Richard Baraniuk

Victor E. Cameron Professor
Department of Electrical and Computer Engineering
Rice University

Compressed Sensing: A New Framework for Computational Signal Processing

Over the past several years, sensors and signal processing algorithms and hardware have been under increasing pressure to accommodate: ever larger and higher-dimensional data sets; ever faster capture, sampling, and processing rates; ever lower power consumption; communication over ever more difficult channels; and radically new sensing modalities. Fortunately, over the same time period, there has been an enormous increase in computational power and data storage thanks to Moore's Law, which provides a new angle to tackle these challenges. We could be on the verge of moving from a "digital signal processing" (DSP) paradigm, where analog signals are sampled periodically to create their digital counterparts for processing, to a "computational signal processing" (CSP) paradigm where analog signals are converted directly to any of a number of intermediate, "condensed" representations for processing using optimization techniques. At the foundation of CSP lie new uncertainty principles that generalize Heisenberg's between the time and frequency domains and the concept of sparsity. As an example of CSP, I will overview "Compressed Sensing," an emerging field based on the revelation that a small number of linear, even random, projections of a sparse signal contain enough information for reconstruction. The implications of CS are promising for many applications, including analog-to-digital converters, imaging devices and cameras, and sensor networks and arrays.
 
Tuesday, September 20, 2005
3:00p.m. - Duncan Hall McMurtry
Rice University


* Biography:

Richard G. Baraniuk received a B.Sc in 1987 from the University of Manitoba, the M.Sc in 1988 from the Unviersity of Wisconsin-Madison, and the PhD degree in 1992 from the University of Illinois at Urbana-Champaign. In 1999, he launched Connexions®, a community-based educational project that provides a critical mass of educational content free to anyone in the world. A recipient of the George R. Brown Award for Superior Teaching at Rice, Richard's research interest are in signal- and image-processing theory and applications, wavelets, statistical modeling, and networking; community based, open-source/open-content educational materials development.


ECE Affiliates Meeting - Afternoon Session



Last modified: September 26, 2005