used wavelets to investigate the scaling
of turbulent regions of space, in attempt to determine the
three-dimensional structure of astronomical objects. There are many
other uses, and many published papers and books.
---
Arnold G. Gill -- astrophysician at play [email protected]
|
| 1409.32 | Wavelet Digest, Vol. 2, Nr. 18. | STAR::ABBASI | only 11 days left... | Tue Dec 07 1993 01:58 | 1065 |
| Subj: Wavelet Digest, Vol. 2, Nr. 18.
Wavelet Digest Monday, December 6, 1993 Volume 2 : Issue 18
Today's Editor: Wim Sweldens
[email protected]
Today's Topics:
1. Reply to Volume 2 : Issue 17, Topic 9.
2. CFP: IEEE-SP Intern. Symp. Time-frequency and time-scale analysis.
3. Wavelets in Japan
4. Wavelet Toolbox for Matlab
5. Two papers available
6. Question: Costfunctions for best basis selection in wave packets
7. Contents: Algorithms for Approximation III
8. First IEEE international conference on image processing
9. FBI Wavelet/Scalar Quantization image coding standard
10. Wavelet/EEG paper
11. Question: Application of Wavelets to Coupled DE?
12. Re: CFP: Mathematical Imaging, Wavelet Applications
13. Question: looking for image sequences
14. Preprint available: Complex Daubechies wavelets
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
To unsubscribe, e-mail with "unsubscribe" followed by your e-mail
address as subject. To change address, unsubscribe and resubscribe.
Archive site, preprints, references and back issues:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directories /pub/wavelet and /pub/imi_93.
Gopher: bigcheese.math.scarolina.edu.
Current number of subscribers: 3045
--------------------------- Topic #1 -----------------------------------
From: [email protected] (Leonard J. Trejo)
Subject: Reply to Volume 2 : Issue 17, Topic 9.
A mailing list, "SP-EEG," dedicated to signal processing and EEG,
including wavelet applications, has been formed by Vince Samar. I'm
attaching three announcements from the list. One explains how to
subscribe, the other two have some wavelet references.
Vince is out of the country now, so it may take a while for new
subscriptions to be processed.
LJT
From: "Vince Samar" <[email protected]>
(4) Query Summary: Neurometric Wavelet Analysis Papers
I'm building a bibliography of papers that have applied the wavelet
transform in one way or another to the analysis of neurometric
signals. I have not found many. The following is the list I currently
have. If anyone knows of additional references on this topic, I'd
appreciate receiving them (better yet, send me the papers!). I'll post
a final reference list when I've got one.
Neurometric Wavelet Papers:
Bartnik, E.A., Blinowska, K.J. and Durka, P.J. (1991). Single evoked
potential reconstruction by means of wavelet transform. Biological
Cybernetics, 67,175-181.
Bartnik, E.A. and Blinowska, K.J. (1992). Wavelets: New method of evoked
potential analysis, Medical and Biological Engineering and Computing, 30,
125-126.
Chin, S. and Kassam, S. A. (1993). Analysis of EEG signals using wavelet
decomposition. Proceedings of the Conference on Information Sciences
and Systems. Johns Hopkins University, Baltimore, March 24-26.
Hanrahan, H. E. (1990). Extraction of features in auditory brainstem
response (ABR) signals. COMSIG 90, Proceedings of the third South
African Conference on Communications and Signal Processing, IEEE catalog
number 90TH0314-5/90, 61-66.
Hanrahan, H. E. (1992). A family of wavelets which are dilatable by simple
IIR filters. International Conference on Acoustics, Speech, and Signal
Processing, IEEE catalog number 0-7803-0532-9/92, March 23-26, San
Francisco, pp. IV_285-IV_288.
Samar, V. J., Raghuveer, M. R. & Swartz, K. (in press). Multiresolution
analysis of Event-Related Potentials by wavelet decomposition. In
V. J. Samar and D. L. Molfese (Eds.), Contemporary Trends in
Electroencephalographic, Magnetoencephalographic, and Event
Related Potential Waveform Analysis, Brain and Cognition.
Len Trejo also has a very nice recent paper out on the use of wavelet
decompositions in conjunction with neural network classification of
ERPs. However, I don't have the reference handy right now. I'll
include it in the final list that I post.
Additional neurometric wavelet papers:
Cohen, A. (1986). Biomedical Signal Processing. Volume II.
Compression and Automatic Recognition. Boca Raton, FL: CRC Press.
[Discusses wavelet detection methods *not* based on the wavelet
transform.]
Grusser, O.-J. and Landis, T. (1991). Visual agnosias and other
disturbances of visual perception and cognition. In J. Cronley-Dillon
(Ed.), Vision and visual dysfunction, Volume 12. pp. 240-247,
Boca Raton FL: CRC Press.
[Wavelets applied to VEP. Wavelet functions not clearly defined
here, but appear to be undecimated & symmetric (2nd derivative of
a Gaussian)]
Tang, Z., and Ishii, N. (1993). The recognition system with two
channels at different resolution for detecting spike in the human's
EEG. IECE Transactions on Information and Systems, E76-D,
377-387.
Trejo, L. J. (1993a). Pattern recognition neural networks for human
event-related potentials (ERP): A comparison of feature extraction
methods. Proceedings of the Artificial Neural Network Conference. Navy
Personnel Research and Development Center. San Diego, February 2-3.
Trejo, L. J. and Shensa, M. J. (1993b, in press). Linear and neural
network models for predicting human signal detection performance from
event-related potentials: A comparison of the wavelet transform with
other feature extraction methods. Proceedings of the 1993 Simulation
Technology Multiconference, San Francisco, CA, November 7-10. San
Diego: Society for Computer Simulation.
LJT
--------------------------- Topic #2 -----------------------------------
From: [email protected]
Subject: CFP: IEEE-SP Intern. Symp. Time-frequency and time-scale analysis.
FIRST ANNOUNCEMENT AND CALL FOR PAPERS
IEEE-SP International Symposium on
TIME-FREQUENCY AND TIME-SCALE ANALYSIS
Oct. 25-28, 1994
Adam's Mark Hotel, Philadelphia, PA, USA
Sponsored by the IEEE Signal Processing Society
General and Organization Chair
Moeness G. Amin
Villanova University, Dept. of ECE, Villanova, PA 19085
[email protected]
Technical Program Chair
Patrick Flandrin
Ecole Normale Superieure de Lyon, France
[email protected]
Technical Program Committee
Les Atlas (Univ. of Washington), G. Faye Boudreaux-Bartels (Univ. of Rhode
Island), Douglas Jones (Univ. of Illinois), Stephane Mallat (New York
Univ.), Nenad Marinovich (City Univ. of New York), Olivier Rioul (CNET),
Martin Vetterli (Univ. of California)
Finance
Robert Yantorno
Temple University, Dept. of EE, Philadelphia, PA 19122
Publicity
Theodore Posch
Hughes Aircraft Co., Bldg 604 M/S B 114,
1901 W. Malvern Ave., Fullerton, CA 92634
Proceedings
Athina Petropulu
Drexel University, Dept. of EE, Philadelphia, PA 19104
Meeting Arrangements and Registration
Ralph Nadel, Palisades Institute,
Phone: (212) 620-3721
The 1994 IEEE Symposium on Time-Frequency and Time-Scale (Wavelet) Analysis
will be held October 25-28 in the beautiful and historical city of
Philadelphia, PA.
This workshop will consist of a half day of tutorials ("New concepts in
quadratic time-frequency analysis," by Franz Hlawatsch (Univ. of Vienna,
Austria) and "Wavelets and adaptive signal analysis and modeling," by Ahmed
Tewfik (Univ. of Minnesota)), followed by two and half days of technical
sessions. (Conference proceedings will be distributed at the symposium.)
Papers are solicited for technical sessions on the theory and applications
of time-frequency and time-scale methods which include the following
topics:
% Theory and Analysis
- algorithms
- linear signal decompositions
- multiscale models
- quadratic energy distributions
- signal-dependent methods
- time-frequency signal processing
- time-frequency localization and filtering
- time-scale signal processing
- time-varying spectra
- wavelet transforms and approximations
- wavelet and filterbanks
- other (specify)
% Applications
- image processing
- manufacturing sensor signal processing
- medical signal processing
- music analysis and synthesis
- physics, geophysics, and astronomy
- remote sensing
- sonar and radar signal processing
- speech and audio
- vibration analysis
- other (specify)
Prospective authors are invited to submit 4 copies of extended summaries
of no more than 4 pages for review. The top of the first page of the
summary should include the name, affiliation, and full
address of each author. The author to whom correspondence should be
directed, along with his/her telephone and fax numbers, and complete e-mail
address must be clearly indicated on the cover page.
The authors are asked to indicate one or more of the above categories that
best describe the topic of the paper.
Send papers to Prof. Moeness Amin (address above).
SCHEDULE
Submission of extended summary May 6, 1994
Notification of acceptance July 3, 1994
Submission of photo-ready paper September 9, 1994
--------------------------- Topic #3 -----------------------------------
From: [email protected]
Subject: Wavelets in Japan
Reports on some wavelet seminars, meetings, etc. in Japan are available by
anonymous ftp from cs.arizona.edu in: japan/kahaner.reports
"wavelets.91" 15 Nov 1991 First SIAM Japan wavelet seminar
"wavelets.192" 4 Feb 1992 Audio Visual Research Information Group Seminar
"j-siam9.93" 24 Sept 1993 SIAM Japan Fall Meeting, Session on Wavelets
"wavelets.93" 28 Oct 1993 Second SIAM Japan wavelet seminar
The first two are now slightly outdated; the third also
gives a brief overview of SIAM Japan and its activities.
The directory kahaner.reports is a gigantic library of reports on scientific
activities here in Japan. Readers may find other reports of interest.
Warning: I have received notes from readers that some of the details
in my reports are not quite right. Pls. accept my apologies, but also
pls. keep in mind that these reports are usually written within 24-48
hours of the event(s) described and are meant to alert interested
readers and to give out follow-up references rather than to serve as a
detailed academic documents.
--------------------------- Topic #4 -----------------------------------
From: Carl Taswell <[email protected]>
Subject: Wavelet Toolbox for Matlab
WavBox
(Wavelet Toolbox for Matlab)
Copyright (c) 1991-93 Carl Taswell
All rights reserved.
Permission is granted for use and non-profit distribution providing that
this notice be clearly maintained. You must read and consent to all terms and
conditions detailed below prior to any use of this software package. The right
to distribute any portion for profit or as part of or application in any
commercial product is specifically reserved for the author.
If you download any of the WavBox software, please register your use of the
software by sending me a brief email message specifying your area of research
and application of wavelet transform analysis.
You may send bug reports, enhancement requests, and any other questions via
email to "[email protected]". WavBox is available by anonymous ftp
from the directory "/pub/taswell" at the site "simplicity.stanford.edu"
which can also be accessed by the numeric id "36.8.0.104"
Matlab is a trademark of The MathWorks Inc.
Non-exclusive License Terms and Conditions:
WavBox 1.0 - 3.0 software is Copyright (C) 1991-93 Carl Taswell.
All rights are reserved by the author.
However, you may use this software for scholarly and research purposes only,
free of charge, subject to the following terms and conditions:
1) You do not use this software for any commercial for-profit venture of any
kind nor modify this software for subsequent commerical for-profit use of any
kind without obtaining the prior written consent of the author.
2) You do not make modifications to the software for your own use without
obtaining permission from the author via email at "[email protected]".
3) You may redistribute the software to other users but you cannot remove
any copyright notices including this file from the distribution package, nor
modify the software in any way prior to redistribution, nor can you charge any
fees for redistributing the software.
4) The software package WavBox and its Copyright (c) 1991-93 Carl Taswell
notice must be properly cited in any publication reporting results obtained
from application and use of this software package.
5) This software is provided "as is" without warranty of any kind by Carl
Taswell. In no event shall Carl Taswell be liable for any losses or for any
indirect, special, punitive, exemplary, incidental, or consequential damages
arising from the use or misuse, and possession or performance, of this software.
WavBox 1.0
Copyright (c) 1991 Carl Taswell
***File called wavbox10.tar.Z***
Contains MatLab 3.5 m-files to compute:
discrete wavelet transform
for arbitrary length signals,
using compact orthogonal wavelets,
with several different convolution versions;
self-running demonstration file called
demodwt;
***File called FDTWT.ps.Z***
Contains companion tech report entitled:
Wavelet Tranform Algorithms for Finite-Duration Discrete-Time Signals.
This tech report was written in 1991, revised in 1993, and will be published in
1994 in the Association for Computing Machinery's Transactions on Mathematical
Software. For continued work related to the 1991 tech report, see "Wavelet
Transform Algorithms for Finite-Duration Discrete-Time Signals" pages 221-224
in Progress in Wavelet Analysis and Applications: Proceedings of the International
Conference "Wavelets and Applications" Toulouse France June 1992, edited by Yves
Meyer and Sylvie Roques; part II of the 1991 tech report is currently being revised.
WavBox 1.2
Copyright (c) 1993 Carl Taswell
***File called wavbox12.tar.Z***
Contains MatLab 4.1 m-files to compute:
same as WavBox 1.0 except upgraded to MatLab 4.1
with additional changes to several of the convolution versions;
WavBox 2.0
Copyright (c) 1992 Carl Taswell
***File called wavbox20.tar.Z***
Contains MatLab 3.5 m-files to compute:
discrete wavelet transform
for power-of-2 length signals,
using compact orthogonal and biorthogonal wavelets,
with several different convolution versions;
the wavelet shrinkage and thresholding estimation methods
of Donoho and Johnstone;
self-running demonstration files called
demodwt, figadapt, figideal;
WavBox 3.0
Copyright (c) 1993 Carl Taswell
***File called wavbox30.tar.Z***
Contains MatLab 4.1 m-files to compute:
discrete wavelet transform
for power-of-2 length signals,
using compact orthogonal and biorthogonal wavelets,
with several different convolution versions;
wavelet packet decompositions
with adaptive selection of best-basis and best-level bases;
cosine packet decompositions;
with adaptive selection of best-basis and best-level bases;
time-frequency atom decompositions by matching pursuit
using wavelet packets with backfitting;
self-running demonstration files called
demowb (DemoWavBox master which calls other demos),
demodwt, demodpd, demowpmp;
WavBox 4.0
Currently under development
Test versions available by special request
Contains MatLab 4.1 m-files for:
all transforms and decompositions from WavBox 1-3 combined
with a new integrated architecture and uniform conventions;
extensions to arbitrary length signals and arbitrary (non-square,
non-power-of-2) size 2-D signals and images;
additional transforms such as overcomplete pyramids and
continuous wavelet transform;
replacement of the greedy matching pursuit algorithm by the more
efficient orthogonal matching pursuit algorithm of Pati et al;
comprehensive set of visual displays and plots;
interactive graphical user interface controls;
detailed documentation for each m-file;
This document written September 28, 1993; last modified November 24, 1993.
--------------------------- Topic #5 -----------------------------------
From: Debao Chen, The University of Texas at Austin, <[email protected]>.
Subject: Two papers available
The following two papers are parts of my dissertation. If interested, please
send me e-mail.
Debao Chen
Department of Mathematics
The University of Texas at Austin
Austin, TX 78712
e-mail: [email protected]
Extended Families of Cardinal Spline wavelets
(This paper has been revised several times. The title has been changed two
times. However, this is the final version and will appear in Applied and
Computational Harmonic Analysis>)
Abstract:
Cardinal spline pre-wavelets $\psi_m$ and $\eta_m$ are well known.
In this paper we present an extension of cardinal spline pre-wavelets
and construct non-orthogonal cardinal spline
wavelet systems. Both compactly supported spline wavelets
$\psi_{m,l;c}$ and globally supported spline
wavelets $\eta_{m,l;c} (x) =L_{m+l;c}^{(l)} (2x-1)$
are given. When $l=m $ and $c=0$,
we obtain cardinal spline pre-wavelets $\psi_m$ and $\eta_m$.
As $l$ decreases, so does the support of the wavelet $\psi_{m,l;c}$. When $l$
increases, the smoothness of the dual wavelets $\widetilde {\psi}_{m,l;c}$
and $\widetilde {\eta}_{m,l;c}$ improves. When $c=0$ the wavelets
$\psi_{m,l;0} = \psi_{m,l} $ and $\eta_{m,l;0} = \eta_{m,l} $
are symmetric or anti-symmetric.
The dual wavelets
$\widetilde{\psi}_{m,l;0} = \widetilde{\psi}_{m,l}$ and
$\widetilde{\eta}_{m,l;0}= \widetilde{\eta}_{m,l}$ are $l^{\roman{th}}$-order
spline functions. We give an explicit analytic formula of the dual wavelet
$\widetilde{\psi}_{m,l}$.
The wavelets $\psi_{m,l} $ and $\eta_{m,l} $ have properties similar to
spline pre-wavelets $\psi_m$ and $\eta_m$
except for the orthogonality among the wavelet spaces.
Each wavelet is
constructed by spline multiresolution analysis.
The dual MRA's are given.
---
Spline Wavelets of Small support
(To appear in SIAM J. Anal.)
Abstract:
Every $m^{\roman{th}}$-order spline wavelet
is a linear combination of the functions $\{ N_{m+l}^{(l)} (2 x - j), j\in
\bold Z \} $.
In this paper we prove that the single function $N_{m+l}^{(l)} (2 x)$,
or $N_{m+l}^{(l)} (2 x - 1)$
is a wavelet when $m$ and $l$ satisfy some mild conditions.
As $l$ decreases, so does the support of the
wavelet.
When $l$ increases,
the
smoothness of the dual wavelet
improves.
Each wavelet is constructed by spline multiresolution analysis.
The dual multiresolution analyses are given.
--------------------------- Topic #6 -----------------------------------
From: [email protected] (Peter Schuett )
Subject: Question: Costfunctions for best basis selection in wave packets
Dear waveletters,
Does anyone know which cost functions for the best basis selection in wave
packets are already published?
Thanks a lot.
Peter Schuett
e-mail: [email protected]
--------------------------- Topic #7 -----------------------------------
From: [email protected] (Daniel Baltzer)
Subject: Contents: Algorithms for Approximation III
Algorithms for Approximation III
Editors:
M.G. Cox,
Division of Information Technology & Computing,
National Physical Laboratory,
Teddington,
Middlesex TW11 0LW,
UK
J.C. Mason,
Applied and Computational Mathematics Group,
RMCS (Cranfield),
Shrivenham, Swindon,
Wiltshire SN6 8LA,
UK
Algorithms for Approximation III is published as Volume 5, 1993 of
Numerical Algorithms, ISSN 1017-1398, a primary journal covering all
aspects of numerical algorithms: theoretical results, implementation,
numerical stability, complexity, subroutines and applications.
Editor-in Chief:
Claude Brezinski
Laboratoire d'Analyse Numerique et d'Optimisation,
UFR IEEA - M3,
Universite de Sciences et Technologies de Lille,
59655 Villeneuve d'Ascq Cedex, France.
E-mail: [email protected]
Postal Address: Paris Drouot BP 18, 75433 Paris Cedex 09, France
Contents Numerical Algorithms volume 5, Algorithms for Approximation III
Part I Development of Algorithms
1. Spline Approximation and Applications
C. de Boor, On the evaluation of box splines
J.C. Mason, G. Rodriguez and S. Seatzu, Orthogonal splines based on
B-splines - with applications to least squares, smoothing and
regularization problems
B.L. MacCarthy, C.S. Syan and M. Caulfield-Browne, Splines in motion - An
introduction to MODUS and some unresolved approximation problems
G. Plonka, An efficient algorithm for periodic Hermite spline interpolation
with shifted nodes
L. Traversoni, An algorithm for natural spline interpolation
R. van Damme, An algorithm for determining the approximation orders of
multivariate periodic spline spaces
2. Radial Basis Functions and Applications
I. Barrodale, R. Kuwahara, R. Poeckert and D. Skea, Side-scan sonar image
processing using thin plate splines and control point matching
M.J.D. Powell, Truncated Laurent expansions for the fast evaluation of thin
plate splines
D. Handscomb, Local recovery of a solenoidal vector field by an extension
of the thin-plate spline technique
3. Interpolation
M.G. Cox, Reliable determination of interpolating polynomials
J.-P. Berrut, A closed formula for the Chebyshev barycentric weights of
optimal approximation in H2
M. Dohlen and M. Floater, Iterative polynomial interpolation and ata compression
J. Prestin, Lagrange interpolation on extended generalized Jacobi nodes
4. Multivariate Approximation
I.J. Anderson, M.G. Cox and J.C. Mason, Tensor-product spline interpolation
to data on or near a family of lines
L. Lenarduzzi, Practical selection of neighbourhoods for local regression
in the bivariate case
S. Thiry, Extremal signatures for bivariate Chebyshev approximation problems
5. Generic Approximation
W. Dahmen, Decomposition of refinable spaces and applications to operator
equations
W.A. Light, Techniques for generating approximations via convolution kernels
G.A. Watson, On matrix approximation problems with Ky Fan k norms
6. Nonlinear Approximation
I.D. Coope and P.R. Graves-Morris, The rise and fall of the vector epsilon
algorithm
B. Fischer and J. Modersitzki, An algorithm for complex linear
approximation based on semi-infinite programming
M.-P. Istace and J.-P. Thiran, On computing best Chebyshev complex rational
approximants
K. Jonasson, A projected conjugate gradient method for sparse minimax problems
J. Williams and Z. Kalogiratou, Nonlinear Chebyshev fitting from the
solution of ordinary differential equations
7. Constrained Approximation
M.T. Bozzini and C. Paracelli, An algorithm for constrained smoothing functions
R.H. Chan and P.T.P. Tang, Constrained minimax approximation and optimal
preconditioners for Toeplitz matrices
G.H. Elliott, Least squares data fitting using shape preserving piecewise
approximations
8. Smoothing and Regularization
C.A. Micchelli, Optimal estimation of linear operators from inaccurate
data: a second look
G. Rodriguez and S. Seatzu, Approximation methods for the finite moment problem
K.W. Bosworth and U. Lall, An L1 smoothing spline algorithm with cross
validation
D. de Falco, KM. Frontini and L. Gotusso, A unifying approach to the
regularization of Foutier polynomials
PART II. APPLICATIONS
9. Integrals and Integral Equations
C. Barone and E. Venturino, On the numerical evaluation of Cauchy transforms
L. Brutman, An application of the generalized alternating polynomials to
the numerical solution of Fredholm integral equations
C. Dagnino, V. Demichelis and E. Santi, An algorithm for numerical
integration based on quasi-interpolating splines
M. Tasche, Fast algorithms for discrete Chebyshev-Vandermonde transforms
and applications
10. Metrology
B.P. Butler and A.B. Forbes, An algorithm for combining data sets having
different frames of reference
P. Ciarlini and F. Pavese, Application of special reduction procedures to
metrological data
M.G. Cox, P.M. Harris and D.A. Humphreys, An algorithm for the removal of
noise and jitter in signals and its application to picosecond electrical
measurement
R. Drieschner, Chebyshev approximation to data by geometric elements
A.B. Forbes, Generalised regression problems in metrology
H.-P. Helfrich and D. Zwick, A trust region method for implicit orthogonal
distance regression
11. Geometric Modelling
E. Galligani, C1 surface interpolation with constraints
B.J. Hogervorst and R. van Damme, Degenerate polynomial patches of degree
11 for almost GC2 interpolation over triangles
P.R. Pfluger and M. Neamtu, On degenerate surface patches
S. Rippa, Scattered data interpolation using minimum energy Powell-Sabin
elements and data dependent triangulations
12. Applicated in Other Disciplines
E. Grosse, Approximation in VLSI simulation
R. Model and L. Trahms, An inverse problem of magnetic source localization
A. Potchinkov and R. Reemtsen, A globally most violated cutting plane
method for complex minimax problems with application to digital filter
design
V.V.S.S. Sastry, Algorithms for the computation of Hankel functions of
complex order
PART III PANEL DISCUSSION AND WORKING SESSIONS
Working session on splines
Panel discussion on applications of approximation
Working session on metrology
Panel discussion on geometric modelling
Panel discussion on multivariate problems
Panel discussion on parallel processing
Free sample copy Numerical Algorithms, ISSN 1017-1398, available.
Volume 5, 1993, Algorithms for Approximation III, 650 pages, available at
US$ 233.- (institutions) and US$ 126.- (individuals and institutions in
developing countries). All major credit cards accepted!
In the United States please send your order to: J.C. Baltzer AG, Science
Publishers, P.O. Box 8577, Red Bank, NJ 07701-8577
>From all other countries please send your order to: J.C. Baltzer AG,
Science Publishers, Wettsteinplatz 10, CH-4058 Basel, Switzerland.
Fax: +41-61-692 42 62, E-mail: [email protected]
J.C. Baltzer AG, Science Publishers
Asterweg 1A
1031 HL Amsterdam
The Netherlands
tel. +31-20-637 0061
fax. +31-20-632 3651
e-mail: [email protected]
--------------------------- Topic #8 -----------------------------------
From: [email protected] (International Conf on Image Processing Mail Box)
Subject: First IEEE international conference on image processing
Dear Wavelet Friends,
I am sending along the Final Call for Papers for our
conference in the next email.
The conference will include special sessions on wavelets
for image processing, and will feature a tutorial on
wavelets in image processing by Martin Vetterli
Thanks!! Al Bovik
FIRST IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING
November 13-16, 1994
Austin Convention Center, Austin, Texas, USA
CALL FOR PAPERS
Sponsored by the Institute of Electrical and Electronics
Engineers (IEEE) Signal Processing Society, ICIP-94 is the
inaugural international conference on theoretical, experimental
and applied image processing. It will provide a centralized,
high-quality forum for presentation of technological advances and
research results by scientists and engineers working in Image
Processing and associated disciplines such as multimedia and
video technology. Also encouraged are image processing
applications in areas such as the biomedical sciences and
geosciences.
SCOPE:
1. IMAGE PROCESSING: Coding, Filtering, Enhancement,
Restoration, Segmentation, Multiresolution Processing,
Multispectral Processing, Image Representation, Image Analysis,
Interpolation and Spatial Transformations, Motion Detection and
Estimation, Image Sequence Processing, Video Signal Processing,
Neural Networks for image processing and model-based compression,
Noise Modeling, Architectures and Software.
2. COMPUTED IMAGING: Acoustic Imaging, Radar Imaging,
Tomography, Magnetic Resonance Imaging, Geophysical and Seismic
Imaging, Radio Astronomy, Speckle Imaging, Computer Holography,
Confocal Microscopy, Electron Microscopy, X-ray
Crystallography, Coded-Aperture Imaging, Real-Aperture Arrays.
3. IMAGE SCANNING DISPLAY AND PRINTING: Scanning and Sampling,
Quantization and Halftoning, Color Reproduction, Image
Representation and Rendering, Graphics and Fonts, Architectures
and Software for Display and Printing Systems, Image Quality,
Visualization.
4. VIDEO: Digital video, Multimedia, HD video and packet video,
video signal processor chips.
5. APPLICATIONS: Application of image processing technology to
any field.
PROGRAM COMMITTEE:
GENERAL CHAIR: Alan C. Bovik, U. Texas, Austin
TECHNICAL CHAIRS: Tom Huang, U. Illinois, Champaign and
John W. Woods, Rensselaer, Troy
SPECIAL SESSIONS CHAIR: Mike Orchard, U. Illinois, Champaign
EAST EUROPEAN LIASON: Henri Maitre, TELECOM, Paris
FAR EAST LIASON: Bede Liu, Princeton University
SUBMISSION PROCEDURES
Prospective authors are invited to propose papers for lecture or
poster presentation in any of the technical areas listed above.
To submit a proposal, prepare a summary of the paper using no
more than 3 pages including figures and references. Send five
copies of the paper summary along with a cover sheet stating the
paper title, technical area(s) and contact address to:
John W. Woods
Center for Image Processing Research
Rensselaer Polytechnic Institute
Troy, NY 12180-3590, USA.
Each selected paper (five-page limit) will be published in the
Proceedings of ICIP-94, using high-quality paper for good image
reproduction. Style files in LaTeX will be provided for the
convenience of the authors.
SCHEDULE
Paper summaries/abstracts due*: 15 February 1994
Notification of Acceptance: 1 May 1994
Camera-Ready papers: 15 July 1994
Conference: 13-16 November 1994
*For an automatic electronic reminder, send a "reminder please"
message to: [email protected]
CONFERENCE ENVIRONMENT
ICIP-94 will be held in the recently completed state-of-the-art
Convention Center in downtown Austin. The Convention Center is
situated two blocks from the Town Lake, and is only 12 minutes
from Robert Meuller Airport. It is surrounded by many modern
hotels that provide comfortable accommodation for $75-$125 per
night.
Austin, the state capital, is renowned for its natural hill-
country beauty and an active cultural scene. Within walking
distance of the Convention Center are several hiking and jogging
trails, as well as opportunities for a variety of aquatic sports.
Live bands perform in various clubs around the city and at night
spots along Sixth Street, offering a range of jazz, blues,
country/Western, reggae, swing and rock music. Day temperatures
are typically in the upper sixties in mid-November.
An exciting range of EXHIBITS, TUTORIALS, SPECIAL PRODUCT
SESSIONS,, and SOCIAL EVENTS will be offered.
For further details about ICIP-94, please contact:
Conference Management Services
3024 Thousand Oaks Drive
Austin, Texas 78746
Tel: 512/327/4012; Fax:512/327/8132
or email: [email protected]
--------------------------- Topic #9 -----------------------------------
From: Chris Brislawn, Los Alamos National Laboratory
Subject: FBI Wavelet/Scalar Quantization image coding standard
Material related to the FBI's Wavelet/Scalar Quantization (WSQ)
standard (IAFIS-IC-0110) for compression of gray-scale fingerprint
images is available via anonymous ftp from the Los Alamos ftp host
ftp.c3.lanl.gov (128.165.21.64)
Login under the user name "anonymous" and use your e-mail ident
for a password. Material related to the WSQ compression standard
is in the directory pub/WSQ. Here's a list of the current contents:
Directory: Contents:
---------- ---------
print_data sample fingerprint image data
tutorial demo program on the symmetric wavelet transform algorithm
documents papers in electronic format related to the specification
e-memos copies of old electronic memos, labelled by date
In particular, the specification itself is available in WSQ/documents,
along with several supporting technical and expository papers:
File: Title, Date of last revision:
----- -----------------------------
wsqSpec2 "WSQ gray-scale fingerprint image compression specification"
ver. 2.0, 2/16/93. NOTE: missing pictures on p. 16
classify "Classification of symmetric wavelet transforms", 3/22/93.
Includes description of WSQ wavelet transform algorithm.
wsqSynop "The FBI Wavelet/Scalar Quantization Standard for gray-scale
fingerprint image compression", 4/93. Missing pictures.
A friendly synopsis of the complete WSQ algorithm.
bit-alloc "Proposed first-generation WSQ bit allocation procedure,"
9/8/93. Computing scalar quantizer parameters, like "q".
We also have an electronic mailing list for parties interested in
receiving timely updates on developments concerning the standard.
If you'd like to be added to this mailing list, or if you don't have
ftp service or would simply prefer to be sent hard copies of these
papers, please contact me at one of the numbers given below.
NOTE: Please do not ask us for copies of our software; the Los Alamos
WSQ software has been reserved for official use only.
A public WSQ implementation for Windows 3.1 platforms has been written
at Washington University and made available by anonymous ftp, as posted
in Wavelet Digest, vol. 2, no. 14, Oct. 5, 1993. In addition to the
above documents, we can also provide the reference (cited in WD vol. 2,
no. 14) on which the Washington University WSQ implementation is based:
John J. Werner, "IAFIS Use of the Wavelet Scalar Quantization (WSQ)
Compression/Decompression Algorithm," NIST Conference, Dec. 8, 1992.
Send requests to me, as this document is available in hard copy form only.
DISCLAIMER: The Washington University software has not been certified
as complying with the official WSQ gray-scale fingerprint image compression
specification; this memo is for informative purposes only and does not
imply any endorsement or guarantee of said software by the US Government.
Chris Brislawn [email protected]
Los Alamos National Laboratory (505) 665-1165
Computer Research/Applications Group FAX: 665-5220
Mail Stop B-265
Los Alamos, NM 87545-1663
--------------------------- Topic #10 -----------------------------------
From: [email protected] (Leonard J. Trejo)
Subject: Wavelet/EEG paper
Dear Colleague,
Thanks for your interest in our paper on wavelets and EEG. Due to
the large number of reprint requests I've received (over 100), I
will not be able to mail reprints to everyone as I had hoped to.
Instead, I have placed a compressed PostScript copy of the paper,
"simtec.ps.Z," in our public ftp archive on "coral.nprdc.navy.mil" in
directory "pub/neuro." If you can use ftp and can print PostScript,
please get the paper using the following example ftp dialog as a
guide. Be sure to type your own userid/address for the password.
% ftp coral.nprdc.navy.mil
Connected to coral.nprdc.navy.mil.
220 coral FTP server (SunOS 4.1) ready.
Name (coral.nprdc.navy.mil:user): anonymous
331 Guest login ok, send ident as password.
Password:
230 Guest login ok, access restrictions apply.
ftp> cd pub/neuro
250 CWD command successful.
ftp> bin
200 Type set to I.
ftp> get simtec.ps.Z
200 PORT command successful.
150 Binary data connection for simtec.ps.Z (164.94.1.158,1031) (167324 bytes).
226 Binary Transfer complete.
local: simtec.ps.Z remote: simtec.ps.Z
167324 bytes received in 1.3 seconds (1.3e+02 Kbytes/s)
ftp> bye
221 Goodbye.
% uncompress simtec.ps
%
If you don't have access to the uncompress program, there is also
an uncompressed version of the paper, "simtec.ps," in the same ftp
directory.
If you can't use ftp to get the paper, or you can't print PostScript,
please send me another e-mail message and I will mail you a reprint.
If you sent your mailing address before, I should still have it.
If you received this message in error, please accept my apology.
LJT
--------------------------- Topic #11 -----------------------------------
From: T. Lawrence, Applied Research Lab, University of Texas
Subject: Question: Application of Wavelets to Coupled DE?
Dear Wavelet Digest Readers,
Is there anyone who is interested in the application of Wavelets
to the solution of coupled differential equations, or could anyone
suggest papers where this topic appears? The particular application that
interests us is solutions to coupled Helmholtz Equations.
Thanks for your help,
[email protected]
--------------------------- Topic #12 -----------------------------------
From: [email protected]
Subject: Re: CFP: Mathematical Imaging, Wavelet Applications
Conference Title: "Mathematical Imaging: Wavelet Applications in
Signal and Image Processing II"
Part of SPIE`s Annual International Symposium on
Optoelectronic Applied Science and Engineering
July 27-29, 1994
San Diego, California
San Diego Convention Center, Marriot Hotel Marina
Conference Chairs:
Andrew Laine, University of Florida
Michael Unser, National Institutes of Health
Program Committee:
Bjorn Jawerth, University of South Carolina
Martin Vetterli, University of California, Berkeley
Ronald Coifman, Yale University
Stephane Mallat, New York University
Victor Wickerhauser, Washington University
Akram Aldroubi, National Institutes of Health
Charles Chui, Texas A&M University
Arun Kumar, Southwestern Bell Technology Resources
Alan Bovik, University of Texas, Austin
KEYNOTE ADDRESS: Ingrid Daubechies, Princeton University
and AT&T Bell Laboratories
The analysis of signals and images at multiple scales
is an attractive framework for many problems in computer vision,
signal and image processing. Wavelet theory provides
a mathematically precise understanding of the concept of multiresolution.
The conference shall focus on novel applications of wavelet methods of
analysis and processing techniques, refinements of existing methods,
and new theoretical models. When possible, papers should compare
and contrast wavelet based approaches to traditional techniques.
Topics for the conference may include but are not limited to:
- image pyramids
- frames and overcomplete representations
- multiresolution algorithms
- wavelet-based noise reduction and restoration
- multiscale edge detection
- wavelet texture analysis and segmentation
- Gabor transforms and space-frequency localization
- wavelet-based fractal analysis
- multiscale random processes
- wavelets and neural networks
- image representations from wavelet maxima or zero-crossings
- wavelet compression, coding and signal representation
- wavelet theory and multirate filterbanks
- wavelets in medical imaging
Abstract Due Date: January 15, 1994.
MANUSCRIPT DUE DATE: June 27, 1994
(Proceeding will be made available at the conference)
Applicants shall be notified of acceptance by March 1, 1993.
Your abstract should include the following:
1. Abstract Title.
2. Author Listing (Principal author first and affiliations).
3. Correspondence address for EACH author (email, phone/FAX, ect.).
4. Submitted to: Mathematical Imaging: Wavelet Applications
in Signal and Image Processing,
Andrew Laine, Chairman.
5. Abstract: 500-1000 word abstract.
6. Brief Biography: 50-100 words (Principal author only).
Please send FOUR copies of your abstract to the address below
via FAX, email (one copy) or carrier:
San Diego '94
SPIE, P.O. Box 10, Bellingham, WA 98227-0010
Shipping Address: 1000 20th Street, Bellington, WA 98225
Phone: (206) 676-3290
email: [email protected]
FAX: (206) 647-1445
CompuServe: 71630,2177
Notice: Late submissions may be considered subject to program
time availability and approval of the program committee.
Andrew Laine, Assistant Professor
Computer and Information Sciences Department
University of Florida
Gainesville, FL 32611
Phone:(904) 392-1239
Email:[email protected]
--------------------------- Topic #13 -----------------------------------
From: [email protected]
Subject: question: looking for image sequences
I am looking for a set of grey-scale image sequences that
is available via ftp or email. The length of the sequence
only needs to be 4 or 5 frames.
Thanks in advance!
Jim Craft
[email protected]
--------------------------- Topic #14 -----------------------------------
From: [email protected] (Jean-Marc Lina)
Subject: Preprint available: Complex Daubechies wavelets
Preprint available: Request pre-print by e-mail to [email protected]
COMPLEX DAUBECHIES WAVELETS
Jean-Marc LINA and Michel MAYRAND
Atlantic Nuclear Services Ltd. and Laboratoire de Physique Nucl\'eaire,
Universit\'e de Montr\'eal, Canada
Using a parametrization of the space of multiresolution analyses with
compact support [-J,J+1] and a maximum of J vanishing moments for the
wavelet, this work investigates the general solution of the orthogonality
conditions. More specifically, all real and complex solutions up to
J=5 are presented. A classification of the well-known real solutions
according to the degree of symmetry of the associated scaling function
is proposed. Furthermore, symmetric but complex multiresolution analyses
are described for a number of vanishing moments up to J=8. Finally, a
symmetric multiresolution analysis is constructed on [0,1].
|
| 1409.33 | wavelet Digest, Vol. 2, Nr. 19 | STAR::ABBASI | sleeples days.... | Wed Dec 22 1993 02:48 | 411 |
| Subj: Wavelet Digest, Vol. 2, Nr. 19.
Wavelet Digest Tuesday, December 21, 1993 Volume 2 : Issue 19
Today's Editor: Wim Sweldens
[email protected]
Today's Topics:
1. CFP: Advances in Computational Mathematics
2. Question: Historical Development of Wavelets Application.
3. Reprint available: Wavelet Analysis of Coherent Structures
at the Atmosphere-Forest Interface
4. Joint Time-Frequency Analyzer
5. ACHA: Abstracts for accepted papers in November-December, 1993.
6. Wavelet Packet Laboratory for Windows
7. New paper available: (on wavelets and multigrid)
8. CFP: 3rd International Workshop on SVD and Signal Processing
9. Wavelet DeNoising Scripts Available
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
To unsubscribe, e-mail with "unsubscribe" followed by your e-mail
address as subject. To change address, unsubscribe and resubscribe.
Archive site, preprints, references and back issues:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet.
Gopher: bigcheese.math.scarolina.edu.
Current number of subscribers: 3083
--------------------------- Topic #1 -----------------------------------
From: [email protected] (Daniel Baltzer)
Subject: CFP: Advances in Computational Mathematics
Call for Papers:
Dear Colleague,
The journal Advances in Computational Mathematics would like to announce a
special issue which is to be concerned with practical and theoretical
aspects of multiscale techniques.
Advances in Computational Mathematics is a new journal devoted to all
aspects of computational mathematics, including basic theory, applications,
algorithms and software. The journal is published by Baltzer Publishing
from Basel, Switzerland, and currently has more than forty members on its
editorial board, representing all areas of computational mathematics. John
Mason of the Royal Military College of Science, Shrivenham, UK, and Charles
A. Micchelli of T.J. Watson IBM Research Center, Yorktown Heights, NY, USA
serve as editors-in-chief.
Multigrid algorithms and multilevel methods are very prominent and perhaps
the most important recently developed concepts in a central area of
computational mathematics, namely the numerical treatment of partial
differential equations. The concept of wavelets arose from rather different
sources but turns out to be closely related. In fact, while wavelets have
found their primary applications in signal processing, image analysis and
data compression, recent investigations indicate their promising potential
for the numerical treatment of operator equations as well.
Summarizing all these techniques under the term multiscale techniques is
perhaps justified by their many common features. Their enormous overall
impact on computational mathematics as well as perpetually fruitful
feedback between applications, theory and development of tools make it an
excellent topic for a special issue of a journal with the above profile
which we hope could help synthesizing the ideas, aspects and directions of
research.
Therefore we cordially invite you to submit high quality, unpublished
manuscripts on any mathematical or algorithmic aspect of multiscale
techniques and their applications. We particularly welcome manuscripts on
wavelet and multiscale techniques for the numerical treatment of operator
equations, fast numerical linear algebra, inverse problems and statistics
or applications to geometric modelling.
All submitted papers will undergo the usual process of peer review. We hope
to publish a large number of submissions and will strive to ensure that
each paper receives careful and prompt consideration.
If you would like to submit a paper to this special issue please send three
copies of the manuscript to:
Prof. Dr. Wolfgang Dahmen,
Guest Editor, Special Issue on Multiscale Techniques,
Institut fur Geometrie und Praktische Mathematik,
RWTH Aachen,
Templergraben 55,
52056 Aachen,
Germany.
E-mail: [email protected]
The deadline for submissions is June 30, 1994. We anticipate that the
special issue will appear early in 1995. A free sample copy of Advances in
Computational Mathematics containing 'instructions to authors' is available
from [email protected]
Sincerely,
Wolfgang Dahmen, J.C. Baltzer AG, Science Publishers
Asterweg 1A, 1031 HL Amsterdam, The Netherlands
tel. +31-20-637 0061 fax. +31-20-632 3651
e-mail: [email protected]
--------------------------- Topic #2 -----------------------------------
From: [email protected] (Jakob Hilmer)
Subject: Question: Historical Development of Wavelets Application.
I am writting a project about the historical development of wavelets.
One of ours chapters is about the application of wavelets. In this chapter
we will give an brief introduction to the number of applications.
In Wavelet Digest Volume 2 : Issue 13 Topic 4, Norman Elridge, give a list
of subjects for an upcoming Wavelets Applications Conference in Orlando
(included below).
We are interested in brief explanation, when started, motivation , why is it
special usefull here and expectations in the future. In any of these or
in any other subject not include in this list.
o Theory -pattern recognition
-multiresolution -medical images
-complexity theory -remote sensing
-subband coding -radar processing
-complete orthogonal basis -source identification
-adaptive super-mother wavenet -fingerprints.
-nonlinear dynamics noise
removal o Implementations
-wavelet transform of PDE to -holographic
ODE. -4D optics
-feedback adaptive WT
o Applications -optoelectronic devices
-data compression -wavelet chips
-data representation -neuro-wavelet chips
-texture analysis -inspection systems
-scene segmentation -HDTV designs
-signal processing -B-ISDN designs
-speech processing
-image processing
Thanks in advance..
Stud. Jakob Hilmer
Department of mathematics, IMFUFA
Roskilde University, Denmark
E-mail: [email protected]
--------------------------- Topic #3 -----------------------------------
From: [email protected] (Bai-lian Li)
Subject: Reprint available: Wavelet Analysis of Coherent Structures
at the Atmosphere-Forest Interface
Reprint available: request reprint by e-mail to [email protected]
Wavelet Analysis of Coherent Structures at the Atmosphere-Forest Interface,
Journal of Applied Meteorology, 32(11): 1717-1725.
Authors are Weigang Gao at Environmental Research Division, Argonne National
Laboratory, Argonne, IL 60439 and Bai-lian Li at Center for Biosystems
Modelling, Department of Industrial Engineering, Texas A&M University,
College Station, TX 77843-3131.
--------------------------- Topic #4 -----------------------------------
From: [email protected] (Kan Chen)
Subject: Joint Time-Frequency Analyzer
Joint Time-Frequency Analyzer
If you are in the area of the joint time-frequency analysis (JTFA), a software
package called the JTFA Toolkit (part number 776733-01) is available now from
the National Instruments. The JTFA Toolkit not only includes those well-
known bilinear transform algorithms, such as the cone-shaped
transformation, the Choi-Williams distribution, the spectrogram, and the
Wigner-Ville distribution, but it also incorporates the recently developed
adaptive spectrogram and the time-frequency distribution series (also known
as the Gabor Spectrogram in industry).
The JTFA package is a stand-alone application, which runs on
Macintoshes, PCs running Windows, and Sun SPARCstations. Combining the
JTFA Toolkit with LabVIEW and a data acquisition board, one can analyze real
data. Because of friendly user interface and flexibility, the JTFA package has
already won two industrial awards: the EDN (Electronic Design News) Software
Innovation Award of 1992 and R&D 100 Award of 1993.
The address of National Instruments is: 6504 Bridge Point Parkway,
Austin, Texas 78730-5039, USA, (800) IEEE488 (toll-free U.S. and Canada).
--------------------------- Topic #5 -----------------------------------
From: Charles Chui, Texas A&M University.
Subject: ACHA: Abstracts for accepted papers in November-December, 1993.
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS
Time-Frequency and Time-Scale Analysis, Wavelets,
Numerical Algorithms, and Applications (ACHA)
The first issue will appear in December 1993.
SUBSCRIPTION RATES:
Institutional Personal
In the U.S.A. and Canada: $184 $74
All other countries: $221 $93
ADDRESS: Academic Press, Inc. 525 B Street, Suite 1900, San Diego, S.A.
CA 92101
ABSTRACTS of the accepted papers can be obtained via ftp by following
the steps listed below:
a) ftp wavelet1.math.tamu.edu
b) USER: achasite
c) password: abstract
d) mget acha12.abstract
--------------------------- Topic #6 -----------------------------------
From: R.R Coifman <[email protected]>
Subject: Wavelet Packet Laboratory for Windows
A user friendly version of the Wavelet Packet Laboratory has been
developed for Windows by Digital Diagnostic Corporation and is
available from
A K Peters, Ltd., 289 Linden St., Wellesley, MA 02181
Phone: 617-235-2210, Fax: 617-235-2404
Internet: [email protected]
WPLW has been developed by Victor Wickerhauser and myself initially for
Next and other work station environments (such as XWPL available on our ftp
site pascal.math.yale.edu), this program is an interactive software tool for
the Microsoft Windows operating environment which is much more flexible
and capable than our versions. This program allows you to perform Adapted
Waveform Analysis on digital signals with Wavelet Packet and Local
Trigonometric Transforms in order to explore and design specialized algorithms
using these transforms. In particular it accepts sound signals in various
formats, these can be processed in a variety of ways then played and stored.
The program has two extended manuals, theoretical and "how to".
--------------------------- Topic #7 -----------------------------------
From: [email protected] (Andreas Rieder)
Subject: New paper available
The following preprint is available:
ON THE ROBUSTNESS OF THE DAMPED V-CYCLE OF THE
WAVELET FREQUENCY DECOMPOSITION MULTIGRID METHOD
Andreas Rieder und Xiaodong Zhou
Abstract:
The V-cycle of the wavelet variation of the "Frequency decomposition
multigrid method" of Hackbusch [Numer. Math., 56, pp. 229-245, 1989]
is considered.
It is shown that its convergence speed is not affected by the presence
of anisotropy provided that the corresponding coarse grid correction
is damped sufficiently strong. Our analysis is based on properties of
wavelet packets which are supplied and proved.
Numerical approximations to the speed of convergence illustrate
the theoretical results.
Key words: wavelets, wavelet packets, robust multilevel methods,
V-cycle
Subject classification: AMS(MOS) 65F10, 65N30
Technical Report of the Computational Mathematics Laboratory,
No. CML TR93-10, Rice University, Houston, 1993
A compressed postscript copy of this report is available from
the ftp site cml.rice.edu (128.42.62.23). The file is pub/reports/9310.ps.Z.
(login: anonymous, password: your email-address)
Andreas Rieder, Computational Mathematics Laboratory, Rice University,
Houston, Texas 77251, USA
email: [email protected]
--------------------------- Topic #8 -----------------------------------
From: [email protected]
Subject: CFP: 3rd International Workshop on SVD and Signal Processing
3rd International Workshop on
SVD and Signal Processing
August 22--25, 1994
Leuven, Belgium
Preliminary Announcement and Call for Papers
This Workshop on Singular Value Decomposition and Signal Processing
is a continuation of two previous workshops of the same name which were
held in Les Houches, France, September 1987, and Kingston, Rhode Island,
U.S.A., June 1990.
The workshop is organized in cooperation with EURASIP, the IEEE Benelux
Signal Processing Chapter and the IEEE Benelux Circuits and Systems Chapter.
Papers are solicited for technical sessions on the following and related
topics
SVD Algorithms :
real-time and adaptive algorithms, continuous algorithms,
complexity, accuracy, convergence,
related decompositions, tensor SVD
SVD Architectures :
application specific hardware, parallel implementation
SVD Applications :
array signal processing, model identification, model reduction,
spectrum analysis, harmonic retrieval, speech and image processing
control
Other topics related to the SVD and its applications are welcome.
Authors are invited to submit four copies of an extended summary
(2--4 pages) to the workshop secretariat for review.
The preferred presentation format -lecture, poster or video- should
be indicated.
To facilitate rapid communication, authors should provide
a fax number and email address if possible.
Authors of accepted papers will be asked to prepare a version for
publication in a conference proceedings.
Authors' Schedule
Submission of summary: March 15, 1994
Notification of Acceptance: May 15, 1994
Submission of camera-ready paper: August 22, 1994
Conference Committee:
Chairpersons:
Bart De Moor
E.E. Dept., Kath. Universiteit Leuven, Leuven, Belgium
[email protected]
Marc Moonen
E.E. Dept., Kath. Universiteit Leuven, Leuven, Belgium
[email protected]
Members:
Ed Deprettere
E.E. Dept., Delft University of Technology, Delft, The Netherlands
[email protected]
Gene Golub
Dept. of Computer Science, Stanford University, Stanford, CA-94305, U.S.A.
[email protected]
Sven Hammarling
The Numerical Algorithms Group Ltd, Oxford, U.K.
[email protected]
Franklin Luk
Dept. of Computer Science, Chinese University of Hong Kong
Shatin, N.T., Hong Kong
[email protected]
Paul Van Dooren
Coordinated Science Laboratory, University of Illinois at Urbana-Champaign
Urbana, IL-61801, U.S.A.
[email protected]
Workshop Secretariat: Lieven De Lathauwer
E.E. Dept., ESAT/SISTA
Kath. Universiteit Leuven
K. Mercierlaan 94
B-3001 Heverlee
Belgium
tel : 32/16/22.09.31 fax : 32/16/22.18.55
email : [email protected]
--------------------------- Topic #9 -----------------------------------
From: [email protected] (wavelab)
Subject: Wavelet DeNoising Scripts Available
Wavelet DeNoising Scripts Available
In response to numerous requests, we are now making available a software
library containing scripts which will reproduce exactly the figures in four of
our recent articles on Wavelet-based DeNoising. The library is available by
anonymous FTP. It consists of a little more than 400 MATLAB scripts, M-files,
MEX-files, datasets, self-running demonstrations, and on-line documentation.
The library is called TeachWave, and consists of routines for basic wavelet,
wavelet-packet, cosine-packet, and matching-pursuit analysis of 1-d signals and
2-d images. We have used this library in teaching courses at Stanford and
Berkeley; we think that the study of these scripts is a good way for some
students to get quickly into the wavelets field. The total disk storage
required for TeachWave is a little over 2 megabytes when all the datasets are
obtained. The software is copylefted, GNU-Style.
Jeffrey Scargle of NASA-Ames is leading a team of researchers supported by the
NASA Astrophysics Data Program that plans to develop a series of applications
of wavelet-based methods to Astrophysical data problems, and which plans to
further develop and add to this collection.
To access the software, anonymous FTP to playfair.stanford.edu, and cd to
directory pub/software/wavelets. There are archive files oriented towards
different machine architectures (due to different pathname separators and
different MEX formats).
TeachWave0550.tar.Z -- for unix folks;
binary transfer, uncompress, tar xvf.
TeachWave0550.sea.hqx -- for Mac folks;
binary transfer, unbinhex, double-click.
Each archive file contains README, INSTALLATION, GETTINGSTARTED files.
Note for Unix folk: MEX files are included only for Sun4 and DecStation
machines. Users of other machines needn't worry; it is not neccesary to have
the MEX files -- they just speed some things up by factors of 10-30, but they
are not critical unless you are working on 2-d problems.
David Donoho and Iain Johnstone
Department of Statistics, Stanford University
comments to [email protected]
-------------------- End of Wavelet Digest -----------------------------
|
| 1409.34 | Wavelet Digest, Vol. 3, Nr. 2. | STAR::ABBASI | one of the 744 | Wed Feb 16 1994 14:50 | 643 |
| Subj: Wavelet Digest, Vol. 3, Nr. 2.
Wavelet Digest Monday, February 7, 1994 Volume 3 : Issue 2
Today's Editor: Wim Sweldens
[email protected]
Today's Topics:
1. Visiting Positions at the University of Texas at Dallas
2. Contents Adv. Comp. Math.
3. New book on multi-scale representation of image data
4. Question: Looking for wavelet image compression results
5. Preprints available
6. Preprint available
7. Looking for a reference
8. International Workshop On Total Positivity and its Applications
9. CFP: SPIE Mathematical Imaging: Wavelet Applications II
10. Reply to Wavelet Digest 3.1
11. Looking for public domain software
12. Question: Regularity for multidimensional dilation equations
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
To unsubscribe, e-mail with "unsubscribe" followed by your e-mail
address as subject. To change address, unsubscribe and resubscribe.
Archive site, preprints, references and back issues:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet.
Gopher: bigcheese.math.scarolina.edu.
Current number of subscribers: 3178
--------------------------- Topic #1 -----------------------------------
From: R Ober, <[email protected]>
Subject: Visiting Positions at the University of Texas at Dallas
POSITION: Up to four visiting assistant professorships anticipated
in Mathematics and Applied Mathematics.
APPOINTMENT: September 1, 1994. Applications accepted until position
filled. Appointments up to three years in duration.
RESPONSIBILITIES: Research and teaching in Mathematics and Applied
Mathematics. Two course teaching load per semester.
QUALIFICATIONS: Ph.D. in Mathematics, Applied Mathematics, or the
equivalent. Evidence of strong interest in and potential
for research required.
PROGRAMS: The program offers Ph.D., M.S., M.A.T., and B.S. degrees.
Currently we cover Applied Mathematics, Statistics and
Mathematics, and have about 71 graduate and 77
undergraduate majors.
FACILITIES: The Program has access to a variety of computers on
campus, including a Convex C-3, a 64 processor NCUBE, and
a network of Sun's, all connected via Ethernet. Each
faculty and TA office has either a Sun workstation or an
X-terminal connected to one of three SPARCserver 10s.
The University also has access to the UT-System's CRAY
X-MP.
UNIVERSITY: The University of Texas at Dallas was formed in 1969
from the Southwest Center for Advanced Studies, a
private research foundation. The University is located
in a northern suburb of Dallas, Texas, in close proximity
to several high technology industries.
SALARY: Competetive.
Applications including a resume and at least three letters of reference
should be addressed to: L. R. Hunt, Programs in Mathematical Sciences,
EC 35, The University of Texas at Dallas, P.O. Box 830688, Richardson,
Texas, 75083-0688. Area of research should be detailed using the
Mathematical Review subject classification scheme.
--------------------------- Topic #2 -----------------------------------
From: J.C. Baltzer, AG Science Publishers
Subject: Contents Adv. Comp. Math.
Advances in Computational Mathematics, Volume 2, No.1, 1994, ISSN 1019 7168
Editors-in-Chief: John C. Mason & Charles A. Micchelli
special issue: Volume 2, No. 1, 1994: GEOMETRIC MODELING
Editor: Philip Barry
pp 1-21: NURBS approximation of surface / surface intersection curves
C.L. Bajaj and G. Xu
pp 23-40: Elastic curves on the sphere
G. Brunnett and P.E. Crouch
pp 41-66: Pythagorean-hodograph space curves
R.T. Farouki and T. Sakkalis
pp 67-80: A weak condition for the convexity of tensor-product Bezier and
B-spline surfaces
M.S. Floater
pp 81-99 Least squares surface approximation to scattered data using
multiquadratic functions
R. Franke, H. Hagen and G.M. Nielson
pp 101-122 Triangular finite elements of HCT type and class Cr
M. Laghchim-Lahlou and P. Sablonniere
pp 123-142 Helix splines as an example of affine Tchebycheffian splines
H. Pottmann and M.G. Wagner
pp 143-154 Convergence of subdivision and degree elevation
H. Prautzsch and L. Kobbelt
Submissions of articles and proposals for special issues are to be
addressed to the Editors-in-Chief:
John C. Mason
Applied & Computational Mathematics Group
Royal Military College of Science (Cranfield University)
Shrivenham, Swindon, SN6 8LA, England, E-mail: [email protected]
Charles A. Micchelli
Mathematical Sciences Department
IBM Research Center
P.O. Box 218, Yorktown Heights, NY 10598, USA, E-mail: [email protected]
Free specimen copies and orders for Advances in Computational Mathematics
are to be sent to:
E-mail: [email protected]
J.C. Baltzer AG, Science Publishers, Asterweg 1A, 1031 HL Amsterdam
The Netherlands, tel. +31-20-637 0061, fax. +31-20-632 3651
e-mail: [email protected]
--------------------------- Topic #3 -----------------------------------
From: [email protected]
Subject: New book on multi-scale representation of image data:
SCALE-SPACE THEORY IN COMPUTER VISION
by Tony Lindeberg,
Royal Institute of Technology,
Stockholm, Sweden
SHORT DESCRIPTION:
We perceive objects in the world as having structures both at coarse
and fine scales. A tree, for instance, may appear as having a roughly
round or cylindrical shape when seen from a distance, even though it
is built up from a large number of branches. At a closer look,
individual leaves become visible, and we can observe that they in turn
have texture at an even finer scale. This fact, that objects in the
world appear in different ways depending upon the scale of observation,
has important implications when analysing measured data, such as
images, with automatic methods.
"Scale-Space Theory in Computer Vision" describes a formal framework,
called scale-space representation, for handling the notion of scale
in image data. It gives an introduction to the general foundations of
the theory and shows how it applies to essential problems in computer
vision such as computation of image features and cues to surface
shape. The subjects range from the mathematical underpinning to
practical computational techniques. The power of the methodology is
illustrated by a rich set of examples.
This work is the first monograph on scale-space theory. It is intended
as an introduction, reference, and inspiration for researchers,
students, and system designers in computer vision as well as related
fields such as image processing, photogrammetry, medical image analysis,
and signal processing in general.
CONTENTS:
Foreword by Jan Koenderink
Preface
Abstract
1 Introduction and overview
2 Linear scale-space and related multi-scale representations
3 Scale-space for 1-D discrete signals
4 Scale-space for N-D discrete signals
5 Discrete derivative approximations with scale-space properties
6 Feature detection in scale-space
7 The scale-space primal sketch
8 Behaviour of image structures in scale-space: Deep structure
9 Algorithm for computing the scale-space primal sketch
10 Detecting salient blob-like image structures and their scales
11 Guiding early visual processing with qualitative scale and
region information
12 Summary and discussion
13 Scale selection for differential operators
14 Direct computation of shape cues by scale-space operations
15 Non-uniform smoothing
Appendix, Bibliography, Index
435 pages, ISBN 0-7923-9418-6, Kluwer Academic Publishers, 1994.
The complete table of contents, foreword, preface, and abstract are
available by anonymous ftp from world.std.com (192.74.137.5)
>ftp world.std.com
Name: anonymous
Password: <email address>
ftp>cd ftp/Kluwer/books
ftp>get scale_space
The list price has been set to Dfl 275.00 (USD 130). For ordering
information, see the file at the ftp site or contact Mike Casey at
Kluwer Academic Publishers ([email protected] / PO Box 17, NL-3300 AA
Dordrecht, Netherlands). He is offering a prepublication discount
until March 31, 1994.
--------------------------- Topic #4 -----------------------------------
From: [email protected] (Peter Schuett)
Subject: Question: Looking for wavelet image compression results.
Dear Waveletters
I am interested to use wavelets and wave packets in image compression.
It is interesting for me which quantizer they use and which results they
got for which kind of images.
Thanks a lot in advance
Peter Sch"utt
[email protected]
--------------------------- Topic #5 -----------------------------------
From: Akram Aldroubi, [email protected]
Subject: Preprints available
Reprint available: "Familes of multiresolution and wavelet spaces with
optimal properties"
by Akram Aldroubi and Michael Unser, in Numer. Funct. Anal. an Optimiz.,
pp. 417-446, 1993.
Also available, preprint of the paper below. To receive copies, please send
your postal address by email to: [email protected]
Designing MRA-type wavelets and their fast algorithms
By
Patrice Abry, Ecole Normale Superieure de Lyon, France
and
Akram Aldroubi, National Institutes of Health, USA
Often, the Discrete Wavelet Transform is performed and
implemented with the Daubechies wavelets, the Battle-Lemarie wavelets or
the splines wavelets whereas in continuous time wavelet decomposition a much
larger variety of mother wavelets are used. Maintaining the dyadic
time-frequency
sampling and the recursive pyramidal computational structure, we present
various
methods to obtain any chosen analyzing wavelet $\psi_{wanted}$, with some
desired
shape and properties and which is associated with a semi-orthogonal
multiresolution
analysis. We explain in details how to design one's own wavelet, starting from
any given Multiresolution Analysis. We also explicitly derive the formulae
of the
filter bank structure that implements the designed wavelet. We illustrate these
wavelet design techniques with examples that we have programmed with Matlab
routines.
--------------------------- Topic #6 -----------------------------------
From: Mickey Bhatia <[email protected]>
Subject: Preprint available
The following preprint is available through an anonymous ftp to
"lids.mit.edu" in the directory "pub/ssg/papers" (do "cd
pub/ssg/papers" after logging in). The file name is
"LIDS-P-2182.PS.gz". Read the file "README" in the same directory for
further help.
MIT Technical Report LIDS-P-2182:
A WAVELET-BASED METHOD FOR MULTISCALE TOMOGRAPHIC RECONSTRUCTION
M. Bhatia, W. C. Karl, and A. S. Willsky
Stochastic Systems Group
Laboratory for Information and Decision Systems
Massachusetts Institute of Technology
Cambridge, Massachusetts 02139
Telephone: (617) 253-3816 Telefax: (617) 258-8553
Email: [email protected]
We represent the standard ramp filter operator of the filtered
back-projection (FBP) reconstruction in different bases composed of
Haar and Daubechies compactly supported wavelets. The resulting
multiscale representation of the ramp filter matrix operator is
approximately diagonal. The accuracy of this diagonal approximation
becomes better as wavelets with larger number of vanishing moments
are used. This wavelet-based representation enables us to formulate
a multiscale tomographic reconstruction technique wherein the object
is reconstructed at multiple scales or resolutions. A complete
reconstruction is obtained by combining the reconstructions at
different scales. Our multiscale reconstruction technique has the
same computational complexity as the FBP reconstruction method. It
differs from other multiscale reconstruction techniques in that 1)
the object is defined through a multiscale transformation of the
projection domain, and 2) we explicitly account for noise in the
projection data by calculating maximum aposteriori probability (MAP)
multiscale reconstruction estimates based on a chosen fractal prior
on the multiscale object coefficients. The computational complexity
of this MAP solution is also the same as that of the FBP
reconstruction. This is in contrast to commonly used methods of
statistical regularization which result in computationally intensive
optimization algorithms. The framework for multiscale
reconstruction presented here can find application in object feature
recognition directly from projection data, and regularization of
imaging problems where the projection data are noisy.
Key words: multiresolution reconstruction, wavelets, tomography,
stochastic models.
--------------------------- Topic #7 -----------------------------------
From: [email protected]
Subject: Looking for a reference.
Dear Waveletters,
I've the following question : Does anyone know the following reference:
Misra, Prasanna:" Parallel Computation of 2-D wavelet transforms "
in: ICPR (International Clinical Products Review ??) , Volume D, 1992, p.111-
114.
I couldn't get it here in Europe ! Please e-mail to: [email protected]
Thanks in advance
Andrew
--------------------------- Topic #8 -----------------------------------
From: Allan Pinkus <[email protected]>
Organization: Technion - Israel Institute of Technology
Subject: International Workshop On Total Positivity and its Applications
Announcing
An International Workshop On
Total Positivity and its Applications
Jaca, Spain, September 26--30, 1994
Organised by Departamento de Matematica Aplicada
University of Zaragoza, Spain
Total Positivity has proved to be a powerful tool in many areas of
pure and applied mathematics. However there has never been a meeting
which focused solely on this subject. In view of the diverse
applications of total positivity we feel that a meeting which
draws together experts whose lectures will cover the principal areas of
applications and participants whose research can benefit from these
techniques will be a timely and important scientific event.
Among the topics which we plan to have represented at the conference
are the applications of total positivity to Probability and Statistics,
Combinatorics, Integral and Differential Equations, Geometric Modeling,
Matrix Theory, Approximation Theory, Complex Analysis, Numerical Analysis
and Wavelets Analysis. In each of these fields there are problem
areas in which total positivity has proved to be useful, if not
indispensable. We intend to bring together as many researchers
as possible who use total positivity in their work or are
interested in learning about the latest developments in this subject.
Location:
The meeting will take place in the Residence of the University of
Zaragoza, located in the picturesque town of Jaca, in the foothills
of the Spanish Pyrenees, near the French border. Jaca has its historical
origin as the first capital of the medieval kingdom of Aragon, and it
was one of the main gateways to Spain for European pilgrims following the
(Camino de Santiago) to Santiago de Compostela. The cathedral of Jaca
was built in Romanesque style in the 11th century. Jaca now serves as
a year round resort center, because of its proximity to several ski
resorts, as well as for its hiking and camping facilities within the
Pyrenees. There are many possibilities for excursions from Jaca
to enjoy the natural scenery and historical sites within the
surrounding region. The town is accessible by public transportation from
Zaragoza, Madrid and Barcelona. The distance from Zaragoza is
approximately 160 Km. while from Madrid or Barcelona it is about 480 Km.
Organising Committee:
Prof. Mariano Gasca
University of Zaragoza, Spain
Dr. Charles A. Micchelli
IBM T.J. Watson Res. Center, USA
Prof. Allan Pinkus
Technion, Israel
Dr. Timothy N.T. Goodman
University of Dundee, Scotland, UK
Partial List of Invited Speakers:
B. Bojanov (Sofia), F. Brenti (Perugia), J. Carnicer (Zaragoza),
J. Garloff (Konstanz), M. Gasca (Zaragoza), T.N.T. Goodman (Dundee),
B. Heiligers (Augsburg), R-Q. Jia (Edmonton), S. Karlin (Stanford),
K. Morken (Oslo), J. M. Pena (Zaragoza), A. Pinkus (Haifa),
H. Pottmann (Vienna), R. Zalik (Auburn).
Local Organising Committee:
J.M.Carnicer, M. Garcia, M.C. Lopez de Silanes, J.J.Martinez, J.M.Pena.
Correspondence and Further Information:
Those interested in participating, contributing or receiving future
announcements, please contact the organisers:
IWTPA, Depto. Matematica Aplicada
Facultad de Ciencias
Edificio de Matematicas
Universidad de Zaragoza
50009 Zaragoza, Spain
Fax: (34)76 356244 Phone: (34)76 356617
E-mail: [email protected] or [email protected]
--------------------------- Topic #9 -----------------------------------
From: Andrew Laine, [email protected]
Subject: CFP: Mathematical Imaging: Wavelet Applications II.
Extended Deadline!
Conference Title: "Mathematical Imaging: Wavelet Applications in
Signal and Image Processing II"
Part of SPIE`s Annual International Symposium on
Optoelectronic Applied Science and Engineering
July 27-29, 1994
San Diego, California
San Diego Convention Center, Marriot Hotel Marina
Conference Chairs:
Andrew Laine, University of Florida
Michael Unser, National Institutes of Health
Program Committee:
Bjorn Jawerth, University of South Carolina
Martin Vetterli, University of California, Berkeley
Ronald Coifman, Yale University
Stephane Mallat, New York University
Victor Wickerhauser, Washington University
Akram Aldroubi, National Institutes of Health
Charles Chui, Texas A&M University
Arun Kumar, Southwestern Bell Technology Resources
Alan Bovik, University of Texas, Austin
KEYNOTE ADDRESS: Ingrid Daubechies, Princeton University
and AT&T Bell Laboratories
The analysis of signals and images at multiple scales
is an attractive framework for many problems in computer vision,
signal and image processing. Wavelet theory provides
a mathematically precise understanding of the concept of multiresolution.
The conference shall focus on novel applications of wavelet methods of
analysis and processing techniques, refinements of existing methods,
and new theoretical models. When possible, papers should compare
and contrast wavelet based approaches to traditional techniques.
Topics for the conference may include but are not limited to:
- image pyramids
- frames and overcomplete representations
- multiresolution algorithms
- wavelet-based noise reduction and restoration
- multiscale edge detection
- wavelet texture analysis and segmentation
- Gabor transforms and space-frequency localization
- wavelet-based fractal analysis
- multiscale random processes
- wavelets and neural networks
- image representations from wavelet maxima or zero-crossings
- wavelet compression, coding and signal representation
- wavelet theory and multirate filterbanks
- wavelets in medical imaging
--------> EXTENDED DEADLINES <---------
Abstract Due Date: February 15, 1994.
MANUSCRIPT DUE DATE: June 27, 1994
(Proceeding will be made available at the conference)
Applicants shall be notified of acceptance by March 15, 1993.
Your abstract should include the following:
1. Abstract Title.
2. Author Listing (Principal author first and affiliations).
3. Correspondence address for EACH author (email, phone/FAX, ect.).
4. Submitted to: Mathematical Imaging: Wavelet Applications
in Signal and Image Processing,
Andrew Laine, Chairman.
5. Abstract: 500-1000 word abstract.
6. Brief Biography: 50-100 words (Principal author only).
Please send FOUR copies of your abstract to the address below
via FAX, email (one copy) or carrier:
San Diego '94
SPIE, P.O. Box 10, Bellingham, WA 98227-0010
Shipping Address: 1000 20th Street, Bellington, WA 98225
Phone: (206) 676-3290
email: [email protected]
FAX: (206) 647-1445
CompuServe: 71630,2177
Notice: Late submissions may be considered subject to program
time availability and approval of the program committee.
Andrew Laine, Assistant Professor
Computer and Information Sciences Department
University of Florida
Gainesville, FL 32611
Phone:(904) 392-1239
Email:[email protected]
--------------------------- Topic #10 -----------------------------------
From: [email protected] (Mike Lyall)
Subject: Reply to Wavelet Digest 3.1:
Hi Suman,
I would like to offer some suggestions for you and others beginning to learn
about wavelets. I have been studying wavelets for about 6 months now,
so I've read alot of papers, books, etc., trying to learn about them.
As an engineer, you probably should concentrate on reading wavelet papers
in the engineering journals. These papers will relate to concepts that
you are already familiar with, so it will be easier to see their properties.
You should begin by learning the motivation for the wavelet transform. This
involves a comparison against the Fourier Transform and the Short-Time Fourier
Transform. I think you should begin by limiting yourself to the Discete WT only,
since it is less involved. Then, you should spend some time studying the multi-
resolution analysis (MRA) introduced by Mallat. This will shed light on the
specific properties of wavelets. I also think it is important that you study
the concept of filter banks. These are well known in the signal processing
community and several excellent papers have been written. Wavelets can
be generated from filter banks, and they also relate to subband coding for
image compression, the hot area for wavelets right now.
There are many papers written on wavelets by the mathematical community.
You should wait until you master some of the basics before you tackle these.
In fact, many of the wavelet introductions are written by mathematicians. While
these are excellent papers, it is easy to get lost at first. Their motivations
are slightly different from engineering ones, and there are subtle notation
differences that can be difficult to overcome at first.
At this point, you should be able to handle most of the wavelet literature.
You can then move on to some of the books, like Ten Lectures, which is an
excellent combination on almost all of the work done in wavelets. As a matter
of fact, I would recommend reading this book *while* you are reading the
introductory topics I mentioned above. It really ties everything together well.
Now for the articles/papers/books.
The following are ones I found the most useful when I was starting out:
General Introduction/Motivations
O. Rioul and M. Vetterli, "Wavelets and Signal Processing," IEEE Signal
Processing Magazine, October 1991, pp. 14-35.
Don Lancaster, "Hardware Hacker: Understanding transforms, video compression
secrets, ..., and more wavelet breakthroughs," Radio-Electronics Magazine,
July 1991, pp. 68 - ?.
Y. Meyer, Book Reviews, (BULLETIN (New Series) OF THE AMERICAN MATHEMATICAL
SOCIETY, Vol. 28, No.2, April 1993), pp. 350-360. Review of the books
"An introduction to wavelets", by C. Chui, and "Ten Lectures on Wavelets", by
I. Daubechies.
Multiresolution analysis
S. Mallat, "A Theory for Multiresolution Signal Decomposition: The Wavelet
Representation," IEEE Trans. on Patt. Anal. and Mach. Intell., July 1989,
pp. 674 - 693.
B. Jawerth and W. Sweldens, "An Overview of Wavelet Based Multiresolution
Analyses", To appear in SIAM Review, available by anonymous ftp from
maxwell.math.scarolina.edu as /pub/wavelet/papers/imi_reports/imi93_1.ps
Wavelets and Filter Banks
M. Vetterli and C. Herley, "Wavelets and Filter Banks: Theory and Design,"
IEEE Trans. Sig. Proc., Sept. 1992, pp. 2207-2232.
P.P. Vaidyanathan, "Theory and Design of M-Channel Maximally Decimated
Quadrature Mirror Filters with Arbitrary M, Having the Perfect-Reconstruction
Property," IEEE Trans. ASSP, April 1987, pp. 476-492.
M. Vetterli, "Filter Banks Allowing Perfect Reconstruction," Signal Processing,
vol. 10, no. 3, 1986, pp. 219-244.
Mathematical Papers
B. Jawerth and W. Sweldens, "An Overview of Wavelet Based Multiresolution
Analyses", To appear in SIAM Review, available by anonymous ftp from
maxwell.math.scarolina.edu as /pub/wavelet/papers/imi_reports/imi93_1.ps
G. Strang, "Wavelets and Dilation Equations: A Brief Introduction," SIAM
Review, Dec. 1989, pp. 614-627.
Books
I. Daubechies, Ten Lectures on Wavelets (CBMS-NSF Series), Philadelphia:
SIAM, 1992.
Lecture Notes/Theses
Any you can get your hands on!
This is just a small list of papers and books. I am sure there are many
other excellent works out there. Hopefully, others will add to this list
so that it will be more complete.
I hope this helps. Good luck!!!
--------------------------- Topic #11 -----------------------------------
From: Jim McNeece, <[email protected]>
Subject: Looking for public domain software.
Is there any public domain wavelet software I could get my hands on?
Algorithms, etc.? Jim McNeece ([email protected])
--------------------------- Topic #12 -----------------------------------
From: Tomasz Bielecki, <U49077%[email protected]>
Subject: Question: Regularity for multidimensional dilation equations
Question: What is known regarding the regularity of solutions of
multidimensional two-scale dilation equations?
Tomasz Bielecki
Dept. Mathematics, Statistics and Computer Science
University of Illinois - Chicago
|
| 1409.35 | Wavelet Digest, Vol. 3, Nr. 3. | STAR::ABBASI | one of the 744 | Wed Feb 16 1994 14:50 | 453 |
| Subj: Wavelet Digest, Vol. 3, Nr. 3.
Wavelet Digest Tuesday, February 15, 1994 Volume 3 : Issue 3
Today's Editor: Wim Sweldens
[email protected]
Today's Topics:
1. Question: Wavelets for time series.
2. Question: Wavelets and non-linear integral transforms
3. Meeting: Workshop course on Wavelets Filter Banks and Applications
4. Preprint: Fast Surface Interpolation Using Wavelet Transform
5. Book: Wavelets: An Elementary Treatment of Theory and Applications
6. Question: Looking for wavelet shareware for Macintosh
7. Question: Biorthogonal Splines in Ten Lectures
8. Meeting: Artificial Neural Networks in Engineering (ANNIE '94)
9. Question: Factorization of multidimensional orthogonal wavelets
10. Answer: WD 3.2 about 2D-refinement equation
11. Book: Multivariate Approximation: From CAGD to Wavelets
12. Question: Daubechies wavelets for large N
Submissions for Wavelet Digest:
E-mail to [email protected] with "submit" as subject.
Subscriptions for Wavelet Digest:
E-mail to [email protected] with "subscribe" as subject.
To unsubscribe, e-mail with "unsubscribe" followed by your e-mail
address as subject. To change address, unsubscribe and resubscribe.
Archive site, preprints, references and back issues:
Anonymous ftp to maxwell.math.scarolina.edu (129.252.12.3),
directory /pub/wavelet.
Gopher: bigcheese.math.scarolina.edu.
Current number of subscribers: 3202
--------------------------- Topic #1 -----------------------------------
From: Sarah Szita
Date: 08-Feb-1994
Subject: Question: Wavelets for time series.
Could anybody recommend a suitable reference for me?
I am familiar with the basic concepts of wavelets but could do with
some advice on the practical applications. What I want to do is
write a program to apply wavelet transforms to a long time series
of data - I've already done Fourier analysis and am now looking at
recurrent but non-periodic features of the data.
I am a post-graduate physicist but not much of a mathematician (!) and
consequently find most of the standard texts quite heavy going. I
would welcome any advice on the programming or suitable references.
Also, is it straightforward to define a wavelet to match the shape you
are looking for, and what restrictions are there on this?
Thanks in advance,
Sarah Szita
[email protected]
--------------------------- Topic #2 -----------------------------------
From: [email protected] (Hans Fraaije)
Subject: Question: Wavelets and non-linear integral transforms
Dear waveletters,
I am studying wavelets as a tool for possible rapid inversion
of certain 'local density functionals'. These are non-linear integral
transforms with applications in the description
of molecular aggregates in chemistry; related functionals are used in
quantum chemistry.
I would appreciate any suggestions for literature - is there already a study
of wavelets in the realm of non-linear integral transforms?
Hans Fraaije
[email protected]
--------------------------- Topic #3 -----------------------------------
From: Gil Strang <[email protected]>
Subject: Meeting: Workshop course on Wavelets Filter Banks and Applications
SECOND ANNOUNCEMENT
WORKSHOP COURSE on WAVELETS FILTER BANKS and APPLICATIONS
at Wellesley College near Boston: June 24-27 1994
organized and taught by Gilbert Strang and Truong Nguyen , M I T
The topics will include
Digital Filters: Analysis and Design
Matrix Analysis: Toeplitz Matrices and Circulants
Multirate Signal Processing: Filtering, Decimation, Polyphase
Wavelet Transform: Pyramid Implementation
Daubechies Wavelets, Biorthogonal Wavelets, Multiwavelets
Compression, Transient Detection, Radar Processing
Non-Destructive Evaluation, Digital Communications
Perfect Reconstruction: Orthonormal, Cosine-modulation
Spectral Factorization, Transmultiplexers
Quantization effects
This workshop course will NOT assume an extended background in
filter banks and wavelets. It is developed from an MIT graduate course
emphasizing applications of the theory. Participants are invited to
suggest problems and possible applications for discussion.
The workshop will begin Friday morning June 24 and end Monday afternoon
June 27. We will meet in the Wellesley College Science Center
(air-conditioned) where computing facilities are available. You may choose
on-campus housing or live off-campus. Meals and morning coffee will be
available to all participants, Friday breakfast through Monday lunch.
On-campus: Wellesley College will provide single or double accomodation
(your preference) for four nights Thursday - Friday - Saturday - Sunday.
There will be an informal social time for all participants who arrive
on Thursday evening, and a reception at Professor Strang's home on a
mid-Workshop evening.
Off-campus within walking distance: Wellesley Inn on the Square:
576 Washington Street, Wellesley MA 02181; (617)235-0180.
For information about cost and reservations and travel, please
send a short message to [email protected]
We hope and believe that this will be an outstanding Workshop.
--------------------------- Topic #4 -----------------------------------
From: to [email protected] (Ming-Haw Yaou).
Subject: Preprint: Fast Surface Interpolation Using Wavelet Transform
Preprint of the following paper is availiable. If interested, please email
to [email protected](Ming-Haw Yaou).
(Accepted by IEEE Trans. on PAMI 1993)
Fast Surface Interpolation Using Multi-Resolution Wavelet Transform
Ming-Haw Yaou and Wen-Thong Chang
Institute of Communication Engineering
Center for Telecommunication Research
National Chiao Tung University, Hsinchu, Taiwan, R.O.C
KEYWORDS: Surface Interpolation, Regularization, Discretization,
Wavelet transform, Basis transfer scheme, Preconditioning.
ABSTRACT:
Discrete formulation of the surface interpolation problem usually leads to a
large sparse linear equation system. Due to the poor convergence condition of
the equation system, the convergence rate of solving this problem with iterative
method is very slow. To improve this condition, a multi-resolution basis
transfer scheme based on the wavelet transform is proposed.
By applying the wavelet transform, the original interpolation basis is
transformed into two sets of bases with larger supports while the admissible
solution space remains unchanged. With this basis transfer, a new set of nodal
variables results and an equivalent equation system with better convergence
condition can be solved. The basis transfer can be easily implemented by using
an QMF matrix pair associated with the chosen interpolation basis. The
consequence of the basis transfer scheme can be regarded as a preconditioner to
the subsequent iterative computation method. The effect of the transfer
is that the interpolated surface is decomposed into its low frequency
and high frequency portions in the frequency domain.
It has been indicated that the convergence rate of the interpolated
surface is dominated by the low frequency portion. With this frequency domain
decomposition, the low frequency portion of the interpolated surface can be
emphasized. As compared with other acceleration methods, this basis transfer
scheme provides a more systematical approach for fast surface interpolation.
The easy implementation and high flexibility of the proposed algorithm also
make it applicable to various regularization problems.
--------------------------- Topic #5 -----------------------------------
From: T.H. Koornwinder <[email protected]>
Subject: Book: Wavelets: An Elementary Treatment of Theory and Applications
New book:
WAVELETS: AN ELEMENTARY TREATMENT OF THEORY AND APPLICATIONS
edited by T.H. Koornwinder (University of Amsterdam),
Vol.1 in: Series in Approximations & Decompositions
(C.K. Chui, series editor),
World Scientific, 1993.
240pp., ISBN 981-02-1388-3, Price: US$48
SHORT DESCRIPTION:
The emphasis in this volume, based on an intensive course on Wavelets given at
CWI, Amsterdam, is on the affine case.
The first part presents a concise introduction of the underlying theory to
the uninitiated reader.
The second part gives applications in various areas. Some of the contributions
here are a fresh exposition of earlier work by others, while other papers
contain new results by the authors. The areas are so diverse as
seismic processing, quadrature formulae, and wavelet bases adapted to
inhomogeneous cases.
CONTENTS:
N.M. Temme, "Wavelets: first steps"
P.W. Hemker, T.H. Koornwinder & N.M. Temme, "Wavelets: mathematical
preliminaries"
T.H. Koornwinder, "The continuous wavelet transform"
H.J.A.M. Heijmans, "Discrete wavelets and multiresolution analysis"
P. Nacken, "Image compression using wavelets"
A.B. Olde Daalhuis, "Computing with Daubechies' wavelets"
P.W. Hemker & F. Plantevin, "Wavelet bases adapted to inhomogeneous cases"
E.H. Dooijes, "Conjugate quadrature filters for multiresolution analysis and
synthesis"
W. Sweldens & R. Piessens, "Calculation of the wavelet decomposition using
quadrature formulae"
T.H. Koornwinder, "Fast wavelet transforms and Calderon-Zygmund operators"
J.A.H. Alkemade, "The finite wavelet transform with an applicatiom to seismic
processing"
M. Hazewinkel, "Wavelets understand fractals"
--------------------------- Topic #6 -----------------------------------
From: Ming Wei, University of North Texas
Subject: Question: Looking for wavelet shareware for Macintosh
Hi There
Can someone tell me where I can get some wavelet processing shareware (for
macintosh) on internet? The response will be very appreciate!
Thank you very much in advance!
Ming Wei
[email protected]
--------------------------- Topic #7 -----------------------------------
From: [email protected]
Subject: Question: Biorthogonal Splines in Ten Lectures
Dear Wavelet Enthusiasts:
I have evaluated the expressions for m0(xi) for the bi-orthogonal spline
basis/filters on page 271 in Daubechies' Ten Lectures on Wavelets. The filter
coefficients from those two formulas do not match the coefficients for (N~,N)M0
given on the right hand side of Table 8.2 on page 277 in most of the cases. The
coefficients from page 271 are symmetric and add up to unity, as do the
corresponding cases in Table 8.2.
Am I evaluating something wrong, or is there a typo somewhere? Have any
of you out there encountered the same problem, or have an answer? I would
greatly appreciate it if someone can shed some light on this.
email: [email protected]
Thanks a lot in advance
Jim Scholl
Rockwell Science Center
Thousand Oaks, CA 91360
--------------------------- Topic #8 -----------------------------------
From: Metin Akay <[email protected]>
Subject: Meeting: Artificial Neural Networks in Engineering (ANNIE '94)
Artificial Neural Networks in Engineering (ANNIE '94)
St. Louis, Missouri, November 13-16, 1994
Announcement of The New Track
"Emerging Technologies in Medicine and Biology"
Dear Colleague,
I am organizing a new track on EMERGING TECHNOLOGIES IN MEDICINE AND
BIOLOGY for the upcoming Artificial Neural Networks in Engineering (ANNIE'94)
Conference to be held in St Louis, Missouri, November 13-16, 1994.
This new track will include five special sessions:
1. Time-Frequency and Wavelet Transforms in ENGINEERING, MEDICINE and BIOLOGY.
2. Fuzzy Logic in MEDICINE and BIOLOGY.
3. Neural Networks and Artificial Intelligence in MEDICINE and BIOLOGY.
4. Virtual Really in ENGINEERING, MEDICINE and BIOLOGY.
5. Chaos and Fractals in ENGINEERING, MEDICINE and BIOLOGY.
If you are interested in submitting a paper or papers to this track,
please send a letter of intent, an information sheet that includes the
full name of the author(s), title, address, phone number and FAX or e-mail
address (if applicable) by March 4, 1994 to:
Dr. Cihan Dagli, Conference Chair
223 Engineering Management Building
University of Missouri-Rolla
Rolla, MO 65401-0249 USA
Phone:(314) 341-4374
Fax: (314) 341-6567
e-mail:[email protected]
and one copy to me
Dr. Metin Akay, Organizing Committee Member
Biomedical Engineering Debt.
Rutgers University
P.O. Box. 909
Piscawatay, NJ 08854
Phone:(908) 932-4906
Fax: (908) 235-7048
e-mail:[email protected]
Full papers are due by May 20, 1994. Authors will be notified of the status
of their submittal by July 8, 1994 and camera-ready papers will be due by
August 12, 1994. Approximately six to eight pages will be allocated for each
accepted paper in the proceedings.
I hope you will be able to join us in what promises to be an exciting
meeting discussing the recent advances in Biomedical Engineering Research.
Looking forward to hearing from you.
Sincerely,
Metin Akay, Ph.D.
--------------------------- Topic #9 -----------------------------------
From: Stanhill David <[email protected]>
Subject: Question: Factorization of multidimensional orthogonal wavelets
Does any body know of work done on the
factorization / parametrization of multidimensional
(2D in particular) orthogonal wavelets
and filter-banks ?
Or put in another way,
I would like to know if any body has dealt or
knows of work done on the factorization of
para-unitary (lossless) matrices of polynomials
in two (or more) variables.
Is there any reason why one can not obtain a 2D
generalization of the factorization given by
Vaidyanathan et.al. (and Gohberg, in operator theory)
in the 1D case ?
Thanks.
David Stanhill, [email protected].
--------------------------- Topic #10 -----------------------------------
From: Lars Villemoes <[email protected]>
Subject: Answer: WD 3.2 about 2D-refinement equation
Reply to question in Wavelet Digest 3.2 about 2D-refinement equation
regularity. (Topic #12):
The Sobolev regularity of solutions to multidimensional refinement equations
with finite masks can be found from the spectral radius of a finite matrix.
This is true when some power of the dilation matrix is proportional to the
identity, as in the quincunx or FCO cases. This kind of result can be
found implicitly in [1] and explicitly for the quincunx case in [4].
Hoelder regularity is harder. The technology for continuity is known
to the CAGD community, [2], [3]. The Hoelder exponent can be related
to the spectral radius of the adjoint of the subdivision operator when
it acts on a certain subspace of l^1, see [5].
Lars Villemoes
Dept. Mathematics, Royal Inst. Technology, S 10044 Stockholm.
1. A. Cohen and I. Daubechies: "Non-separable bidimensional wavelet bases"
Rev. Mat. Iberoamericana 1993 pp. 51-137 vol 9 No. 1 (1993)
2. G. Deslauriers, J. Dubois and S. Dubuc: "Multidimensional iterative
interpolation", Can. J. Math. vol 43 pp. 297-312 (1991)
3. N. Dyn and D. Levin "Interpolating subdivision schemes for the generation
of curves and surfaces", pp. 91-106 in Multivariate interpolation and
approximation ed. W. Haussmann and K. Jetter, Birkauser Verlag Basel (1990)
4. L.F. Villemoes: "Sobolev regularity of wavelets and stability of
iterated filter banks", pp. 243-251 in Progress in wavelet analysis and
applications, proc. Toulouse June 1992, ed. Y. Meyer and S. Roques.
Editions Frontieres, France.
5. L.F. Villemoes: "Continuity of quincunx wavelets", to appear in
Applied and Computational Harmonic Analysis (1994).
--------------------------- Topic #11 -----------------------------------
From: [email protected] (Kurt Jetter)
Subject: Book: Multivariate Approximation: From CAGD to Wavelets
Multivariate Approximation: From CAGD to Wavelets
Proceedings of the Internat. Workshop, Santiago, Chile, Sept. 24 - 30, 1992
Editors: Kurt Jetter and Florencio I. Utreras
now available
from World Scientific, Singapore, ISBN 981-02-1442-1
Contents:
G. Baszenski and M. Tasche,
Fast Algorithms for Simultaneous Polynomial Approximation
M. Bozzini and L. Lenarduzzi,
alpha-Spline of Smoothing for Correlated Errors in Dimension Two
M.D. Buhmann,
New Developments in the Theory of Radial Basis Function Interpolation
C.K. Chui and X. Li,
Realization of Neural Networks with One Hidden Layer
P. Costantini,
A General Method for Constrained Curves with Boundary Conditions
F. Fontanella and C. Manni,
A Local Scheme for Comonotone Bivariate Interpolation over Contours
M. Gasca and J.M. Pena,
Sign-regular and Totally Positive Matrices: An Algorithmic Approach
R. Gormaz and P.-J. Laurent,
Some Results on Blossoming and Multivariate B-Splines
K. Jetter,
Riesz Bounds in Scattered Data Interpolation and L_2-Approximation
A. Le Mehaute,
On Multivariate Hermite Polynomial Interpolation
B. Lenze,
Quantitative Approximation Results from Sigma-Pi-Type Neural Network Operators
D. Levin,
Local Interpolation Schemes - From Curves to Surfaces
M.C. Lopez de Silanes,
Some Results on Approximation by Smoothing D^m-Splines
R. Morandi and C. Conti,
Blending Function for Interpolating Non-Uniformly Distributed Data
Ch. Rabut,
Computing Cardinal Interpolants and Wavelets by Using Convolution Products
V. Ramirez-Gonzalez and F.J. Munoz-Delgado,
Some Results on Linear Polynomial Operators
M. Reimer,
On the Existence of Gauss-Like Node Distributions on High-Dimensional Spheres
R. Schaback,
Comparison of Radial Basis Function Interpolants
J. Stoeckler,
Non-Stationary Wavelets
F.I. Utreras,
Multiresolution and Pre-Wavelets Using Radial Basis Functions
--------------------------- Topic #12 -----------------------------------
From: Fred Daneshgaran, Chair of the commun. group, Calif. State Univ.
Subject: Question: Daubechies wavelets for large N
1-Question: I have difficulty generating Daubechies
wavelets and scaling function for very large N (say 100).
Numerical accuracy seems to be the problem. Has anybody
generated very long wavelets and if so how?
2-Question: I need some good references on continuous
time and scale wavelet transforms. More specifically
I am looking for constructive results. Could anybody
recommend some?
Thank you, Fred Daneshgaran.
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