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IEEE Signal Processing Society Santa Clara Valley Chapter





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Thursday, August 14, 2014

Bayesian Methods for Sparse Signal Recovery and Compressed Sensing

Speaker (Distinguished Lecturer):

   Dr. Bhaskar D. Rao

   Ericsson Endowed Chair and Professor

   Electrical and Computer Engineering department

   University of California, San Diego

   San Diego, California



  1 AMD Place, Sunnyvale, CA 94088 (Commons Bldg - map or Google Maps)


   6:30pm: Networking/Light Dinner

   7:00pm: Announcements

   7:05pm: Presentation

   8:15pm: Adjourn



  Free. Donation accepted for food.



Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem.



Bhaskar D. Rao (F) received the B.Tech. degree in electronics and electrical communication engineering from the Indian Institute of Technology, Kharagpur, India, in 1979 and the M.S. and Ph.D. degrees from the University of Southern California, Los Angeles, in 1981 and 1983, respectively. Since 1983, he has been with the University of California at San Diego, La Jolla, where he is currently a Professor with the Electrical and Computer Engineering. He is the holder of the Ericsson Endowed Chair in Wireless Access Networks and was the Director of the Center for Wireless Communications (2008-2011).

Prof. Rao was elected IEEE Fellow in 2000 "for his contributions to the statistical analysis of subspace algorithms for harmonic retrieval". His work has received several paper awards; Best Paper Award (2013) for the paper "Multicell Random Beamforming with CDF-based Scheduling: Exact Rate and Scaling Laws"; SPS Best Paper Award (2012) for the paper "An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem"; Stephen O. Rice Prize Paper Award in the Field of Communication Systems (2008) for the paper "Network Duality for Multiuser MIMO Beamforming Networks and Applications"; Best Paper Award (2000) for the paper "PDF Optimized Parametric Vector Quantization of Speech Line Spectral Frequencies". His students have received several student paper awards; Best Student Paper Award (2006) for D. Wipf for the paper "Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization," by D. P.Wipf, R. R. Ramirez, J. A. Palmer, S. Makeig, and B. D. Rao; Student Paper Award (2006) for Jun Zheng for the paper "Capacity Analysis of Multiple Antenna Systems with Mismatched Channel Quantization Schemes," by J. Zheng and B. D. Rao; Best Student Paper Award (2005) for J. McCall and D. Wipf for the paper "Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning" by J. McCall, D. Wipf. M. Trivedi and B. D. Rao; and Student Paper Award (2005) for Haichang Sui for the paper "RAKE Finger Placement for CDMA Downlink Equalization," by H. Sui, E. Masry and B. D. Rao.

Prof. Rao has been a Member, Statistical Signal and Array Processing Technical Committee; Member, Signal Processing Theory and Methods Technical Committee (1999-2004); Signal Processing for Communications and Networking Technical Committee (2005-2007); Member, Machine Learning for Signal Processing Technical Committee (2012-Present); Editorial Board Member, EURASIP Signal Processing Journal; and Technical Member, several IEEE conferences. Prof. Rao’s interests are in the areas of digital signal processing, estimation theory, and optimization theory, with applications to digital communications, speech signal processing, and biomedical signal processing.


Video of Presentation




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