Compressed sensing over finite fields

IEEE International Symposium on Information Theory - Proceedings

Volumn , Issue , 2009, Pages 669-673

  Draper, Stark C ^a   Malekpour, Sheida ^a  

^a University of Wisconsin Madison   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPRESSED SENSING; ERROR EXPONENT; FINITE ALPHABET; FINITE FIELDS; LARGE DEVIATIONS; LINEAR CODING; LOSSLESS SOURCE CODING; MEASUREMENT NOISE;

ACOUSTIC NOISE; CHANNEL CODING; INFORMATION THEORY; SPURIOUS SIGNAL NOISE;

SIGNAL RECONSTRUCTION;

EID: 70449505130     PISSN: 21578102     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISIT.2009.5205666     Document Type: Conference Paper

References (14)

1. D. Baron, M. F. Duarte, S. Sarvotham, M. B. Wakin, and R. Baraniuk. An information-theoretic approach to distributed compressed sensing. In Proc. Allerton Conf., September 2005.
2. E. J. Candes and T. Tao. Decoding by linear programming. IEEE Trans. Inform. Theory, 51(12):4203-4215, December 2005.
3. J. M. Cover and 1. A. Thomas. Elements of Information Theory. John Wiley and Sons, 1991.
4. I. Csiszár, Linear codes for sources and source networks: Error exponents, universal coding. IEEE Trans. Inform. Theory, 28:585-592, July 1982.
5. I. Csiszár and J. Kömer. Information Theory, Coding Theorems for Discrete Memoryless Systems. Akadémiai Kiadó, 1981.
6. D. Donoho. Compressed sensing. IEEE Trans. Inform. Theory, 52(4):1289-1306, April 2006.
7. D. L. Donoho and 1. M. Tanner. Counting faces of randomlyprojected polytopes when the projection radically lowers dimension. J. Amer. Math. Soc., July 2008.
8. R. G. Gallager. Information Theory and Reliable Communication. John Wiley and Sons, 1968.
9. V. K. Goyal. Transform coding with integer-to-integer transforms. IEEE Trans. Inform. Theory, 46(2):465-473, March 2000.
10. V. K. Goyal, A. K. Fletcher, and S. Ragan. Compressive sampling and lossy compression. IEEE Signal Proc. Mag., 25(2):48-56, March 2008.
11. S. Sarvotham, D. Baron, and R. G. Baraniuk. Measurement vs. bits: Ccompressed sensing meets information theory. In Proc. Allerton Conf.
12. M. J. Wainwright. Information-theoretic limits on sparsity recovery in the high-dimensional and noisy setting. Technical Report 725, DC Berkeley Department of Statistics, 2007.
13. W. Xu and B. Hassibi. Efficient compressive sensing with deterministic guarantees using expander graphs. In Proc. Inform. Theory Workshop, Lake Tahoe, CA, September 2007.
14. F. Zhang and H. D. Pfister. Compressed sensing and linear codes over real numbers. In UCSD Workshop Inform. Theory Apps., February 2008.