Many current applications relevant to security and defense involve some form of process detection. This talk will formulate several problems related to the kinds of detection problems sensor networks can solve in cyber, physical, and social environments, with specific attention being paid to tracking behaviors over space and time. We show that there are general Nyquist-like sampling results that are related to a phase transition from polynomial to exponential growth in the number of possible associations of sensor observations. Concrete examples and outlines of proofs will be given. Papers describing details of this body of work can be found at www.pqsnet.net.
Reception to follow in 301 Coordinated Science Laboratory.
George Cybenko is the Dorothy and Walter Gramm Professor of Engineering at Dartmouth College. Prior to joining Dartmouth, he was Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. Cybenko's current research interests are distributed information and software systems, especially related to security, infrastructure protection, and autonomic computing. He is the founding Editor-in-Chief of IEEE Security and Privacy magazine and investigator on projects funded by DHS, DARPA, NSF, and ARDA. He received his B.A. in mathematics from the University of Toronto and his Ph.D. in applied mathematics from Princeton. He is a Fellow of the IEEE, a member of the IEEE Computer Society's Board of Governors, and a Director of the Computing Research Association.