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   -> Volume 3, Issue 11


Meeting: UCLA Short Course on Wavelets
 
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hszu%ulysses@relay.nswc.navy.mil (Harold Szu)
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PostPosted: Mon Dec 02, 2002 1:12 pm    
Subject: Meeting: UCLA Short Course on Wavelets
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Meeting: UCLA Short Course on Wavelets

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

ANNOUNCEMENT

UCLA Extension Short Course

The Wavelet Transform: Techniques and Applications

Overview

For decades, the Fourier Transform (FT) has been used or abused in a
wide variety of application areas involving localized & aperiodic
signals, which are gradually taken over by the appropriate & much more
efficient Wavelet Transforms (WT), a generalized FT for any Wideband
Transients multiple resolution analyses. On the one hand, real-world
signals are usually local transients which match better with localized
WT bases. On the other hand, noise is global that matches with the
global FT. Therefore, a judicious choice of WT can achieve easily a
3dB increase in the signal to noise ratio (SNR) compared with FT. The
class will provide the methods of constructing your own WT for your own
specifications. The applications includes signal processing and pattern
recognition, multimedia compression of wideband ISDN for
telecommunications; lossless transform for fingerprint storage,
identification, and retrieval; 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. Current efforts in applications & implementations
of the Fast WT algorithm using HDL discrete WT chip are reviewed.

This course describes a balanced & computational approach to the theory
of continuous versus discrete subband-coding WT, applications versus
implementations. The WT replaces the windowed FT by a zero-area &
bandpass kernel, called 'mother' wavelet, h(t), of which the complete
bases h((t-b)/a) are generated by the scale-shift affine transform t-->
t'= (t- b)/a using dyadic a,b parameters. Thereby, the WT is enabling
improved representation and analysis of wideband transient(having
pedagogically the same acronym WT as its tool), and noisy real-world
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 to determine optimum mother wavelets, 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 new paradigm for
solving nonlinear dynamics for information processing; e.g., using an
exact nonlinear solution of the first order Born approximation as the
mother wavelet to expand the full nonlinear dynamics. For example,the
environmental surveillance over an ocean, the sea wave is analyzed by
the soliton envelope wavelets, and the statistical sea state by the
dynamics the Cauchy sea state using wavelet analysis.

Finally, implementation techniques in optics and digital electronics
are presented, including O(N) digital filter bank (QMF) 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 adaptive wavelet transforms, pattern recognition, and
constrained optimization & wavelet chip implementation. He has edited
two special issues on Wavelets, Sept 1992 & July 1994 of Optical
Engineering. He is the Co-Chair of SPIE Orlando Wavelet Applications
Conference every year since 1992. The on-site publication of the
Proceedings Wavelet Applications, SPIE Vol 2242, April 4-8, 1994
Orlando has 950 pages & 11 Sections. He is a Fellow of SPIE. 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.

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
All times are GMT + 1 Hour
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