UCLA Short Course on Wavelets announcement (Aug. 9-11)

[Wesley Elsberry]

ANNOUNCEMENT

UCLA Extension Short Course

The Wavelet Transform: Techniques and Applications

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
signal to noise ratio (SNR) 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 WT's 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 note and relevant software are distributed on the first day of
the course.  The notes are for participants only, and are not for
sale.

Coordinator and Lecturer

Harold Szu, Ph.D.

Research physicist, Washington, D.C.  Dr. Szu's current research
involves wavelet transforms, character recognition, and constrained
optimization implementation 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 optical data base machines.  Dr. Szu is also a technical
representative to DARPA and consultant to the Office of Naval Research
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, Ph.D.

Assistant Professor, Department of Electrical Engineering, University
of California, Los Angeles.  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.




For more information, call the Short Course Program Office at (310)
825-3344; Facsimile (213) 206-2815.

Date: August 9-11 (Monday through Wednesday)
Time: 8am - 5pm (subject to adjustment after the first class meeting), plus
optional evening sessions, times to be determined.
Location: Room 211, UCLA Extension Building, 10995 Le Conte Avenue (adjacent
to the UCLA campus), Los Angeles, California.
Reg# E8086W  Course No. Engineering 867.118
1.8 CEU (18 hours of instruction)
Fee: $1195, includes course materials

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Wesley R. Elsberry, elsberry at beta.tricity.wsu.edu
Sysop, Central Neural System BBS, FidoNet 1:3407/2, 509-627-6267