Adaptive Networks and Bio-Inspired Cognition
Speaker: Ali H. Sayed, Electrical Engineering at UCLA
Series: Topical Seminars
Location:
Engineering Quadrangle B205
Date/Time: Thursday, October 14, 2010, 4:30 p.m.
- 5:30 p.m.
ELE 518
Abstract:
The emerging interest in cognitive networks, smart grids, and self-organizing networks is motivating heightened research on collaborative processing strategies that enable networks to learn and respond to information in real-time. Adaptive networks are well-suited to perform decentralized information processing and decentralized inference tasks. They are also well-suited to model self-organizing behavior such as animal flocking and swarming. These networks avoid centralized processing and perform in-network inference and control decisions without relying on omnipotent agents (or fusion centers). This is because solutions that rely on information fusion are not scalable, are hard to adapt to changing network conditions, and create single points of vulnerability and information bottlenecks.
Adaptive networks consist of spatially distributed agents that are linked together through a connection topology. The topology may vary with time and the nodes may also move. The agents cooperate with each other through local interactions and by means of in-network processing. The diffusion of information across the network results in various forms of self-organizing behavior and collective intelligence. A key property of adaptive networks is that all agents behave in an isotropic manner and are assumed to have similar abilities. This kind of behavior is common in many socio-economic and life and biological networks where no single agent is in command.
This talk describes recent developments in distributed processing over adaptive networks and illustrates the techniques by studying self-organization in biological networks such as bird formations, fish schooling, bee swarming, and bacteria motility.
Biography:
Ali H. Sayed is Professor of Electrical Engineering at UCLA where he directs the Adaptive Systems Laboratory (www.ee.ucla.edu/asl). He has published widely in the areas of adaptation and learning with over 350 articles and 5 books. He is the author of the textbooks Fundamentals of Adaptive Filtering (Wiley, NJ, 2003), and Adaptive Filters (Wiley, NJ, 2008). He is a Fellow of IEEE and has served as Editor-in-Chief of the IEEE Transactions on Signal Processing (2003-2005) and the EURASIP J. Advances in Signal Processing (2006-2007). His research has received several recognitions including the 1996 IEEE D. G. Fink Prize, a 2002 Best Paper Award from the IEEE Signal Processing Society, the 2003 Kuwait Prize, the 2005 Terman Award, and a 2005 Young Author Best Paper Award from the IEEE Signal Processing Society. He served as a 2005 Distinguished Lecturer of the IEEE Signal Processing Society. He has been a member of the Publications (2003-2005), Awards (2005), and Conference (2007-present) Boards of the IEEE Signal Processing Society. He served as General Chairman of ICASSP 2008, as member of the Board of Governors (2007-2008) of the IEEE Signal Processing Society, and is now serving as Vice-President (Publications) of the same society (2009-present)
