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Center for Nonlinear Studies |
This talk will overview some of the sensor network research projects underway in the Rice University Digital Signal Processing (DSP) group. In a battery-powered sensor network, energy and communication bandwidth are both limited. Moreover, processing a sensor measurement locally often requires orders of magnitude less energy than communicating it to a distant node, yielding an interesting communication/computation tradeoff: whenever possible, the network should reduce the need for global communication at the expense of increased local processing and communication. In the COMPASS Project, we are developing a multiscale sensor network architecture whose communications hierarchy is aligned with the information flow of its computations. We have also introduced new theory that extends the compressive sensing framework to sensor network settings. Distributed compressive sensing (DCS) enables new distributed coding algorithms for multi-signal ensembles that exploit both intra- and inter-signal correlation structures. The DCS theory rests on a new concept that we term the joint sparsity of a signal ensemble and provides compression that is universal to the signal structure while requiring no communication between the sensors.
Host: DDMA Speaker Series