Systems Seminar
Distributed Compressed Sensing
Dr. Dror Baron
Rice University
ECE Department
Abstract
Sensors, signal processing hardware, and algorithms are under increasing
pressure to accommodate ever larger data sets; ever faster sampling and
processing rates; ever lower power consumption; and radically new sensing
modalities. Fortunately, over the past few decades, there has been an
enormous increase in computational power and data storage capacity, 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.
As an example of CSP, I will overview our recent work in "Compressed
Sensing", an emerging field based on the revelation that a small
collection of linear projections of a sparse signal contains enough
information for reconstruction. The implications of compressed sensing are
promising for many applications and enable the design of new kinds of
cameras and analog-to-digital converters.
I will then focus on our new theory for distributed compressed sensing
(DCS) that enables new distributed coding algorithms that exploit both
intra- and inter-signal correlation structures in multi-signal ensembles.
The DCS theory rests on a new concept that we term the joint sparsity of a
signal ensemble. We study in detail three simple models for jointly
sparse signals, propose algorithms for joint recovery of multiple signals
from incoherent projections, and characterize theoretically and
empirically the number of measurements per sensor required for accurate
reconstruction. We establish a parallel with the Slepian-Wolf theorem from
information theory and establish upper and lower bounds on the measurement
rates required for encoding jointly sparse signals. In two of our three
models, the results are asymptotically best-possible, meaning that both
the upper and lower bounds match the performance of our practical
algorithms. Moreover, simulations indicate that the asymptotics take
effect with just a moderate number of signals. In some sense DCS is a
framework for distributed compression of sources with memory, which has
remained a challenging problem for some time. DCS is immediately
applicable to a range of problems in sensor networks and arrays.
More
For more information, a compressed sensing resource page is available on
the web at http://www.dsp.ece.rice.edu/cs/.
Biography
Dror Baron received the B.Sc. (summa cum laude) and M.Sc. degrees from the
Technion - Israel Institute of Technology, in 1997 and 1999, and the Ph.D.
degree from the University of Illinois at Urbana-Champaign in 2003, all in
electrical engineering. From 1997 to 1999 he worked at Witcom Ltd. in
modem design. From 1999 to 2003 he was a research assistant at the
University of Illinois at Urbana-Champaign, where he was also a visiting
assistant professor in 2003. Since 2003 he has been a postdoctoral
research associate in the Department of Electrical and Computer
Engineering at Rice University. His research interests include distributed
systems, efficient algorithms. information theory, and signal processing.
Time and Place: Mon., Dec. 12, at 2:30 pm in 3609 Engr. Hall.
*** NOTE SPECIAL DAY, TIME, & ROOM ***
SYSTEMS SEMINAR WEB PAGE:
http://www.cae.wisc.edu/~gubner/seminar/