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   -> Volume 2, Issue 17


Announcing a UCLA Extension Short Course.
 
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"Watanabe, Nonie" (NWatanab@unex.ucla.edu)
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PostPosted: Fri Nov 29, 2002 3:39 pm    
Subject: Announcing a UCLA Extension Short Course.
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Announcing a UCLA Extension Short Course.

Announcing a UCLA Extension Short Course.

Wavelet Transform: Techniques and Applications
March 7-11, 1994 at UCLA

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 S/N ratio 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 WTs 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 notes and relevant software are distributed on the first day of the
course. These notes are for participants only, and are not for sale.

COORDINATOR AND LECTURER
Harold Szu, PhD
Research Physicist, Washington, D.C. Dr. Szu's current research involves
wavelet transforms, character recognition, and constrained optimization
implementable 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 new optical data base machines. Dr. Szu is
also a technical representative to DARPA and consultant to ONR 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, PhD
Assistant Professor, Department of Electrical Engineering, School of
Engineering and Applied Science, UCLA. 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.

DAILY SCHEDULE
Monday (Szu)
Introduction to Wavelet Transform (WT)
- Formulation of small group projects using WT

Review of WT
- Historical: Haar 1910, Gabor 1942, Morlet 1985
- Definition of WT

Applications: Principles by Dimensionality, Functionality
- Signal processing: oil exploration, heart diagnosis
- Image processing: lossless compression, fingerprint
- Telecommunication: multi-medium wide-band ISDN

Discrete and Continuous Mathematics of WT
- Example: Haar WT and Daubechies WT
- Complexity Pyramid Theorem:
- Connection with continuous WT
- WT normalizations, causality conditions

Tuesday Morning (Villasenor)
Discrete Wavelet Transforms
- Background: motivation, multiresolution analysis, Laplacian pyramid coding
- Brief review of relevant digital signal processing concepts/notation
- Discrete wavelet transforms in one dimension: conceptual background, QMF
filter banks, regularity, examples

Tuesday Afternoon (Villasenor and Szu)
Computer Laboratory Demonstration
- Sound compression
- Adaptive speech wavelet code
- Image transforms using wavelets

Wednesday (Szu)
Adaptive Wavelet Transform
- Practical examples: ears, eyes
- Mathematics of optimization
- Applications: cocktail party effect, hyperacuity paradox

Examples: Superposition Mother Wavelets
- For phonemes
- For speaker ID
- For mine field

Nonlinear WT Applications: Soliton WT Kernel
- Practical examples: ocean waves, cauchy sea states
- Paradigms for solving nonlinear dynamics

Thursday (Villasenor)
Discrete Wavelet Transforms II
- Wavelet filter design: ensuring regularity, tradeoffs in filter length,
filter evaluation criteria
- 2D wavelet transforms and applications: extension of wavelets to two
dimensions, computational and practical considerations
- Image compression: techniques for coding of wavelet transforms, comparison
with JPEG, extension to video coding
- Future trends in image processing using wavelets

Friday (Szu)
Comparison
- Quadrature mirror filter vs. perfect inverse image filter
Regularity, Decimation, Sampling theorem

WT Implementation Issues
- Optical WT: Real-time image compression and transmission
- WT chips: WT butterfly

Advanced Applications in WT
- Virtual reality
- Environmental representation: surveillance planning
- Real-time techniques

Problem-Solving Methodology
- Four principles for creative research

Research Project Presentations
- Signal processing groups
- Image processing groups
- Implementation groups

Date: March 7-11 (Monday through Friday)
Time: 8 am-5 pm (subject to adjustment after the first class meeting),
plus optional evening sessions, times to be determined.
Location: Room G-33 West, UCLA Extension Building, 10995 Le Conte Avenue
(adjacent to the UCLA campus), Los Angeles, California
Course No. Engineering 867.121
Fee: $1495, includes course materials

To reserve a place in this course and/or request an application form, call
the UCLA Extension Short Course Program Office at (310) 825-3344; FAX (310)
206-2815.
All times are GMT + 1 Hour
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