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Wavelet Digest, Vol. 4, Nr. 6.



Wavelet Digest       Friday, June 2, 1995                Volume 4 : Issue 6


Today's Editor: Wim Sweldens
                Katholieke Universiteit Leuven, Belgium
                wim.sweldens@cs.kuleuven.ac.be


Today's Topics:

     1. Book:     Wavelets: a beginner's guide (in Japanese)
     2. Preprint: Connection Coefficients (+ software)
     3. Preprint: Building your own Wavelets at Home
     4. Preprint: Visualization of Multidimensional Shape and Texture...
     5. Preprint: Papers on the Wavelet Extrapolation method.
     6. Preprint: Density estimation via wavelets 
     7. Software: Wavelet Signal Processing Workstation (MATLAB Demo)
     8. Software: Computing Refinable Integrals
     9. Meeting:  Still-Image Compression 1996 (CFP)
    10. Meeting:  UCLA short course on "Wavelet Transform Applications"
    11. Job:      TU Berlin, Image compression using wavelets
    12. Contents: JAT Vol. 81, No. 2, May 95
    13. Contents: JAT Vol. 81, No. 1, April 95
    14. Contents: Constructive Approximation, vol 11, no 2
    15. Question: Wavelet packega for JACAL ?
    16. Question: Wavelet use in Seismic Engineering
    17. Question: Looking for help starting out in wavelets
    18. Question: Applications of Wavelets Analysis to Time-Series

Submissions:
  E-mail to wavelet@math.scarolina.edu with "submit" as subject.

Subscriptions:
  E-mail to wavelet@math.scarolina.edu with "subscribe" as subject.
  To unsubscribe, e-mail with "unsubscribe" followed by your e-mail
  address as subject. To change address, unsubscribe and resubscribe.

Preprints, references, and back issues can be obtained from our
information servers:
  Ftp:    ftp.math.scarolina.edu (/pub)
  Gopher: gopher.math.scarolina.edu
  WWW:    <A HREF="http://www.math.scarolina.edu/~wavelet/">Here</A>


Current number of subscribers: 4697

Calendar of events:

 Jun  6-8 : Mathematics and Physics of Wavelets, College Park MD   WD 4.4  #12
 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  8-17: Fractal Image Encoding and Analysis, Trontheim, Norway WD 4.4  #10
 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 11-15: UCLA short course on "Wavelet Transform Applications"  WD 4.6  #10
 Sep 17-21: ASME Wavelets in Vibrations and Acoustics, Boston      WD 3.17 #11
 Sep 20-23: Advances In Biomedical Signal And Image Proc, Monteal  WD 4.5  #9
 Dec 10-13: Neural Networks and Signal Processing, Nanjing, China  WD 4.3  #9
 -- 1996 --
 Jan 28-Feb 2: Digital Video Compression, San Jose, CA             WD 4.5  #8 
*Jan 27-Feb 2: Still-Image Compression, Photonics West, San Jose   WD 4.6  #9

 
--------------------------- Topic #1 -----------------------------------
From: mei@trlvm.vnet.ibm.com
Subject: Book: Wavelets: a beginner's guide (in Japanese)

      Wavelets: a beginner's guide (in Japanese)

      Prof. Susumu SAKAKIBARA (Iwaki Meisei Univ.)

published by: Tokyo Denki University Shuppan Kyoku
price: 4000 yen + 120 yen (consumption tax), hardcover

This 225-page beginner's guide (which takes an intuitive
approach) contains many useful examples, computer programs,
as well as a CD-ROM disk with Mathematica programs.

A translation is planned, but no concrete schedule (yet) for
the publication date of the translation.

                        Mei Kobayashi
                        IBM Tokyo Research Laboratory
                        internet: mei@trlvm.vnet.ibm.com

--------------------------- Topic #2 -----------------------------------
From: Juan Restrepo <restrepo@mcs.anl.gov>
Subject: Preprint: Connection Coefficients (+ software)

Hello, many have asked for a copy of 

Aware Technical Report AD910708, 1991 
"The Evaluation of Connection Coefficients of Compactly Support Wavelets" 
by A. Latto, H. L. Resnikoff and E. Tenenbaum.

Firstly, a better reference is

%A A. Latto
%A H. L. Resnikoff
%A E. Tenenbaum
%T The Evaluation of Connection Coefficients of Compactly Supported Wavelets
%R Proceedings of the French-USA Workshop on Wavelets and Turbulence
%C Princeton
%I Springer-Verlag, New York
%D 1991

Since I offer some companion papers and the software to compute these
things, I get a lot of requests for the paper. Someone borrowed and
never returned my only copy of the paper, so I can't forward it to
anyone. However, my papers paraphrase in sufficient detail (I think) the
calculation devised by the above authors.

You can retrieve my papers and the code via anonymous ftp from
info.mcs.anl.gov. The directory is /pub/tech_reports/restrepo and
the files you want are ntuples.tar.gz
		       wavper-t.ps.Z
		       wlg-t.ps.Z

alternatively, you can download the software and the papers from my web
page with coordinates given below. I also want to thank several readers
for pointing out a couple of typos in the first paper. I will correct 
them as soon as possible.

Juan Mario Restrepo
Mathematics and                     (708) 252-4668
Computer Science Division           (708) 252-5986 FAX
Bldg. 221 Argonne National Laboratory 9700 S Cass Ave Argonne IL 60439
e-mail:               restrepo@mcs.anl.gov
www-home page:        http://www.mcs.anl.gov/people/restrepo/index.html

--------------------------- Topic #3 -----------------------------------
From: Wim Sweldens, sweldens@math.scarolina.edu
Subject: Preprint: Building your own wavelets at home

Title: Building your own wavelets at home
Authors: Wim Sweldens and Peter Schroeder

Abstract: We give an practical overview of three simple techniques to
construct wavelets under general circumstances: interpolating
subdivision, average interpolation, and lifting. We include examples
concerning the construction of wavelets on an interval, weighted
wavelets, and wavelets adapted to irregular samples.

Report 1995:5, Industrial Mathematics Initiative, Department of Mathematics,
University of South Carolina.

ftp://ftp.math.scarolina.edu/pub/imi_95/imi95_5.ps or imi95_5.ps.gz

--------------------------- Topic #4 -----------------------------------
From: "Lars Lippert" <lippert@inf.ethz.ch>
Subject: Preprint:  Visualization of Multidimensional Shape and Texture...

Title:  Visualization of Multidimensional Shape and Texture
        Features in Laser Range Data using Complex-Valued Gabor Wavelets

Preprint & figures available by anonymous ftp from ftp.inf.ethz.ch
Directory: /doc/papers/is/cg/ieee-tvcg95

Authors:  M. H. Gross, R. Koch

Abstract:
This paper describes a new method for visualization and analysis of
multivariate laser range data using complex-valued non-orthogonal
Gabor wavelets, principal component analysis and a topological
mapping network. The initial data set that provides both shape and
texture information is encoded in terms of both amplitude and phase
of a complex valued 2D image function. A set of carefully designed
oriented Gabor filters performs a decomposition of the data and
allows for retrieving local shape and texture features. The feature
vector obtained from this method is multidimensional and in order to
evaluate similar data features, further subspace methods to transform
the data onto visualizable attributes, such as R,G,B, have to be
determined. For this purpose, a feature-based visualization pipeline
is proposed consisting of principal component analysis, normalization
and a topological mapping network. This process finally renders a
R,G,B subspace representation of the multidimensional feature vector.
Our method is primarily applied to the visual analysis of features in
human faces - but is not restricted to that.

anonymous ftp://ftp.inf.ethz.ch/doc/papers/is/cg/ieee-tvcg95

Lars Lippert                             Computer Graphics Research Group
Institute for Information Systems, Swiss Federal Institute of Technology 
Tel.: +41-1-632 71 21                         Email:  lippert@inf.ethz.ch
Fax : +41-1-632 11 72                         Office: IFW E41
http://www.inf.ethz.ch/department/IS/cg/

--------------------------- Topic #5 -----------------------------------
From: Kevin Amaratunga <kevin@phaeton.mit.edu>
Subject: Preprint: Papers on the Wavelet Extrapolation method.

The following preprints are now available on line.  These papers describe
the Wavelet Extrapolation method for the treatment of finite length data
sequences and for the solution of PDEs on domains of arbitrary shape.
Please see our web page at

	http://www-iesl.mit.edu/pub_docs/Wavelets/wavelet_docs.html

for downloading information and for image examples.  (Alternatively, email
us at the addresses given below.)

Title: A Discrete Wavelet Transform Without Edge Effects

IESL Technical Report No: 95-02

Authors: John R. Williams and Kevin Amaratunga

Abstract:
In this paper, we use Daubechies' orthogonal wavelets to develop a
discrete wavelet transform which does not exhibit edge effects.  This
work follows on from our earlier work on high order polynomial
extrapolation methods for initial and boundary value problems.  The
underlying idea is to extrapolate the data at the boundaries by
determining the coefficients of a best fit polynomial through data
points in the vicinity of the boundary.


Title: Time Integration Using Wavelets

Presented at: SPIE Aerosense `95 - Wavelet Applications II
              Vol 2491, 894-902, Orlando, FA, 1995.

Authors: Kevin Amaratunga and John R. Williams

Abstract:
In this work, we describe how wavelets may be used for the temporal
discretization of ODEs and PDEs.  A major problem associated with the
use of wavelets in time is that initial conditions are difficult to
impose.  A second problem is that a wavelet-based time integration
scheme should be stable.  We address both of these problems.

Firstly, we describe a general method of imposing initial conditions,
which follows on from some of our recent work on initial and boundary
value problems.  Secondly, we use wavelets of the Daubechies family as
a starting point for the development of stable time integration
schemes. By combining these two ideas we are able to develop schemes
with a high order of accuracy.  More specifically, the global error is
O(h^{p-1}), where p is the number of vanishing moments of the
original wavelet.  Furthermore, these time integration schemes are
characterized by large regions of absolute stability, comparable to
increasingly high order BDF methods. In particular, Daubechies D4 and
D6 wavelets give rise to A-stable time-stepping schemes.

In the present work we deal with single scale formulations.  We note,
however, that the standard multiresolution analysis for orthogonal
wavelets on L^2(R) applies here.  This opens up interesting possibilities 
for treating BVP's and IVP's at multiple scales.


Title: High Order Wavelet Extrapolation Schemes for Initial Value Problems
       and Boundary Value Problems

IESL Technical Report No: 94-07

Authors: John R. Williams and Kevin Amaratunga

Abstract:
One of the main problems with the Wavelet-Galerkin method is the
treatment of boundary conditions.  Here, we describe a general method
of imposing boundary conditions based on polynomial extrapolation.
The resulting schemes are exact if the solution is a polynomial of
degree p-1, where p is the number of vanishing moments of the
wavelet.  More generally, numerical evidence confirms that the error
decays as O(h^p) for pure Dirichlet boundary conditions and as
O(h^{p-1}) for Neumann boundary conditions. Boundary conditions can
be imposed at points other than mesh points without loss of accuracy.
This makes the method suitable for boundaries of arbitrary shape.

We extend the polynomial extrapolation idea to initial value problems
in two ways.  First, we describe how the method may be used to impose
initial conditions.  Secondly, the method is used to develop
stable time-stepping schemes for the wavelet coefficients.  These
schemes have a global error of O(h^{p-1}), and they have large
regions of absolute stability, comparable to those of increasingly
high order BDF methods.  In particular, Daubechies D4 and D6 wavelets
give rise to A-stable time-stepping schemes.

In the present work we deal with single scale formulations.  We note,
however, that the standard multiresolution analysis for orthogonal
wavelets on L^2(R) applies here.  This leaves us with interesting
possibilities for treating BVP's and IVP's at multiple scales, without
the use of special boundary wavelets.

John R. Williams                          Kevin Amaratunga
<john@iesl.mit.edu>                       <kevin@iesl.mit.edu>
Associate professor / Director            Graduate student / Research assistant

         Intelligent Engineering Systems Laboratory, Room 1-253
         Department of Civil and Environmental Engineering
         Massachusetts Institute of Technology
         77 Massachusetts Avenue
         Cambridge, MA 02139, USA

--------------------------- Topic #6 -----------------------------------
From: Brani Vidakovic <brani@isds.Duke.EDU>
Subject: Preprint: Density estimation via wavelets 

A paper on density estimation via wavelets is available
in:
   pub/brani/papers/
as
   SqrtDenWav.ps.

Your comments are most welcome. 

Estimating the square root of a density via compactly supported wavelets

Aluisio Pinheiro   University of North Carolina -- Chapel Hill
Brani Vidakovic    Duke University

A large body of nonparametric statistical literature is devoted to
density estimation. Overviews are given in Silverman (1986) and
Izenman (1991).  This paper addresses the problem of univariate
density estimation in a novel way.  Our approach falls in the class of
so called projection estimators, introduced by {\v C}encov (1962).
The orthonormal basis used is a basis of compactly supported wavelets
from Daubechies' family. Kerkyacharian and Picard (1992), Donoho et
al. (1993), and Delyon and Juditsky (1993), among others, applied
wavelets in density estimation.  The local nature of wavelet functions
makes the wavelet estimator superior to projection estimators that use
classical orthonormal bases (Fourier, Hermite, etc.)

Instead of estimating the unknown density directly, we estimate the
square root of the density, which enables us to control the
positiveness and the $L_1$- norm of the density estimate. However, in
that approach one needs a pre-estimator of density to calculate sample
wavelet coefficients.  We describe VISUSTOP, a data-driven procedure
for determining the maximum number of levels in the wavelet density
estimator.  Coefficients in the selected levels are thresholded to
make the estimator parsimonious.

Our method is illustrated on the Galaxy velocity data set (Roeder,
1990) and implemented in S-Plus. The method can be readily extended to
a multidimensional case and other wavelet bases.

Key words and phrases:
Wavelets, Density Estimation, Thresholding.

1991 AMS Subject Classification: 62G07

Brani Vidakovic tel. office 919-684-8025 DUKE, ISDS, Box 90251
tel. home 919-309-9638 Durham, NC 27708-0251

--------------------------- Topic #7 -----------------------------------
From: Anthony Teolis <tonyt@drsews.nrl.navy.mil>
Subject: Software: Wavelet Signal Processing Workstation (MATLAB Demo)

We have made the following wavelet signal processing software
available to the wavelet community.


	Wavelet Signal Processing Workstation Version 0.1D (MATLAB Demo)

 		(c) Copyright 1995
       			AIMS, Inc. 
       			6110 Executive Blvd., Suite 850 
       			Rockville, MD 20852-3904

       		Inquiries, comments, and/or bug reports may be sent to: 

               		tonyt@dr_sews.nrl.navy.mil


Introduction:

	This document explains the functionality of a DEMO version of
an interactive software tool for the analysis of 1 and 2 dimensional 
signals using continuous and discrete wavelet transformations. 

	The software runs in MATLAB versions 4.0 and later.

Downloading:

	The demo version m-files (decommented) are available via
guest login or ftp to dr_sews.nrl.navy.mil. The guest password is 
"WSPW_demo". A compressed and tar-ed version "WSPW_demo.tar.Z"
is located in the guest home directory.


Background:

	The WSPW is built on a hierarchy of user levels which consist of
		(i) Graphical User Interface,
		(ii) Workstation Module,
		(iii) MATLAB, and
		(iv) C-code.
Users may choose to operate at any one of these levels although the
contribution of this software is at levels (i) and (ii).  The main
trade-off associated with user operation level is between flexibility 
in functionality (or speed) and ease of use. At the highest level, 
the Graphical User Interface, the user is constrained by the functionality 
of the interface. On the Workstation Module level users may construct 
their own user interfaces, while the MATLAB and C-code levels are 
conventional MATLAB.

In this demo distribution the user is effectively restricted to operate on 
the Graphical User Interface level.


Functionality:

	The WSPW Demo supports the ``continuous'' Fourier and the
``continuous'' and discrete wavelet analyses of a predefined set of 
1 and 2 dimensional signals. 

	On startup the WSPW 0.1D Signal Loader window is spawned. This
window consists of three main user interface panels which hold graphical
controls which direct processing actions. The three panels are (from
bottom to top) Load panel, Transform panel, and the Data Type panel.
Each is explained below:

	Data Type Panel

		The Data Type panel is used to specify either ``sig'' (1D) 
	or ``image'' (2D) processing. Selection of one or the other effects 
	the other choices in the other user interface panels. 


	Transform Panel

		The Transform panel is used to specify a transformation 
	to apply to a loaded signal. Possible transforms include the
	``sfft'' (continuous Fourier transform), ``cwt'', (continuous
	wavelet transform), and ``fwt'' (fast wavelet transform) for 
	1D signals and just the `fwt2'' (two dimensional fast wavelet 
	transform) for image data. Auxiliary windows are spawned by
	the selection of any of the wavelet type transforms. The two
	possible types of interface windows spawned are either the ``Discrete 
	Wavelet Interface'' or the ``Parameteric Bandlimited (PBL) Wavelet 
	Interface''. These interfaces are described below.

		The ``alpha'' parameter is generic transform parameter.
	For the sfft it is unused, and for the cwt and fwt(2), its
	value is interpreted as a frequency upshift and scale limit
	respectively. For the cwt the signal to be transformed is first
	upshifted by the quantity ``alpha'' along the frequency axis
	before the wavelet transform is computed. For the fwt or fwt2
	alpha is used to specify the number of scales to compute.

		Depressing the ``Xform'' button causes the specified
	transform to be computed on the loaded data.


	Load Panel

		The Load panel is used to select and load a signal
	 from a predefined menu of signals. The user may also specify
	 a level at which to add noise to the clean signal. Depressing
	 the Load button causes the specified signal with the specified
	 noise to be loaded (actually synthesized) and displayed in the
	 body of the Loader window. The noise is additive Gaussian noise
	 with zero mean and standard deviation given by the noise parameter.
	 (SNRs are roughly 10 log10(std)).

		All of the 1 dimensional (1D) signals in the ``File(s)''
	menu are analytic, i.e., the have Fourier transforms which
	vanish for negative frequencies, and therefore are complex signals.
	Once a 1D signal is loaded its real part and magnitude are
	displayed in separate graphs in the Loader window. Processing
	can now be performed on the loaded signal.



Wavelet Interfaces:

	Discrete Wavelet Interface 

		The discrete wavelet interface is minimal in that it
	allows only the specification of Daubechies type wavelets of
	orders 1 through 9. A single menu and button comprise the
	interface panel. Depressing the button ``Daub'' causes the
	Daubechies high and low pass filter coefficients as well
	as the wavelet and scaling functions  to be computed and 
	displayed.


	Parametric Band Limited (PBL) Wavelet Interface

		The PBL wavelet interface allows the specification of
	a parameterized family of analyzing wavelets and a corresponding
	filter bank. Analyzing wavelets are specified directly in the 
	frequency domain by three parameters: order, bandwidth (BW), and
	center frequency (CF). Filter banks are generated by two additional
	parameters: number of filters Nf and a dilation constant a0>1. The 
	bank has filters with impulse responses 

			{ D_(a0^k) g }, 	k = 1,2, ..., Nf,

	where D_s is the L^2 dilation by s operator defined as

			(D_s g)(t) = s^(1/2) g(s t).

	Depressing the ``Wavelet'' button causes the specified wavelet
	to be computed and displayed in time and frequency.
	Depressing the ``Bank'' button cause the specified wavelet to
	be computed and displayed in time and frequency (just as the 
	``Wavelet'' button does) and additionally computes and displays
	the magnitude Frequency transfer functions for the entire
	filter bank and the total filter bank support function G where

			G = sum_{k=1}^Nf | (D_(a0^k) g)^ |^2.

	This function is directly related to the inversion properties
	of the transform. 

--------------------------- Topic #8 -----------------------------------
From: Angela Kunoth <kunoth@isc.tamu.edu>
Subject: Software: Computing Refinable Integrals

Author:          Angela Kunoth

Applications:   * Evaluating refinable functions in 1D-3D
                * Computing inner products in 1D-3D
                * Computing entries of stiffness matrices
                  in Petrov-Galerkin schemes in 1D-3D

Based on the theory developed in [W.Dahmen, C.A. Micchelli, Using 
the refinement equation for evaluating integrals of wavelets, Siam 
J. Numer. Anal. 30, 1993, 507-537], I have written a C++-program 
that computes function values and derivatives of refinable functions 
(generators) and integrals of refinable functions for up to four 
factors in the integral in one and two dimensions.   In three dimensions,
integrals with at most three factors can be computed.   The routines
calculate the desired values exactly up to round-off while avoiding
any quadrature rules by using the refinement equations for the 
computations.   As input data, only the mask coefficients of the
refinable functions and some parameters like the dimension of the
underlying domain and the number of refinable functions are needed.

A comprehensive documentation of the program in latex-format called 

     Computing Refinable Integrals --- Documentation of the Program 
                                   --- Version 1.1 ---

which summarizes the theory and contains several examples (Daubechies'
generators, B- and Box Splines) can be obtained via anonymous ftp as 
follows

> ftp ftp.igpm.rwth-aachen.de 
login: anonymous
password: your e-mail address
>cd reports
>bin
>get inn.ps.Z     (or alternatively inn.ps.gz, inn.dvi.Z, inn.dvi.gz)
>quit

then uncompress (or unzip) file.    The documentation contains
directions how to obtain the program.

In a future version, I intend to include a special treatment of the
tensor product case.  Also I hope to modify the program soon such that
four factors in 3D-integrals can be computed.

--------------------------- Topic #9 -----------------------------------
From: Alex Drukarev <drukarev@hplaid.hpl.hp.com>
Subject: Meeting: Still-Image Compression 1996 (CFP)

		Still-Image Compression 1996

Conference Chairs: Robert L. Stevenson, Univ. of Notre Dame; Alexander I.
Drukarev, Hewlett-Packard Co.; Thomas R. Gardos, Intel Corp.

Recent advances in digital image capture and hard copy, coupled with
breakthroughs in the price/performance of digital hardware and firmware, have
resulted in an ever increasing need for storage and transmission of digital
images. Diverse applications include consumer imaging, color facsimile, image
archiving, remote sensing, medical imaging, education, and entertainment.
Image compression refers to the techniques that aim at reducing the
statistical redundancy and/or the irrelevancy of the digital image data to
save transmission time (channel bandwidth), storage space, or to improve data
transfer and access rate.
     This conference serves as a forum where advances in still-image
compression techniques and associated software and hardware implementations,
in addition to international standards, can be presented and discussed.

In particular, papers are solicited in the following areas:
*    lossless compression of still continuous-tone and bi-level images
*    lossy compression of still continuous-tone and bi-level images
*    image compression international standards (JPEG, JBIG, etc.)
*    image pre- and post-processing techniques for improving compression
     performance
*    model-based approaches for still-image compression (human visual system
     models and nontraditional approaches for very low data rates)
*    VLSI, hardware, and software implementation of image compression
     algorithms
*    system issues involving image compression (rate control, manipulation of
     compressed bit streams, integration with other media, etc.)


This conference is just one of nearly 30 conferences to be held at the EI'96
symposium. And EI'96 is just part of the larger Photonics West Symposium being
held 27 January - 2 February 1996, San Jose Convention Ctr., San Jose,
California USA.

TO OBTAIN ALL CALLS FOR PAPERS ELECTRONICALLY
The calls for papers for all conferences in the Photonics West symposium will
be available early June on
SPIE Web
(http://www.spie.org/web/meetings/calls/pw96_home.html), 
by anonymous FTP
(ftp://spie.org/meetings/calls/pw96*), 
or by e-mail file retrieval
send a message to info-optolink-request@spie.org with the following in the
message body:  
send [meetings.calls]pw96*}

For a printed call for papers or other information:
E-mail: pw96@spie.org
Fax: 360/647-1445 (*)
Phone: 360/676-3290 (*)

Electronic Imaging '96 DEADLINES
Paper Abstracts Due from Authors: 
     3 July 1995

Advance Programs due from Chairs: 
     31 July 1995

Manuscripts Due from Authors: 
     2 January 1996

GUIDELINES FOR SUBMITTING AN ABSTRACT

Send a 500 word abstract of your paper, by the appropriate deadline, in ONE of
the following ways:

>>mail (please mail 4 hard copies) to:
     IS&T/SPIE Electronic Imaging '96
     SPIE, P.O. Box 10, Bellingham, WA  98227-0010
     Shipping Address: 1000 20th Street, Bellingham, WA  98225
     Telephone: 360/676-3290 (*)
>>electronic mail in ASCII format to Internet abstracts@spie.org 
     (Please send one submission per email message.)
>>fax to SPIE at 360/647-1445 (*) 
     (Please send one submission per fax.)

Be sure each abstract includes the following:

1. CONFERENCE CHAIR and CONFERENCE TITLE (submit to ONLY ONE conference)
     to which the abstract is submitted

2. AUTHOR LISTING (List principal author first)
     for each author: full name [first(given) last(family] and affiliation,
     mailing address, phone/fax numbers, email

3. ABSTRACT/PAPER TITLE

4. ABSTRACT TEXT: 500 words typed on white paper

5. KEYWORDS: maximum of 5 keywords

6. BRIEF BIOGRAPHY of the principal author: 50-100 words

Please contact SPIE if you have any questions or require further information.

(*) Please note: 
SPIE's area code changed from 206 to 360 in February 1995. If you experience
any difficulty using the 360 area code, please use 206 and notify SPIE, your
local phone company, and the people in charge of the phone system from where
you placed your call. You may also call 1-800-441-5516 to report the
difficulty. Thank you for your patience while US West and other regional phone
companies fix this problem. You may also call SPIE at 800/483-9034, a
temporary number for use during this transition to the new area code.

--------------------------- Topic #10 -----------------------------------
From: "Goodin, Bill" <BGoodin@UNEX.UCLA.EDU>
Subject: Meeting: UCLA short course on "Wavelet Transform Applications"

On September 11-15, 1995, UCLA Extension will present the short course,
"Wavelet Transform Applications to Data, Signal, Image, and Video
Processing", on the UCLA campus in Los Angeles.

The instructors are Dr. Harold Szu, Research Physicist, Washington, DC,
and Prof. John Villasenor, UCLA.

The National Information Infrastructure (NII) has generated substantial 
interest
in the broad tele-informatics processing area in which a new mathematical
tool called the Wavelet Transform (WT) has been developed based on
human sensor wideband transient characteristics.  The wavelet transform
has proved to be a powerful and efficient mechanism whenever the noisy
data, signal image, and/or video processing functions are related to the
quality of human sensory perception.

This course builds the basics of both continuous and discrete WTs (CWTs
and DWTs) and demonstrates both techniques with various real world signal 
restoration and pattern recognition applications.  Case studies are then
examined, including the FBI's decade-long fingerprint compression program,
the five-year NIST/ATP program in digital video information infrastructure,
the ARPA tele-medicine program, among others.

The topics to be discussed include: Introduction to the Wavelet Transform
(WT); Applications-Driven Wavelet: Principles by Dimensionality, Design by 
Functionality; Continuous and Discrete Mathematics of WT and Comparisons,
How to Design Mother Wavelets; Neural Network Adaptive WT; Applications
of Super-Mother Wavelets; Advanced Medical Applications Using WT;
Nonlinear Dynamics Applications: Soliton WT Kernel; WT Implementation:
Hardware and Software Issues; Image Compression; 2D Wavelet Theory
and Practice; and Video Compression Applications.

For more information and a complete course description, please contact
Marcus Hennessy at:
(310) 825-1047
(310) 206-2815  fax
mhenness@unex.ucla.edu
 
--------------------------- Topic #11 -----------------------------------
From: Sekretariat MA 7-2 Downes <downes@math.tu-berlin.de>
Subject: Job: TU Berlin, Image compression using wavelets

We are offering a one year position for research and development on the project
     

                   Image compression using wavelets


starting Sept. 1, 1995.

This project which is financed by "Deutsche Telekom AG" is carried out by the 
Technical University Berlin in collaboration with the Technical College of 
Deutsche Telekom AG.

Applicants should hold a Ph.D. degree in Electric Engineering, Physics or 
Mathematics and should have practice in C++.  Working language is english or 
german.

Applications should be sent to

Hans L.Cycon
Matthias Holschneider
Ruedi Seiler
Dept. of Mathematics,
Technische Universitaet Berlin
e-mail  seiler@math.tu-berlin.de

Sekr. MA 7-2
Str. des 17. Juni 136
10623 Berlin

--------------------------- Topic #12 -----------------------------------
From: Marilyn Radcliff <radcliff@math.ohio-state.edu>
Subject: Contents: JAT Vol. 81, No. 2, May 95

Table of Contents: J. Approx. Theory, Volume 81, Number 2, May 1995

Grzegorz Lewicki
Best approximation in finite dimensional subspaces of $L(W,V)$
151--165
Kang Zhao
Simultaneous approximation from PSI spaces
166--184
S.P. Zhou
On rational lacunary approximation on the interval $[-1,1]$
185--194
Herman Bavinck and Roelof Koekoek
On a difference equation for generalizations of Charlier polynomials
195--206
F. M\'oricz
A quantitative version of the young test for the convergence of 
   conjugate series
207--216
Ying Guang Shi
Bounds and inequalities for arbitrary orthogonal polynomials on finite
   intervals
217--230
Jutta Faldey and Wolfgang Gawronski
On the limit distributions of the zeros of Jonqui\'ere polynomials and 
   generalized classical orthogonal polynomials
231--249
Oleg V. Davydov
A class of weak Chebyshev spaces and characterization of best 
   approximations
250--259
H. T. Koelink and R. F. Swarttouw
A $q$-analogue of Graf's addition formula for the Hahn-Exton $q$-Bessel
   function
260--273
J. M. Carnicer and J. M. Pe\~na
On transforming a Tchebycheff system into a strictly totally positive 
   system
274--295

Note
Ryszard Szwarc
Uniform subexponential growth of orthogonal polynomials
296--302

--------------------------- Topic #13 -----------------------------------
From: Marilyn Radcliff <radcliff@math.ohio-state.edu>
Subject: Contents: JAT Vol. 81, No. 1, April 95

Table of Contents: J. Approx. Theory, Volume 81, Number 1, April 1995

J. Bustamante and G. Lopez Lagomasino
Hermite-Pade approximation to a Nikishin type system of meromorphic
   functions
1--12
Vladimir A. Operstein
A characterization of smoothness in terms of approximation by algebraic 
   polynomials in $L_p$
13--22
H.-B. Knoop and Xin-Long Zhou
On convergence of Hermite-Fej\'er interpolation polynomials
23--37
Amos Ron
Approximation orders of and approximation maps from local principal
   shift-invariant spaces
38--65
Peter Borwein and Tam\'as Erd\'elyi
Dense Markov spaces and unbounded Bernstein inequalities
66--77
A. L. Brown
Metric projections in spaces of integrable functions
78--103
Knut Petras
Duality and lower bounds for relative projection constants
104--119
Timur Oikhberg
Absolute widths of some embeddings
120--126
L. Brutman and E. Passow
On the divergence of Lagrange interpolation to $|x|$
127--135
Notes
Gancho T. Tachev
A note on two moduli of smoothness
136--140
Margarita Nikoltjeva-Hedberg and Vladimir Operstein
A note on convex approximation in $L_p$
141--144
Ryszard Szwarc
A lower bound for orthogonal polynomials with an application to
   polynomial hypergroups
145--150


--------------------------- Topic #14 -----------------------------------
From: "Prof. E.B. Saff" <esaff@gauss.math.usf.edu>
Subject: Contents: Constructive Approximation, vol 11, no 2


		CONSTRUCTIVE APPROXIMATION

Volume 11	Number 2	1995

141 	G. C. Kyriazis
	Approximation from Shift-Invariant Spaces
165 	A. Pinkus and B. Wajnryb
	Multivariate Polynomials: A Spanning Question
181     N. M. Atakishiyev, M. Rahman, and S. K. Suslov
	On Classical Orthogonal Polynomials
227	H. Dette and W. J. Studden
	Some New Asymptotic Properties for the Zeros of Jacobi, 
	Laguerre, and Hermite Polynomials
239	M. D. Buhmann, N. Dyn, and D. Levin
	On Quasi-Interpolation by Radial Basis Functions with
	Scattered curves
255	G. Lopez Lagomasino and A. Martinez Finkelshtein
	Rate of Convergence of Two-Point Pade Approximants and
	Logarithmic Asymptotics of Laurent-Type Orthogonal
	Polynomials	

--------------------------- Topic #15 -----------------------------------
From: rms@in-berlin.in-berlin.de (Richard Stallman)
Subject: Question: Wavelet packega for JACAL ?

JACAL is a free symbolic algebra program, part of the GNU project.
I've heard that there are wavelet packages for such non-free programs
as Mathematica, and that people find them useful.  Would anyone be
interested in writing a free wavelet package to go with JACAL?

Writing this might be a substantial amount of work, but many people
would use it and know your name.

Copies of JACAL are available by ftp from ftp-swiss.ai.mit.edu in
(some of the files in) the directory /pub/scm.  You probably need the
packages SLIB and SCM as well, to run JACAL.

--------------------------- Topic #16 -----------------------------------
From: THOMAS MUDD <MUDDT@ex1.wes.army.mil>
Subject:  Question: Wavelet use in Seismic Engineering

-- Reply Requested When Convenient --

Question

We at the US Army Corps of Engineers Waterways Experiment Station
are interested in obtaining information, references or organizations that
are doing research on the use of wavelets in seismic or earthquake
engineering.

muddt@ex1.wes.army.mil

--------------------------- Topic #17 -----------------------------------
From: Petros Dafniotis <DAFNIOTIS@chera9.che.wisc.edu>
Subject: Question: Looking for help starting out in wavelets

Hello Wavelet wizzards,

  I have started a few weeks ago digging inside the beautiful but not -to
my opinion- extremely well organized wavelet theory literature. For my
problem it appears that the Chui spline wavelets would be the best
wavelets to use. After several days of search into Chui's books and
articles I still cannot understand how to calculate wavelets and
scaling functions of the Chui family.

  i.e. given m,n and the independent variable, x, I need to calculate
1) \psi_{m,n}(x)
2) the dual of (1)
3) the scaling function \phi(x)

  Can someone please point me to source code (Fortran/C) or even better
a book with formulas/algorithms that will teach me that? Please help
even with pointers or other places to look. (Yes, I did look into the
wavelet digest at South Carolina WWW, but I did not find something of
big help, I think).

  Thank you very much for your time and attention,
Petros Dafniotis
dafniotis@chera9.che.wisc.edu

--------------------------- Topic #18 -----------------------------------
From: jgaines@mcs.com (James Gaines)
Subject: Question: Applications of Wavelets Analysis to Time-Series

Greetings.

We are  looking for some layman references which spell out exactly how and
why wavelets could be applied to time-series analysis in order to better
understand the underlying system represented by the time-series itself.

We at GCC are interested in *applied* works involving wavelets and their
applications to Time-Series analysis.

The people at StatSci have a wavelets product (which is written in S) that
analysis a time-series in order to better understand localized periodicity
and/or sinusoidal frequency.  At least that is our understanding.  We are
attempting to capture the explanation of what analytical benefits wavelets
analysis of time-series [WATS] brings to the world of analysis.  This would
help us better understand what these products (like those being offered by
StatSci) actually offer and know the benefits and limitations of this
technology's application to time-series analysis.

Although we have seen several books, articles and references; none seem to
provide an example of the numerical methods/procedures/algorithms which
processes raw time-series data and subsequently yields characteristics of
that time-series and it's fundamental dynamics.

PLEASE HELP.

Thanks in advance,

James
jgaines@mcs.com

-------------------- End of Wavelet Digest -----------------------------