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Wavelet Digest, Vol. 4, Nr. 3.
Wavelet Digest Wednesday, March 15, 1995 Volume 4 : Issue 3
Today's Editor: Wim Sweldens
sweldens@math.scarolina.edu
Today's Topics:
1. Preprint: Spherical Wavelet Transform and Its Discretization
2. Preprint: Unbalanced Haar wavelets for Lp on general measure spaces
3. Preprint: Meteorological PDEs in the wavelet representation
4. Preprint: Dissertation, reprints, and preprints from Jian Lu
5. Thesis: Lareef Zubair (ftp site of postscript file).
6. Software: Wavelet Software Toolbox from Aware
7. Software: Accurate Daubechies filter coefficients in Mathematica
8. Project: (ESF) Converging Computing Methodologies in Astronomy
9. Meeting: Int. Conf. on Neural Networks and Signal Processing
10. Meeting: UK Symposium on Applications of Time-Frequency...
11. Course: Wavelets and Applications, Boston, Mar.-Apr. 1995
12. Course: International Summer School, Jyvaskyla, Finland.
13. Contents: JAT Vol. 80 No. 2, Feb. 95
14. Answer: WD4.2 #28 (wavelets and convolution)
15. Answer: WD4.2 #23 (wavelets and music)
16. Answer: WD4.2 #26 (M-Band Wavelets)
17. Question: wavelets and finance.
18. Question: wavelet denoising function
19. Question: Wavelets for time/frequency compression/expansion
20. Question: looking for wavelet tutorials
21. Question: Looking for a FWT Generalized Inverse Code
22. Question: Approximation errors using spline wavelet packets
23. Question: Analyzing vibration of a structure using wavelets
24. Question: Wavelets and signal extrapolation
25. Question: Statistics of wavelet coefficients
26. Question: Wavelet software for geological data.
27. Question: significance levels for continuous wavelet transform
Submissions:
E-mail to wavelet@math.scarolina.edu with "submit" as subject.
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E-mail to wavelet@math.scarolina.edu with "subscribe" as subject.
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Preprints, references, and back issues can be obtained from our
information servers:
Ftp: ftp.math.scarolina.edu (/pub/wavelet)
Gopher: gopher.math.scarolina.edu
WWW: <A HREF="http://www.math.scarolina.edu/~wavelet/">Here</A>
Current number of subscribers: 4407
Calendar of events:
Jan-Apr : Spline Functions and the theory of Wavelets, Montreal WD 4.2 #14
*Mar 7-Apr 18: Wavelets and Applications Course, Boston WD 4.3 #11
Mar 17-18: Wavelets at the AMS Meeting, Orlando WD 4.2 #17
Mar 24-25: Wavelets at the AMS Meeting, Chicago WD 4.2 #18
Apr 17-21: SPIE: Wavelet applications for dual use, Orlando WD 3.14 #5
May 17-20: Course: Wavelets: Principles, Applic. and Implement. WD 4.2 #17
May 22-24: Course: Fuzzy Logic, Chaos, and Neural Networks, UCLA WD 4.2 #15
May 30-Jun 3: Meeting of the Acoustical Society of America, DC WD 4.1 #7
Jun 26-30: ANU Wavelets Workshop, Canberra, Australia WD 3.6 #6
Jul 3-7 : SIAM ICIAM 95, Hamburg, Germany WD 3.19 #15
Jul 13-14: SPIE: Mathematical Imaging: San Diego WD 4.1 #6
Jul 24-28: Wavelets in Electromagnetics PIERS, Seattle WD 3.18 #11
*Jul 31-Aug 25: International Summer School, Jyvaskyla, Finland WD 4.3 #12
*Aug 31-Sep 1: UK Symp. on Time-Freq. and Time-Scale, Warwick UK WD 4.3 #10
Sep 17-21: ASME Wavelets in Vibrations and Acoustics, Boston WD 3.17 #11
*Dec 10-13: Neural Networks and Signal Processing, Nanjing, China WD 4.3 #9
--------------------------- Topic #1 -----------------------------------
From: freeden@mathematik.uni-kl.de "W. Freeden"
Subject: Preprint: Spherical Wavelet Transform and Its Discretization
Title: Spherical Wavelet Transform and Its Discretization
Authors: Willi Freeden and Ulrich Windheuser
Abstract:
A continuous version of spherical multiresolution is described, starting from
continuous wavelet transform on the sphere. Scale discretization enables us
to construct spherical counterparts to wavelet packets scale discrete wavelets.
Essential tool is the theory of singular integrals on the sphere. It is shown
that singular integral operators forming a semigroup of contraction operators
of class (C_0) (like Abel-Poisson or Gauss-Weierstrass operators) lead in a
canonical way to (pyramidal) algorithms.
Berichte der Arbeitsgruppe Technomathematik, Report No. 125
Prof. Dr. W. Freeden
University of Kaiserslautern
Laboratory of Technomathematics
Geomathematics Group
Kurt-Schumacher-Str. 26
D-67663 Kaiserslautern
Germany
Tel.: xx631-205-2952 xx631-205-3867 Fax: xx631-29081
e-mail: freeden@mathematik.uni-kl.de
--------------------------- Topic #2 -----------------------------------
From: Maria Girardi (girardi@math.scarolina.edu)
Subject: Preprint: Unbalanced Haar wavelets for Lp on general measure spaces
Title: A new class of unbalanced Haar wavelets that form an unconditional
basis for Lp on general measure spaces
Author: Maria Girardi and Wim Sweldens
Abstract:
Given a complete separable $\sigma$-finite measure space $(X,\Sigma,\mu)$
and nested partitions of $X$, we construct unbalanced Haar-like wavelets
on $X$ that form an unconditional basis for $\Lp(X,\Sigma,\mu)$ where
$1<p<\infty$. Our construction and proofs build upon ideas of Burkholder
and Mitrea. We show that if $(X,\Sigma,\mu)$ is not purely atomic, then
the unconditional basis constant of our basis is $(\max(p,q) - 1)$. We
derive a fast algorithm to compute the coefficients.
Industrial Mathematics Initiative, University of South Carolina,
Research Report 1995:02, available through anonymous ftp at
ftp://ftp.math.scarolina.edu/pub/imi_95/imi95_2.ps or imi95_2.ps.gz.
--------------------------- Topic #3 -----------------------------------
From: fournier@cloudy.geology.yale.edu
Subject: Preprint: Meteorological PDEs in the wavelet representation
Title: Wavelet representation of lower-atmospheric long nonlinear wave
dynamics, governed by the Benjamin-Davis-Ono-Burgers equation
Author: Aime' Fournier
This is a modest note illustrating some advantages of the wavelet
representation for describing the local dynamic interactions between scales in
the theoretical evolution of certain dispersive, nonlinear atmospheric waves.
The wave displacement A(x,t) is governed by the PDE:
A = vA - H[A ] - AA ,
t xx xx x
where v is viscosity, and H is the Hilbert transform operator (without which
this would just be the Burgers equation). The use of a wavelet transform over
x to reduce this PDE to a set of ODEs for a 32-dimensional coefficient array
WA(t) is explained. Interactions between all 4 scales and 16 locations
(implied by 32=16+8+4+2+2) are retained, and numerical optimization is not a
concern. Solutions demonstrate such features as localized energy
cascades (normal and inverse) and also separate features at different scales,
such as dispersive wave trains.
I will be giving an oral presentation of this work at the Wavelet Applications
for Dual Use part of SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use
Photonics (WD 3.14 #5).
This preprint is available by nonanonymous request to me at the address below.
I would be happy to e-mail a PostScript file, or mail a hard copy, and would
welcome comments.
Aime' Fournier fournier@cloudy.geology.yale.edu
Yale University
Dept of Geology and Geophysics
PO Box 208109
New Haven CT 06520-8109 USA
tel: 203 432 3146 fax: 203 432 3134
--------------------------- Topic #4 -----------------------------------
From: jian@ece.ucdavis.edu (Jian Lu)
Subject: Preprint: Dissertation, reprints, and preprints from Jian Lu
Dissertation, reprints, and preprints available
I have recently converted my dissertation from Microsoft Word to PostScript
files and would like to make them available to people who are interested. I
apologize for the long delay to those who requested a copy of my dissertation
during the past year. Here is some relevant information:
Title: Signal Recovery and Noise Reduction with Wavelets
Author: Jian Lu
Advisors: Dennis M. Healy, Jr. and John B. Weaver
Institution: Dartmouth College
FTP Server: zoom.cipic.ucdavis.edu (128.120.67.65)
(login as "anonymous" and use your e-mail address as password)
Directory: /pub/jianlu/Dissertation
Please read the README file in the above directory for more information.
Also available on the same FTP server are some reprints and preprints
related to wavelets and signal and image processing. They are:
Directory: /pub/jianlu/Papers
"cmwt-IPL.ps.Z":
J. Lu
"Parallelizing Mallat algorithm for 2-D wavelet transforms,"
Information Processing Letters, vol. 45, pp. 255-259, 1993.
"cmwt-TR.ps.Z":
J. Lu
"Computation of 2-D wavelet transform on the massively parallel computer
for image processing,"
Technical Report, Thayer School of Engineering, Dartmouth College, Feb. 1991.
Extensive presentation of the parallel algorithms reported in [1].
"denoise.ps.Z":
J. Lu, J.B. Weaver, Y. Xu and D.M. Healy, Jr.
"Noise reduction with multiscale edge representation and perceptual criteria,"
Proc. of IEEE-SP Time-Frequency and Time-Scale Analysis, Victoria, Oct. 1992.
"deblur.ps.Z":
J. Lu, D.M. Healy, Jr. and J.B. Weaver
"Signal recovery and wavelet reproducing kernels,"
IEEE Transactions on Signal Processing, July 1994.
"cntrst.ps.Z":
J. Lu, D.M. Healy, Jr. and J.B. Weaver
"Contrast enhancement of medical images using multiscale edge representation"
Optical Engineering, July 1994.
"icip.ps.Z":
J. Lu and D.M. Healy, Jr.
"Contrast enhancement via multiscale gradient transformation"
Proc. of IEEE Intl. Conf. on Image Processing, Austin, TX, November 1994.
"wtpqs.ps.Z":
J. Lu, V.R. Algazi, and R.R. Estes
"Comparison of wavelet image coders using the Picture Quality Scale (PQS),"
preprint, to appear in Proc. of SPIE, Wavelet Applications for Dual-Use,
Vol. 2491, April 1995.
For Mosaic/Netscape users, hypertext links to all above documents are
provided along with a description of related research projects in my WWW
home page whose URL is given at the bottom of this message.
Jian Lu
Center for Image Processing and Integrated Computing (CIPIC)
University of California, Davis, CA 95616, U.S.A.
Tel: (916)752-8199
Fax: (916)752-8894
E-mail: jian@cipic.ucdavis.edu
WWW: http://info.ece.ucdavis.edu/~jian/index.html
--------------------------- Topic #5 -----------------------------------
From: Lareef Zubair <zubair@minerva.cis.yale.edu>
Subject: Thesis: Lareef Zubair (ftp site of postscript file).
Around June 1993, a postscript version of the thesis of Lareef Zubair was
reported as available from the author in the wavelet digest. It is now
available for anonymous ftp from
ftp.funet.fi:pub/sci/papers/misc/ZubairLareef
and is no longer available from the author who may not have internet access.
Studies in Turbulence using Wavelet Transforms for Data
Compression and Scale-Separation
Lareef Zubair
Abstract
Fluid turbulence is characterized by localized structure of multiple scales.
Wavelet transforms are novel techniques which can be used to analyze
localized data with multiple scales efficiently. Motivated by this congruence,
we use wavelet transform to study the structure of turbulent flows.
Wavelet transform is a generic term and we use, in particular,
the continuous wavelet transform and the wavelet-packet transform.
We study the structure of scalar and vortical fields using the continuous
wavelet transform. We assess the wavelet-packet as a tool for data
compression and introduce a wavelet-packet filtering technique.
We use wavelet-packet filtering to pick out features of particular bands of
scales, and based on this delineation, study three facets of the structure of
turbulence -- local isotropy, intermittency and the scaling of structure
functions.
This thesis presents an assessment of the utility of wavelet transforms as
well as a new understanding of some aspects of the structure of turbulence.
--------------------------- Topic #6 -----------------------------------
From: heller@aware.com (Peter Heller)
Subject: Software: Wavelet Software Toolbox from Aware
Aware's WaveTool, An Interactive Tool
for Wavelet and Multirate Filter Design
Cambridge, MA....March 1, 1995.... Aware announces WaveTool 1.1, a general
wavelet and multirate software toolbox for signal processing and scientific
applications. WaveTool enables the user to quickly and easily apply wavelet
methods to a wide range of applications from data compression to
telecommunications, from feature extraction to numerical analysis.
WaveTool is an interactive system for exploring this innovative new
approach. The software is available in two versions, WaveTool Filter
Design and WaveTool Signal Analysis. Features of WaveTool Filter Design
include:
Interactive design of wavelet and multirate filter banks, from
2 to 4096 channels
Point-and-click graphical user interface
A library of predesigned wavelet (orthogonal and biorthogonal) and
cosine-modulated filter banks
The ability to set filter performance parameters such as:
- stopband attenuation
- transition bandwidth
- flatness (vanishing of wavelet moments)
Interactive graphical display, featuring:
-time and frequency domain plotting
-the ability to measure perfect reconstruction error
-display of wavelet scaling functions
The capability of saving filters in ASCII and MATLAB formats.
Log files recording user actions for editing, playback, and software
support
WaveTool Signal Analysis includes all the features of WaveTool Filter Design
plus extensive algorithm prototyping functions, including:
Interactive creation of arbitrary tree structures of filter banks
(such as Mallat's wavelet tree)
Graphical display of filter bank responses at any node of the tree
Multirate analysis and synthesis operations on user data
Choice of boundary handling methods
Choice of subband analyzer or transmultiplexer modes. Graphical
display of subband outputs.
The ability to read and write transform data to ASCII and MATLAB files
The capability to invoke these algorithms via MATLAB-callable
functions for inclusion in larger simulations.
With Wavetool's easy-to-use GUI and MATLAB compatibility, the user doesn't
have to learn a new language or make a big investment.
WaveTool is available for Sun O/S, Solaris and IRIX (SGI) systems. A
graphical demonstration of the software is available on Aware's World Wide Web
site, http://www.aware.com. For more information, or to order a copy, contact
Aware Inc., One Memorial Drive, Cambridge, MA 02142
(617) 577-1700, FAX: (617) 577-1710, E-mail: sales@aware.com
--------------------------- Topic #7 -----------------------------------
From: paul@earwax.pd.uwa.edu.au (Paul Abbott)
Subject: Software: Accurate Daubechies filter coefficients in Mathematica
Accurate computation of the Daubechies filter coefficients using Riesz
factorisation (as described in Chapter 6 of Daubechies' Ten Lectures on
Wavelets) is straightforward to implement in Mathematica:
P[n_, y_] := Sum[Binomial[n-1+k,k] y^k, {k,0,n-1}]
Filter[n_, prec_:20] :=
Module[{poly, r, small, z, l},
poly = P[n/2,1/2 - 1/(4z) - z/4];
r = z /. N[Solve[poly == 0, z], prec];
small = Select[r, Abs[#] < 1 &];
poly = (z+1)^(n/2) Times @@ (z - small);
l = CoefficientList[poly,z] // Reverse;
l / Sqrt[l.l]
] /; EvenQ[n]
The precision, prec, is the working precision not the final precision of
the resulting filter coefficients. For example, here are the D30
coefficients computed using 40 decimals working precision:
(Daubechies[30] = Filter[30, 40]) // Timing
{15.5333 Second,
{0.00453853736157889888145939491, 0.04674339489276627189170969335,
0.2060238639869957315398915009, 0.4926317717081396236067757074,
0.645813140357424358176420912, 0.3390025354547315276912641144,
-0.1932041396091454287063990534, -0.288882596566965646248412501,
0.0652829528487728169228310792, 0.1901467140071229823484893117,
-0.039666176555790944483843668, -0.111120936037231693365671032,
0.033877143923507686208548178, 0.054780550584507612689137903,
-0.025767007328439962585945258, -0.0208100501696930816778848342,
0.0150839180278359023632927446, 0.0051010003604075431697088602,
-0.0064877345603157449951816831, -0.00024175649076162428116672253,
0.00194332398038221154176491233, -0.000373482354137616992009809421,
-0.000359565244362468812164962008, 0.0001558964899205997479471658241,
0.00002579269915531893680925862418,-0.00002813329626604781364755324777,
-6 -6
3.36298718173757980312484521 10 , 1.811270407940577083768510912
10 ,
-7 -8
-6.3168823258816644212015973 10 , 6.13335991330575202905629946 10 }}
It is easy to check how well the conditions on the filter coefficients are
satisfied. Checking the Normalisation:
(Plus @@ Daubechies[30]) - N[Sqrt[2], 50]
-33
0. 10
Daubechies[30].Daubechies[30] - 1
-33
0. 10
Checking the Approximation Conditions:
Daubechies[30] . Table[(-1)^i,{i,30}]
-27
0. 10
Table[Daubechies[30] . Table[i^l (-1)^i,{i,n}], {l,0,30/2-1}]
-27 -39 -37 -36 -35 -34
{0. 10 , 0. 10 , 0. 10 , 0. 10 , 0. 10 , 0. 10 ,
-32 -30 -21 -21 -21 -21
0. 10 , 0. 10 , 0. 10 , 0. 10 , 0. 10 , 0. 10 ,
-22 -22 -20
0. 10 , 0. 10 , 0. 10 }
It is clear that the highest moment condition leads to the greates
cancellation error.
Checking the Orthogonalisation Conditions:
Table[Take[Daubechies[30],k].Take[Daubechies[30],-k],{k,2,30,2}]
-47 -40 -40 -40 -40 -40
{0. 10 , 0. 10 , 0. 10 , 0. 10 , 0. 10 , 0. 10 ,
-39 -39 -38 -38 -38 -38
0. 10 , 0. 10 , 0. 10 , 0. 10 , 0. 10 , 0. 10 ,
-37 -38
0. 10 , 0. 10 , 1.}
Paul Abbott
Department of Physics Phone: +61-9-380-2734
The University of Western Australia Fax: +61-9-380-1014
Nedlands WA 6907 email: paul@earwax.pd.uwa.edu.au
AUSTRALIA
www: http://www.pd.uwa.edu.au/Paul/abbott.html
----
Note from the editor: Using this software, Chris Heil was able to
find new and confirm known typos in the tables in Ingrid Daubechies'
"Ten Lectures". I have summarised these below.
p. 195, N = 4, n = 2, 11th digit should be 2 i.s.o. 3
p. 196, N = 8, 5th coefficient, first digit should be 9 i.s.o. blank
p. 277, N~ = 3, N = 7, coeff of z^{-3} is 363 i.s.o. 336
--------------------------- Topic #8 -----------------------------------
From: fmurtagh@eso.org
Subject: Project: (ESF) Converging Computing Methodologies in Astronomy
Topic- 3-year ESF project: 'Converging Computing Methodologies in Astronomy'
European Science Foundation (ESF) 'scientific network' on "Converging
Computing Methodologies in Astronomy" makes its debut...
An ESF scientific network provides funding for work visits and workshops
over a 3-year period. The above scientific network started at the beginning
of this year. Among central topics are:
- From vision models to image information retrieval:
Methods such as wavelets and multiresolution approaches, mathematical
morphology, and fuzzy methods have proven their worth in the framework of
accessing appropriate information from large image databases. Such methods
must be moulded together to allow semantically-driven access to data.
- The data life-cycle - methodological aspects:
The astronomical data life-cycle is highly digital: data capture is
increasingly on CCD electronic detectors, data are subject to image
processing and statistical treatment, and the final major stage in this
process involves data archiving, and publication. Not surprisingly,
the issues of electronic publishing and of digital libraries are
increasingly central.
- From data integration to information integration:
Particular data integration (data fusion) problems, such as integration
of data associated with different wavelength ranges, are of great
relevance in the context of large space- and ground-based observing
projects. E.g. co-addition in image restoration; image restoration and
filtering approaches which incorporate semantic information on the cosmic
objects of interest; close, complementary use of multi-million object
astronomical catalogs; classification of terabyte data collections.
Long-term access to stored data - what should be the "future of [society's]
memory?". Beyond data, astronomy is all about information. Compression is
central - in a broad sense, compression is summarization, and therefore is
part of the overall process of scientific analysis.
Coordinating committee of the scientific network: A. Bijaoui (Nice), V. Di
Gesu (Palermo), A. Heck (Strasbourg), M.J. Kurtz (Harvard), P. Linde (Lund),
M.C. Maccarone (Palermo) - Chair, R. McMahon (Cambridge), R. Molina (Granada),
F. Murtagh (Munich) - Secretary, E. Raimond (Dwingeloo).
Further information will be available on the WWW at the address:
http://www.hq.eso.org/conv-comp.html
--------------------------- Topic #9 -----------------------------------
From: Ling Guan <ling@ee.su.oz.au>
Subject: Meeting: Int. Conf. on Neural Networks and Signal Processing
Call For Contributions to The Special Session on Wavelet Transform
(The 2nd Int. Conf. on Neural Networks and Signal Processing(ICNNSP '95))
Due to the success of the 1st ICNNSP, the organizing committee decided
to expand the scope of the conference. A special session on wavelet
transform will be organized to the 2nd venue to be held in December,
10-13, 1995, Nanjing, China.
Papers dealing with wavelet transform and its applications in signal
processing are solicited for this session. Topics are included, but
not limited to, the following:
. Wavelet theory.
. Wavelet algorithms.
. Wavelet in texture analysis.
. Wavelet in Signal processing.
. Wavelet in speech processing and recognition.
. Wavelet in image compression and video coding.
. Wavelet in image processing, analysis, and computer vision.
Prospective authors are invited to submit 4 copies of extended summaries
of 1000-1500 words, headed by the title, author's name(s) (please
indicate the corresponding author), address, affiliation(s), e-mail
address, and telephone and fax numbers, with illustrations if necessary,
and an abstract of less than 100 words, to the special session organizer:
Dr. Ling Guan
Department of Electrical Engineering
The University of Sydney
Sydney, NSW, 2006
Australia
tel: (612)351-2154
fax: (612)351-3847
e-mail: ling@ee.su.oz.au
Please also e-mail the abstract to the organizer.
Authors' schedule:
Deadline for extended summary: April 20, 1995
Notification of acceptance: June 30, 1995
Submission of Camera-ready papers: August 30, 1995
--------------------------- Topic #10 -----------------------------------
From: esrhy@eng.warwick.ac.uk
Subject: Meeting: UK Symposium on Applications of Time-Frequency...
TFTS '95 CALL FOR PAPERS
UK Symposium on Applications of Time-Frequency and Time-Scale Methods
organised by the
IEEE UKRI Section SP Chapter
in association with
Department of Engineering, University of Warwick, UK
Venue:
University of Warwick, Coventry, UK
August 31st and September 1st 1995
A two-day symposium on the application of time-frequency and time-
scale methods will be held in late summer 1995.
The purpose of this event is two-fold. Firstly as an introduction to
the subject for those who wish to see whether it will be useful to
their application area. In this regard, various tutorial sessions will
be held. Secondly the meeting will act as a forum to discuss the
various approaches that have been used and to present new results and
application areas. The opening plenary talk will be given by Dr Leon
Cohen (Hunter College,NY) and one of the tutorials will be presented
by Dr Franz Hlawatsch(Technical University,Vienna).
The venue will be Warwick University which has good road and rail
connections with most parts of UK and, via Birmingham International
Airport, is easily accessible from most parts of Europe. The
University is also close to Shakespeare's birthplace, Stratford-upon-
Avon, the town of Warwick with its well-preserved mediaeval castle and
the Regency splendour of Lemington Spa.
Extended summaries of approximately 1000 to 1500 words are now being
invited in the following application areas:
Image Processing
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 (please specify)
Prospective authors should submit three copies of the summaries for
review to Dr. Stuart Lawson, Department of Engineering, University of
Warwick, Coventry, CV4 7AL, UK. Phone: +44 1203 523780, Fax: +44 1203
418922, e-mail: s.lawson@eng.warwick.ac.uk who also should be
contacted for further details and requests for special sessions,
software demonstrations, commercial displays, etc.
The name, affiliation,full address including phone, fax and e-mail
details of the author to whom correspondence should be sent, must be
clearly indicated on the first page of the summary. If the summary is
acceptable, the author(s) will be requested to submit a full paper of
no more than 8 A4 pages which will be included in the symposium
proceedings. Selected authors will be invited to contribute to a
special issue of the Springer journal 'Applied Signal Processing'.
TIMETABLE
Submission of summaries April 1st 1995
Notification of acceptance May 15th 1995
Submission of camera-ready paper July 7th 1995
Technical Program Committee:
John Arnold(Glasgow Univ)
Stuart Lawson(Warwick Univ)
Mark Sandler(King's College,London)
Ken Sharman(Glasgow Univ)
Steve Summerfield(Warwick Univ)
Roland Wilson(Warwick)
Organising Committee:
Stuart Lawson (Chair)
Steve Summerfield(Finance)
Marie Bradley(Local Arrangements)
Claire Lofts(Local Arrangements)
--
Registration of Interest
Name:
Address:
E-mail: Fax;
Phone:
I intend to submit a paper (deadline April 1st) Yes/No
I intend to participate only Yes/No
I will travel with ? guests
--------------------------- Topic #11 -----------------------------------
From: PANKAJ TOPIWALA <pnt@mitre.org>
Subject: Course: Wavelets and Applications, Boston, Mar.-Apr. 1995
The following course on wavelets is offered under the auspices of
the IEEE-Boston Section. This course will cover both theory
and especially application of wavelets to the coding of waveforms,
from speech to images to audio and video. The course itself will
be videotaped and distributed by the IEEE, and a self-study guide
is also planned. See IEEE-Boston Section's publication, The Reflector,
for further details/announcements. To register for this course,
contact the IEEE-Boston Section at bostonieee@aol.com, 617-890-5290.
For information on the videotapes, contact IEEE Educational Activities,
Dr. Bob Kahrman, rkahrman@ieee.org, 908-562-5491.
INTRODUCTION TO WAVELETS AND APPLICATIONS
TIME: 7:30 PM TUESDAYS, MARCH 7, 14, 21 APRIL 4, 11, 18.
PLACE: MITRE "A" LOBBY, BEDFORD, MA (RT. 62 EXIT FROM RT. 3).
INSTRUCTOR: DR. PANKAJ TOPIWALA, MITRE CORP., BEDFORD, MA
GUEST LECTURERS: PROF. GIL STRANG, MIT, CAMBRIDGE, MA
DR. PETER HELLER, AWARE, INC., CAMBRIDGE, MA
DR. SHUBHA KADAMBE, ATLANTIC AEROSPACE, GREENBELT, MD
MR. THOMAS HOPPER, FBI, WASHINGTON, DC
COURSE OUTLINE:
MATHEMATICAL PRELIMINARIES
LINEAR ALGEBRA
LINEAR ANALYSIS
FOURIER TRANSFORM
UNCERTAINTY RELATIONS
EXTREMAL SOLUTIONS
SAMPLING THEORY
WAVELETS: CONTINUOUS AND DISCRETE
GABOR
AFFINE
TIME-FREQUENCY REPRESENTATIONS
NONSTATIONARY SIGNALS
LOCALIZATION
MULTIRESOLUTION ANALYSIS
DIGITAL FILTERS
SUBBAND CODING; QMFs
DAUBECHIES WAVELETS
SYMMETRIC (BIORTHOGONAL) WAVELETS
SPECTRAL FACTORIZATION; CALCULATIONS
APPLICATIONS
1-D SIGNAL PROCESSING
TRANSIENT DETECTION
PATTERN RECOGNITION
RADAR
SPEECH
COMPRESSION: AUDIO
IMAGE PROCESSING
EDGE DETECTION
COMPRESSION
STILL
MULTISPECTRAL
VIDEO
WSQ FINGERPRINT COMP.
EXTENSIONS
MULTIRATE SYSTEMS AND FILTERS
HIGHER-D WAVELETS; NONSEPARABLE
MULTIWAVELETS
--------------------------- Topic #12 -----------------------------------
From: Laura Varpula <iss5@tukki.jyu.fi>
Subject: Course: International Summer School, Jyvaskyla, Finland.
The University of Jyvaskyla, Finland, is organizing
THE 5th INTERNATIONAL SUMMER SCHOOL
31 July - 25 August 1995.
What is the International Summer School?
The aim of the International Summer School is to offer
advanced courses in various topics to both undergraduate
and graduate students.
What is the programme of the Summer School?
The programme of the 5th International Summer School
consists of courses on the following topics:
Mathematics:
Introduction to wavelets
Signal processing and multifractal analysis
with wavelets
Applications of wavelets to analysis
Applied mathematics
Computer science
Statistics
Biology
Physics
Chemistry
How to apply for the Summer School?
To apply for the 5th International Summer School
please fill in the application form
(http://www.math.jyu.fi/summerschool.html)
and send it to the organizers by 31 March 1995.
What are the costs?
There is no tuition fee for the Summer School.
Housing and living expenses and travel costs
are the responsibility of each participant.
For further information please contact:
The 5th International Summer School
Faculty of Mathematics and Natural Sciences
University of Jyvaskyla
P.O. Box 35
FIN-40351 Jyvaskyla
FINLAND
Phone: +358 41 602 205
Fax: +358 41 602 201
E-mail: iss5@tukki.jyu.fi
WWW: http://www.math.jyu.fi/summerschool.html
--------------------------- Topic #13 -----------------------------------
From: Marilyn Radcliff <radcliff@math.ohio-state.edu>
Subject: Contents: JAT Vol. 80 No. 2, Feb. 95
Journal of Approximation Theory, Volume 80, Number 2, Feb. 1995
Table of Contents
Mariana Mar\u{c}okov\'a
Equiconvergence of two Fourier series
151--163
Wu Li
Convergence of P\'olya algorithm and continuous metric selections in space of
continuous functions
164--179
Frank Deutsch, Vasant A. Ubhaya, and Yuesheng Xu
Dual cones, constrained $n$-convex %L_p$-approximation, and perfect splines
180--203
Yingkang Hu, Dany Leviatan, and Xiang Ming Yu
Copositive polynomial and spline approximation
204--218
A.L. Levin and D.S. Lubinsky
Orthogonal polynomials and Christoffel functions for
$\exp(-|x|^\alpha)$, $\alpha\le1$
219--252
Noli N. Reyes
An asymptotic formula in best approximation
253--266
J.C. Sevy
Lagrange and least-squares polynomials as limits of linear combinations of
iterates of Bernstein and Durrmeyer Polynomials
267--271
G. Derfel, N. Dyn, and D. Levin
Generalized refinement equations and subdivision processes
272--297
Journal of Approximation Theory, Volume 80, Number 3, Mar. 1995
Lars-Erik Andersson, Tommy Elfving, Georgy Iliev, and Krassimira Vlachkova
Interpolation of convex scattered data in $R^3$ based upon an edge convex
minimum norm network
299--320
Fernanado Cobos, Pedro Fern\'andez-Mart\'inez, and Tomas Schonbek
Norm estimates for interpolation methods defined by means of polygons
321--351
Leonid Golinskii
On second kind measures and polynomials on the unit circle
352--366
Rasul A. Khan
Reverse martingales and approximation operators
367--377
Yuichi Kanjin and Ryozi Sakai
Convergence of the derivatives of Hermite-F\'ejer interpolation polynomials of higher order based at the zeros of Freud polynomials
378--389
Erich Novak
Optimal recovery and $n$-widths for convex classes of functions
390--408
R. Getsadze
On a problem of N. Kirchoff and R.J. Nessel
409--422
Vasant A. Ubhaya and Yuesheng Xu
Constrained $L_p$-approximation by generalized $n$-convex functions induced by ECT-Systems
423--449
Author Index for Volume 80
--------------------------- Topic #14 -----------------------------------
From: lindsey@redneck.ent.ohiou.edu (Alan Lindsey ECE Grad)
Subject: Answer: WD4.2 #28 (wavelets and convolution)
An unequivocal NO way! See paper wvltconv.ps obtainable from
voyager.cns.ohiou.edu in directory /pub/papers/lindsey
entitled "The nonexistence of wavelet functions admitting a Wavelet Convolution
Theorem of the Fourier Type." This should answer your question.
Alan Lindsey
lindsey@redneck.ent.ohiou.edu
--------------------------- Topic #15 -----------------------------------
From: msm2@eng.cam.ac.uk (Margaret Margereson)
Subject: Answer: WD4.2 (wavelets and music)
Wavelets and Music
You may be interested in my paper Harmonic and Musical Wavelets, Proc. R.
Soc. Lond A, 1994, Volume 444, pages 605-620. There is also an earlier
paper Harmonic Wavelet Analysis, Proc. R. Soc. Lond A, 1993, Volume 443,
pages 203-225.
Best wishes,
Professor David Newland
Cambridge University Engineering Department
Trumpington St
Cambridge, CB2 1PZ, UK
--------------------------- Topic #16 -----------------------------------
From: Eric Veum <veum@math.arizona.edu>
Subject: Answer: WD4.2 (#26) (M-Band Wavelets)
Q: Do any of you out there know where I can obtain C or Fortran codes for
simple M-band wavelet transforms on signals or images?
A: Yes. ANSI C algorithms for M-Band compactly supported o.n. discrete wavelet
transformations can be found at:
pandemonium.physics.missouri.edu:/pub/wavelets/p-wavelets.tar.Z
--------------------------- Topic #17 -----------------------------------
From: amegroud@pss.enpc.fr (amegroud)
Subject: Question: wavelets and finance.
Has anyone done any work with wavelets in finance ? I would
appreciate any papers or software on the subject .
Please write to me at :
amegroud@paris.enpc.fr
Thanks for any and all help.
Tayeb Amegroud
Ecole Natinale des Ponts et Chaussees - Paris
--------------------------- Topic #18 -----------------------------------
From: K.H.Boonzaaijer@ct.utwente.nl (K.H.Boonzaaijer)
Subject: Question: wavelet denoising function
Greetings
I am studying on peak analysis of Flow Injection Analysis(FIA) peaks.
Therefore it is required do denoise these peaks. Fourier filtering does not
give a satisfactory denoised peak. Now my question is : Is there a simple C
/gcc library which contains a denoising function. And if so where can I find it?
Thanks in advance
Karel Boonzaaijer
K.H.Boonzaaijer@utct.ct.utwente.nl
--------------------------- Topic #19 -----------------------------------
From: sri@phy.ucsf.edu
Subject: Question: Wavelets for time/frequency compression/expansion
I am looking for some information on the feasibility of using wavelet
transforms for time and/or frequency compression/expansion of 1-D signals.
Are there anyone working on this area or aware of people who are working
in this area? Any pointers on whether it is feasible or issues involved will
be appreciated.
Srikantan S. Nagarajan
sri@phy.ucsf.edu
--------------------------- Topic #20 -----------------------------------
From: svenkat@cdotp.ernet.in (SVENKAT)
Subject: Question: looking for wavelet tutorials
Dear waveleters,
Being a new member to this newsgroup and also since I do not have any
idea on wavelet transforms, I would like someone to send me a tutorial
on Wavelet Transforms.
Thanking You,
venkatesh subramanian email- svenkat@cdotp.ernet.in
SDH S/W group, III floor,
C-DOT, 39 Pusa Road
New Delhi. 110 005.
India.
--------------------------- Topic #21 -----------------------------------
From: war@engr.ucf.edu (Wisam A. Rabadi)
Subject: Question : Looking for a FWT Generalized Inverse Code
Dear readers,
I'm looking for a C code to carry out Beylkin's FWT General Inverse, or the
matrix multiplication using wavelets (described in Wavelets and their Applications, CH III, Ruskai et al., ed.)
I would very much appriciate any reply or suggestion,
Wessam Rabadi Center for Research and Education in Optics and Laser (CREOL)
University of Central Florida Orlando, FL 32816
Tel (407) 273-9819 Fax (407) 823-5835
war@engr.ucf.edu
--------------------------- Topic #22 -----------------------------------
From: armin@eng.tau.ac.il <Armin Shmilovici>
Subject: Question: Approximation errors using spline wavelet packets
I am using spline wavelets for approximating control surfaces in
nonlinear control applications. I am looking for a reference or idea on
how to find an expression which describes the approximation error of a
function (preferebly multidimensional) or a bound on the error when it is
approximated with spline wavelet packets. I am aware of works about the
approximation power of spline wavelets but I can't figure how to expand
it to spline wavelet packets
Any suggestion will be welcomed
Armin Shmilovici
Dept. of Industrial Engineering Tel-Aviv University 69978 Tel-Aviv, ISRAEL
--------------------------- Topic #23 -----------------------------------
From: aesoph@ncemt1.ctc.com (Aesoph, Michael)
Subject: Question: Analyzing vibration of a structure using wavelets
Dear All:
I have recently been tasked to analyze the vibration of a structure
following a huge impact loading. The response is suspected to be some
what non-linear and transient in nature. I am looking for a wavelet
transform that will allow me to do basically a joint time and frequency
analysis of this transient waveform. I need only the code and an
overview of how to use it effectively. I prefer ANSI C, but can convert
FORTRAN or other code to what I need. Thanks in advance.
Michael D. Aesoph
Associate NDE Engineer
--------------------------- Topic #24 -----------------------------------
From: godfrey@hfrd.dsto.gov.au
Subject: Question: Wavelets and signal extrapolation
I am currently looking through a number of papers on signal
extrapolation with wavelets written by Dr s Xia, Lin, Jay Kuo and
Zhang. I am hoping to apply the algorithms to High Frequency Radar data.
The format of the data is either 128 or 256 samples, 1-dimensional, complex.
We obtain the wavelet coefficients via MRA using Daubechies wavelets for
which the signal needs to be a power of 2.
Given this, I need to determine first of all if the signal is scale-time
limited. I tried the condition x=D^-1*P_JK*D*x, but couldn't see how P_JK was
to be applied as the wavelet coefficients were non-zero for all scales J.
I would like to hear from anyone who has implemented the Discrete
Generalized Papoulis-Gerchberg alogrithm on measured data. Is there any
paricular software available ?
Susan Godfrey
HF Radar Division Defence Science and Technology Organisation
Salisbury, South Australia 5108
phone : +61 8 259 6775 fax : +61 8 259 6673
e-mail: godfrey@hfrd.dsto.gov.au
--------------------------- Topic #25 -----------------------------------
From: arciero@ercole.cefriel.it (Area DSP)
Subject: Question: Statistics of wavelet coefficients
I'm interested in statystics of wavelets' coefficients belonging
to different levels of analysis (i.e.scales).
I would like to know if a model exist for the pdf and if such a model
strongly depends on the kind of filter used to perform analysis.
Thanks in advance
Giuseppe Arciero e-mail arciero@mailer.cefriel.it
CEFRIEL - Politecnico di Milano Via Emanueli, 15 20126 Milano (Italy)
Tel.: +39-2-66100083 Fax : +39-2-66100448
--------------------------- Topic #26 -----------------------------------
From: baughd@euler.mcs.utulsa.edu (Donna Baughman)
Subject: Question: Wavelet software for geological data.
Hello,
I am looking for information on wavelet software. I need to apply a
wavelet transform to a set of geological data in order to compress it. The
data is in the form of x, y coordinates along with the value at that
point. Our system does not have S or S+ available. Any suggestions would
be much appreciated.
Thanks in advance,
Donna M. Baughman University of Tulsa Tulsa, OK
baughd@euler.mcs.utulsa.edu
--------------------------- Topic #27 -----------------------------------
From: "Steven Meyers" <meyers@matsuno.ocean.fsu.edu>
Subject: Question: significance levels for continuous wavelet transform
I am using the continuous wavelet transform as a spectral analysis tool
and was wondering if there has been any work on significance levels,
confidence intervals, et cetera for the continuous wavelet transform.
I'll collect responses and post a summary.
Thanks for any pointers.
Dr. Steven Meyers meyers@coaps.fsu.edu
Associate Director phone: (904) 644-1168
Center for Ocean-Atmosphere FAX: 904-644-4841
Prediction Studies http://www.coaps.fsu.edu/bios/meyers.html
Florida State University Tallahassee, FL 32306-3041
-------------------- End of Wavelet Digest -----------------------------