[παλιό] 13/4/06 Talk by Ali H. Sayed

[Ethel]

ΔΙΑΛΕΞΗ Πέμπτη 13 ΑΠΡΙΛΊΟΥ
Ώρα 11 π.μ.
Αίθουσα Τηλεκπαίδευσης Κέντρου Δικτύων
Τμήμα Πληροφορικής και Τηλεπικοινωνιών ΕΚΠΑ

Energy Conservation in Adaptive Filtering

by Ali H. Sayed
Electrical Engineering Department UCLA

Adaptive filters are systems that respond to variations in their environment by adapting their internal structure in order to meet certain performance specifications. Such systems are widely used in communications, biomedical applications, signal processing, and control. The performance of an adaptive filter is evaluated in terms of its transient behavior and its steady-state behavior. The former provides information about how fast a filter learns, while the latter provides information about how well a filter learns. Such performance analyses are usually challenging since adaptive filters are, by design, time-variant, nonlinear, and stochastic systems. For this reason, it has been common in the literature to study different adaptive schemes separately due to the differences that exist in their update equations.

The purpose of this talk is to provide an overview of an energy conservation approach to the performance analysis of adaptive filters. The framework is based on studying the energy flow through successive iterations of an adaptive filter and on establishing a fundamental energy conservation relation; the relation bears resemblance with Snell's Law in optics and has far reaching consequences to the study of adaptive schemes. In this way, many new and old results can be pursued uniformly across different classes of algorithms.

In particular, the talk will highlight some recently discovered phenomena pertaining to the learning ability of adaptive filters. It will be seen that adaptive filters generally learn at a rate that is better than that predicted by least-mean-squares theory; that is, they are "smarter" than originally thought! It will also be seen that adaptive filters actually have two distinct rates of convergence; they learn at a slower rate initially and at a faster rate later; perhaps in a manner that mimics the human learning process.

The talk will also discuss adaptive distributed systems that are able to exploit both the temporal and spatial dimensions of the data collected at spatially distributed nodes in order to enhance the robustness of the processing tasks and improve the probability of signal and event detection. Adaptation is needed not only because the environmental conditions vary with time and space, but also because the network topology may vary.

Speaker Bio: Ali H. Sayed is Professor and Chairman of Electrical Engineering at UCLA, where he also directs the Adaptive Systems Laboratory (http://www.ee.ucla.edu/asl). He has published widely in the areas of adaptive filtering, estimation theory, and signal processing for communications with over 250 articles and 4 books, including the textbooks Fundamentals of Adaptive Filtering (Wiley, NY, 2003) and Linear Estimation (Prentice Hall, NJ, 2000). He is a Fellow of IEEE and has served as Editor-in-Chief of the IEEE Transactions on Signal Processing (2003-2005). He serves as Editor-in-Chief of the EURASIP Journal on Applied Signal Processing. His research has received several recognitions including the 1996 IEEE Donald G. Fink Prize, 2002 Best Paper Award from the IEEE Signal Processing Society, 2003 Kuwait Prize, 2005 Frederick E. Terman Award, 2005 Young Author Best Paper Award from the IEEE Signal Processing Society, and two Best Student Paper Awards at international meetings (1999,2001). He has served as a 2005 Distinguished Lecturer of the IEEE Signal Processing Society and as a member of the Publications (2003-2005) and Awards (2005) Boards of the same society. He is a member of the Signal Processing Theory and Methods (SPTM) and Signal Processing for Communications (SPCOM) technical committees of the IEEE Signal Processing Society. He is also serving as General Chairman of ICASSP 2008.