Prof. G. B. Giannakis, University of Minnesota
Date: November 14, 2008 (Friday)
Time: 12:30pm-1:30pm
Location: ECEC 202, NJIT

About the Presenter:

G. B.  Giannakis (Fellow'97) received his Diploma in Electrical Engineering from the National Technical University of Athens, Greece, 1981. From 1982 to 1986 he was with the Univ. of Southern California (USC), where he received his MSc. in Electrical Engineering, 1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engr., 1986. Since 1999 he has been a Professor with the University of Minnesota, where he now holds an ADC Chair in Wireless Telecommunications in the ECE Department and serves as director of the Digital Technology Center.  His general interests span the areas of communications, networking and statistical signal processing - subjects on which he has published more than 275 journal papers, 450 conference papers, two edited books and two research monographs. Current research focuses on complex-field and network coding, cooperative wireless communications, cognitive radios, cross-layer designs, mobile ad hoc networks and wireless sensor networks. He is the (co-) recipient of six paper awards from the IEEE Signal Processing (SP) and Communications Societies including the G. Marconi Prize Paper Award in Wireless Communications. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), a Young Faculty Teaching Award and the G. W. Taylor Award for Distinguished Research from the University of Minnesota. He is a Fellow of EURASIP, has served the IEEE in a number of posts, and is currently a Distinguished Lecturer for the IEEE-SP Society.

About the Talk:

Envisioned applications of wireless sensor networks (WSNs) include surveillance, monitoring and tracking tasks. These motivate well decentralized estimation and smoothing of deterministic and (non)stationary random signals using (possibly correlated) observations collected across distributed sensors. In this talk we present state-of-the-art algorithms for consensus-based distributed estimation using ad hoc WSNs where sensors communicate over single-hop noisy links. The novel framework reformulates basic estimation criteria such as least-squares, maximum-likelihood, maximum a posteriori, and linear mean-square error, as decomposable, constrained, convex optimization problems that are amenable to distributed solutions. The resultant distributed estimators are provably convergent to their centralized counterparts and robust to communication noise. Besides stationary, the framework encompasses adaptive filtering and smoothing of non-stationary signals through distributed LMS and Kalman filtering.

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Note: All MS thesis and PhD dissertation (proposal) defense are counted towards ECE791.