111. COMPRESSED SENSING AND SPARSE SIGNAL RECOVERY BY SPARSE BAYESIAN LEARNING: MODELS, ALGORITHMS, AND APPLICATIONS

Department: Electrical & Computer Engineering
Faculty Advisor(s): Bhaskar Rao

Primary Student
Name: Zhilin Zhang
Email: z4zhang@ucsd.edu
Phone: 858-356-7365
Grad Year: 2012

Abstract
Compressed sensing / sparse signal recovery is a hot field in signal processing. Numerous algorithms have been proposed and have shown promising successes in applications. Among these algorithms, sparse Bayesian learning (SBL) has outstanding performance. In this poster we will summarize our lab's recent work on SBL. We will present four new models that data-adaptively learn and exploit signals' temporal, spatial, spatiotemporal, and dynamic information. The derived algorithms from these models have shown the best, or at least top-tier, performance among existing compressed sensing algorithms in both computer simulations and practical applications (e.g. telemonitoring, biomarker selection in gene expression, source localization, earthquake detection, neuroimaging). Particularly, some of them have: (1) solved the challenge of fetal ECG (and other physiological signals) telemonitoring via wireless body-area networks with ultra-low power consumption, which was not solved before; (2) achieved higher EEG source localization accuracy than other famous algorithms in more complicated environments; (3) broke the record of predicting cognitive outcomes from neuroimaging measures in Alzheimer's disease in 2011; (4) obtained the best accuracy in earthquake detection in some common datasets; Some of these work have been published or submitted to: IEEE Trans. on Signal Processing, IEEE Journal of Selected Topics in Signal Processing, Proceedings of the IEEE, IEEE Trans. on Biomedical Engineering, NeuroImage, CVPR 2012, and ICASSP 2010, 2011, 2012. Also, a US patent is pending.

Related Links:

  1. http://dsp.ucsd.edu/~zhilin/

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