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dc.contributor.authorLavrenko, A.en_US
dc.contributor.authorRömer, F.en_US
dc.contributor.authorDel Galdo, G.en_US
dc.contributor.authorThoma, R.en_US
dc.contributor.authorArıkan, Orhanen_US
dc.coverage.spatialLisbon, Portugalen_US
dc.date.accessioned2016-02-08T11:54:57Z
dc.date.available2016-02-08T11:54:57Z
dc.date.issued2014en_US
dc.identifier.issn2219-5491en_US
dc.identifier.urihttp://hdl.handle.net/11693/27496
dc.descriptionDate of Conference: 1-5 September 2014en_US
dc.descriptionConference Name: 22nd European Signal Processing Conference, EUSIPCO 2014en_US
dc.description.abstractCompressed sensing allows for a significant reduction of the number of measurements when the signal of interest is of a sparse nature. Most computationally efficient algorithms for signal recovery rely on some knowledge of the sparsity level, i.e., the number of non-zero elements. However, the sparsity level is often not known a priori and can even vary with time. In this contribution we show that it is possible to estimate the sparsity level directly in the compressed domain, provided that multiple independent observations are available. In fact, one can use classical model order selection algorithms for this purpose. Nevertheless, due to the influence of the measurement process they may not perform satisfactorily in the compressed sensing setup. To overcome this drawback, we propose an approach which exploits the empirical distributions of the noise eigenvalues. We demonstrate its superior performance compared to state-of-the-art model order estimation algorithms numerically.en_US
dc.language.isoEnglishen_US
dc.source.titleProceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014en_US
dc.relation.isversionofhttps://doi.org/10.5281/zenodo.44108en_US
dc.subjectDetectionen_US
dc.subjectEigenvalues and eigenfunctionsen_US
dc.subjectError detectionen_US
dc.subjectMathematical modelsen_US
dc.subjectSignal processingen_US
dc.subjectSignal reconstructionen_US
dc.subjectCompressed domainen_US
dc.subjectComputationally efficienten_US
dc.subjectEmpirical distributionsen_US
dc.subjectMeasurement processen_US
dc.subjectModel-order selectionen_US
dc.subjectSignal of interestsen_US
dc.subjectSparsity levelen_US
dc.subjectCompressed sensingen_US
dc.titleAn empirical eigenvalue-threshold test for sparsity level estimation from compressed measurementsen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage1761en_US
dc.citation.epage1765en_US
dc.identifier.doi10.5281/zenodo.44108en_US
dc.publisherIEEEen_US
dc.contributor.bilkentauthorArıkan, Orhan


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