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Ali H. Sayed, Ph.D.
Electrical Engineering Department
UCLA
Abstract:
Distributed networks linking PCs, laptops, cell phones, sensors and actuators will form the backbone of future data, communication, and control networks. Applications will range from sensor networks to precision agriculture, environment monitoring, disaster relief management, smart spaces, target localization, as well as medical applications. In all these cases, the distribution of the nodes in the field yields spatial diversity, which should be exploited alongside the temporal dimension in order to enhance the robustness of the processing tasks and improve the probability of signal and event detection. Collaborative signal processing has been advocated as a way to achieve the efficient fusion of information. Regardless of the cooperative technique adopted, it is an accepted fact that distributed processing needs to be adaptive. This is because not only the environmental conditions vary with time and space, but the network topology may vary as well.
Most available distributed techniques tend to be iterative in nature as opposed to adaptive; they tend to operate on two separate time scales. An adaptive network should however react to spatial and temporal data in an instantaneous manner through local collaborations, and the information should flow through the network in real-time. In other words, an adaptive network should behave as an adaptive entity in its own right. The property of adaptation is fundamental in order to (1) endow the network with real-time learning abilities, (2) implement schemes that are robust to spatio-temporal variations, and (3) limit local processing and communications.
In designing such adaptive networks, there are at least two main issues to consider. One issue relates to the topology of the interacting nodes and the other issue relates to the processing and communications constraints imposed on the nodes. This talk describes recent developments in adaptive estimation over distributed networks. The resulting algorithms rely on local collaborations and exploit the space-time structure of the data. Each node is allowed to communicate with its neighbors in order to exploit the spatial dimension, while it also evolves locally to account for the time dimension. Algorithms of the least-mean-squares and least-squares types are described. Both incremental and diffusion strategies are considered.
Biography:
Ali H. Sayed is Professor and Chairman of Electrical Engineering
at UCLA where he directs the Adaptive
Systems Laboratory. He has published widely in the areas of
adaptive filtering, estimation theory, and signal processing for
communications with over 280 articles and 4 books. He is the author
of the textbook Fundamentals of Adaptive Filtering (Wiley, NY,
2003). He is a Fellow of IEEE and served as the Editor-in-Chief
of the IEEE Transactions on Signal Processing during 2003-2005.
He now serves as Editor-in-Chief of theEURASIP Journal on Advances
in Signal Processing. His research has received several recognitions
including the 1996 IEEE D. G. Fink Prize, a 2002 Best Paper Award
and a 2005 Young Author Best Paper award, both from the IEEE Signal
Processing Society, the 2003 Kuwait Prize, the 2005 Terman Award,
and two Best Student Paper Awards at international meetings (1999,2001).
He served as a 2005 Distinguished Lecturer of the IEEE Signal
Processing Society. He also served as a member of the Publications
and Award Boards of the IEEE Signal Processing Society and sits
on the Board of Governors of the same Society. He is serving as
General Chairman of ICASSP 2008.
Friday, May 4th at 11 a.m.
Bossone 303
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