A Bayesian analysis of compressive sensing data recovery in wireless sensor networks

2009 International Conference on Ultra Modern Telecommunications and Workshops

Volumn , Issue , 2009, Pages

  Masiero, Riccardo ^a   Quer, Giorgio ^a   Rossi, Michele ^a   Zorzi, Michele ^a  

^a UNIVERSITY OF PADOVA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS OF DATA; BAYESIAN ANALYSIS; BAYESIAN ESTIMATIONS; CORRELATION STRUCTURE; DATA COLLECTION; DATA GATHERING; DISTRIBUTED SIGNALS; EMPIRICAL EVIDENCE; MAXIMUM A POSTERIORI; NONSTATIONARY SIGNALS; NUMBER OF SAMPLES; ON-LINE ESTIMATION; REAL-WORLD; REAL-WORLD SCENARIO; SENSING DATA;

BAYESIAN NETWORKS; COMPUTER SYSTEM RECOVERY; DATA HANDLING; PRINCIPAL COMPONENT ANALYSIS; SENSOR NETWORKS; SIGNAL RECONSTRUCTION; WIRELESS TELECOMMUNICATION SYSTEMS;

WIRELESS SENSOR NETWORKS;

EID: 74549174878     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICUMT.2009.5345599     Document Type: Conference Paper

References (15)

1. D. Donoho, "Compressed sensing," IEEE Trans. on Information Theory, vol. 52, no. 4, pp. 4036-4048, Apr. 2006.
2. E. Candès and T. Tao, "Near optimal signal recovery from random projections: Universal encoding strategies?" IEEE Trans. on Information Theory, vol. 52, no. 12, pp. 5406-5425, Dec. 2006.
3. E. Candès, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. on Information Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006.
4. J. Haupt, W. Bajwa, M. Rabbat, and R. Nowak, "Compressive Sensing for Networked Data: a Different Approach to Decentralized Compression," IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 92-101, Mar. 2008.
5. R. Masiero, G. Quer, D. Munaretto, M. Rossi, J. Widmer, and M. Zorzi, "Data Acquisition through joint Compressive Sensing and Principal Component Analysis," in IEEE Globecom 2009, Honolulu, Hawaii, USA, Nov.-Dec. 2009.
6. C. R. Rao, "The Use and Interpretation of Principal Component Analysis in Applied Research," Sankhya: The Indian Journal of Statistics, vol. 26, pp. 329-358, 1964.
7. S. Gull, "The Use and Interpretation of Principal Component Analysis in Applied Research," Maximum Entropy and Bayesian Methods in Science and Engineering, vol. 1, pp. 53-74, 1988.
8. J. Skilling, Maximum Entropy and Bayesian Methods. Kluwer academic, 1989.
9. S. Ji, Y. Xue, and L. Carin, "Bayesian Compressive Sensing," IEEE Trans. on Signal Processing, vol. 56, no. 6, pp. 2346-2356, Jun. 2008.
10. J. Haupt and R. Nowak, "Signal reconstruction from noisy random projections," IEEE Trans. on Information Theory, vol. 52, no. 9, pp. 4036-4048, Sep. 2006.
11. G. Quer, R. Masiero, D. Munaretto, M. Rossi, J. Widmer, and M. Zorzi, "On the Interplay Between Routing and Signal Representation for Compressive Sensing in Wireless Sensor Networks," in Information Theory and Applications Workshop (ITA 2009), San Diego, CA, US, Feb. 2009.
12. H. Mohimani, M. Babaie-Zadeh, and C. Jutten, "A fast approach for overcomplete sparse decomposition based on smoothed L0 norm," IEEE Trans. on Signal Processing, 2009, Accepted for publication.
13. D. J. MacKay, "Bayesian Interpolation," Neural Computation Journal, vol. 4, no. 3, pp. 415-447, May 1992.
14. G. Schwarz, "Estimating the Dimension of a Model," The Annals of Statistics, vol. 6, no. 2, pp. 461-464, 1978.
15. E. Candès, M. B. Wakin, and S. Boyd, "Enhancing Sparsity by Reweighted ℓ1 Minimization," Journal of Fourier Analysis and Applications, vol. 14, pp. 877-905, Dec. 2008.