UCLA Short Course on Wavelets announcement (September 12-16 1994)

[Harold Szu]

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 connectionist technique, the Wavelet
Transform (WT), that is replacing the windowed FT in a neural network
to do the applications mentioned above by a WAVENET.  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 neural network computer.  He has edited
two special issues on Wavelets, Sept 1992 & July 1994 of Optical
Engineering. He is the Chair of SPIE Orlando Wavelet Applications
Conference every year since 1992. He is also involved with the design
of a next-generation computer based on the confluence of neural
networks and optical data base machines.  Dr. Szu is also a technical
representative to ARPA and consultant to the Office of Naval Research ,
and has been engaged in plasma physics, optical engineering, electronic
warfare research for the past 16 years.  He holds six patents, has
published about 200 technical papers, plus edided several textbooks.
Dr. Szu is the editor-in-chief for the INNS Press, and currently serves
as the Immediate Past 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: September 12-16 (Monday through Friday)
Time: 8am - 5pm (subject to adjustment after the first class meeting).
Location: Room G-33 West, UCLA Extension Building, 10995 Le Conte
Avenue (adjacent to the UCLA campus), Los Angeles, California.
Reg# E0153M  Course No. Engineering 867.121
3.0 CEU (30 hours of instruction)
Fee: $1495, includes course materials