000155217 001__ 155217
000155217 005__ 20190316234939.0
000155217 0247_ $$2doi$$a10.1109/MSP.2010.938029
000155217 022__ $$a1053-5888
000155217 02470 $$2ISI$$a000283453800012
000155217 037__ $$aARTICLE
000155217 245__ $$aSparse Signal Acquisition and Recovery with Graphical Models
000155217 269__ $$a2010
000155217 260__ $$bInstitute of Electrical and Electronics Engineers$$c2010
000155217 336__ $$aJournal Articles
000155217 520__ $$aA great deal of theoretic and algorithmic research has revolved around sparsity view of signals over the last decade to characterize new, sub-Nyquist sampling limits as well as tractable algorithms for signal recovery from dimensionality reduced measurements. Despite the promising advances made, real-life applications require more realistic signal models that can capture the underlying, application-dependent order of sparse coefficients, better sampling matrices with information preserving properties that can be implemented in practical systems, and ever faster algorithms with provable recovery guarantees for real-time operation.
000155217 6531_ $$aSparse recovery
000155217 6531_ $$asampling theory
000155217 6531_ $$aprobabilistic sparsity
000155217 6531_ $$astructured sparsity
000155217 700__ $$0243957$$g199128$$aCevher, Volkan
000155217 700__ $$aIndyk, Piotr
000155217 700__ $$aCarin, Lawrence
000155217 700__ $$aBaraniuk, Richard
000155217 773__ $$j26$$tIEEE Signal Processing Magazine$$k6$$q92-103
000155217 8564_ $$uhttps://infoscience.epfl.ch/record/155217/files/Sparse%20Signal%20Acquisition%20and%20Recovery%20using%20Graphical%20Models.pdf$$zPostprint$$s1764197$$yPostprint
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000155217 917Z8 $$x199128
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000155217 937__ $$aEPFL-ARTICLE-155217
000155217 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000155217 980__ $$aARTICLE