Compressive Sensing for Inverse Scattering

dc.creatorMarengo, Edwin A.
dc.creatorHernández, R. D.
dc.creatorCitron, Y. R.
dc.creatorGruber, F. K.
dc.creatorZambrano, M.
dc.creatorLev-Ari, H.
dc.date.accessioned2017-08-01T20:56:15Z
dc.date.accessioned2017-08-01T20:56:15Z
dc.date.available2017-08-01T20:56:15Z
dc.date.available2017-08-01T20:56:15Z
dc.date.created2017-08-01T20:56:15Z
dc.date.created2017-08-01T20:56:15Z
dc.date.issued2008-06-30
dc.date.issued2008-06-30
dc.identifierhttp://ridda2.utp.ac.pa/handle/123456789/2413
dc.identifierhttp://ridda2.utp.ac.pa/handle/123456789/2413
dc.description.abstractCompressive sensing is a new field in signal processing and applied mathematics. It allows one to simultaneously sample and compress signals which are known to have a sparse representation in a known basis or dictionary along with the subsequent recovery by linear programming (requiring polynomial (P) time) of the original signals with low or no error [1–3]. Compressive measurements or samples are non-adaptive, possibly random linear projections
dc.languageeng
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectinverse scattering
dc.subjectsignal processing
dc.subjectrandom linear projection
dc.subjectapplied mathematics
dc.subjectcompressive measurement
dc.subjectsparse representation
dc.subjectnew field
dc.subjectknown basis
dc.subjectcompressive sensing
dc.subjectoriginal signal
dc.subjectlinear programming
dc.subjectsubsequent recovery
dc.subjectcompress signal 
dc.subjectinverse scattering
dc.subjectsignal processing
dc.subjectrandom linear projection
dc.subjectapplied mathematics
dc.subjectcompressive measurement
dc.subjectsparse representation
dc.subjectnew field
dc.subjectknown basis
dc.subjectcompressive sensing
dc.subjectoriginal signal
dc.subjectlinear programming
dc.subjectsubsequent recovery
dc.subjectcompress signal 
dc.titleCompressive Sensing for Inverse Scattering
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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