NSF Org:	CCF Division of Computing and Communication Foundations
Recipient:	TEXAS A&M ENGINEERING EXPERIMENT STATION
Initial Amendment Date:	August 1, 2012
Latest Amendment Date:	August 1, 2012
Award Number:	1218398
Award Instrument:	Standard Grant
Program Manager:	John Cozzens CCF  Division of Computing and Communication Foundations CSE  Directorate for Computer and Information Science and Engineering
Start Date:	September 1, 2012
End Date:	July 31, 2015 (Estimated)
Total Intended Award Amount:	$252,674.00
Total Awarded Amount to Date:	$252,674.00
Funds Obligated to Date:	FY 2012 = $133,980.00
History of Investigator:	Henry Pfister (Principal Investigator) henry.pfister@duke.edu
Recipient Sponsored Research Office:	Texas A&M Engineering Experiment Station 3124 TAMU COLLEGE STATION TX  US  77843-3124 (979)862-6777
Sponsor Congressional District:	10
Primary Place of Performance:	Texas Engineering Experiment Station 1470 William D Fitch Pkwy. College Station TX  US  77845-4645
Primary Place of Performance Congressional District:	10
Unique Entity Identifier (UEI):	QD1MX6N5YTN4
Parent UEI:	QD1MX6N5YTN4
NSF Program(s):	Comm & Information Foundations
Primary Program Source:	01001213DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	7923, 7936
Program Element Code(s):	779700
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

Compressed sensing (CS) is a rapidly advancing area of signal processing and statistics that has the potential to radically change the way that analog signals are transformed into digital signals. The main idea is to acquire a sparse signal from a very small number of measurements using a specialized sampling and reconstruction process. Since one promising application of CS is medical imaging, improvements in CS systems are also expected to advance real-world healthcare applications. In this project, the investigators will study the fundamental connection between error-correcting codes (ECC) and CS and leverage recent advances in ECC to design improved CS measurement and reconstruction systems.

In particular, the connection between linear-programming (LP) decoding of binary linear codes and LP reconstruction will be used to develop a non-asymptotic theory for the design and analysis of CS algorithms and measurement matrices. The first part of the project will focus on novel relaxations of the CS reconstruction problem that allow non-convex regularization and iterative solution. The second part of the project will focus on applying the theory of pseudo-codewords, which was originally developed to understand iterative and LP decoding of binary linear codes, to achieve a non-asymptotic analysis of iterative reconstruction algorithms for CS. The third part of the project will focus on exploiting additional signal structure (i.e., beyond sparsity) that exists in high-contrast imaging applications such as angiograms.

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