Cevher, VolkanIndyk, PiotrCarin, LawrenceBaraniuk, Richard2010-11-222010-11-222010-11-22201010.1109/MSP.2010.938029https://infoscience.epfl.ch/handle/20.500.14299/58017WOS:000283453800012A 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.Sparse recoverysampling theoryprobabilistic sparsitystructured sparsitytext::journal::journal article::research article