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Richard BaraniukVictor E. Cameron ProfessorDepartment of Electrical and Computer Engineering Rice University |
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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.
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Tuesday, September 20, 2005 3:00p.m. - Duncan Hall McMurtry Rice University
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* 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