Compressive Sensing for Inverse Scattering
dc.contributor.author | Marengo, Edwin A. | |
dc.contributor.author | Hernández, R. D. | |
dc.contributor.author | Citron, Y. R. | |
dc.contributor.author | Gruber, F. K. | |
dc.contributor.author | Zambrano, M. | |
dc.contributor.author | Lev-Ari, H. | |
dc.date.accessioned | 2017-08-01T20:56:15Z | |
dc.date.accessioned | 2017-08-01T20:56:15Z | |
dc.date.available | 2017-08-01T20:56:15Z | |
dc.date.available | 2017-08-01T20:56:15Z | |
dc.date.issued | 2008-06-30 | |
dc.date.issued | 2008-06-30 | |
dc.identifier.uri | http://ridda2.utp.ac.pa/handle/123456789/2413 | |
dc.identifier.uri | http://ridda2.utp.ac.pa/handle/123456789/2413 | |
dc.description | Compressive 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 | en_US |
dc.description.abstract | Compressive 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 | en_US |
dc.language | eng | |
dc.language.iso | eng | en_US |
dc.rights | https://creativecommons.org/licenses/by-nc-sa/4.0/ | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.subject | inverse scattering | en_US |
dc.subject | signal processing | en_US |
dc.subject | random linear projection | en_US |
dc.subject | applied mathematics | en_US |
dc.subject | compressive measurement | en_US |
dc.subject | sparse representation | en_US |
dc.subject | new field | en_US |
dc.subject | known basis | en_US |
dc.subject | compressive sensing | en_US |
dc.subject | original signal | en_US |
dc.subject | linear programming | en_US |
dc.subject | subsequent recovery | en_US |
dc.subject | compress signal | en_US |
dc.subject | inverse scattering | |
dc.subject | signal processing | |
dc.subject | random linear projection | |
dc.subject | applied mathematics | |
dc.subject | compressive measurement | |
dc.subject | sparse representation | |
dc.subject | new field | |
dc.subject | known basis | |
dc.subject | compressive sensing | |
dc.subject | original signal | |
dc.subject | linear programming | |
dc.subject | subsequent recovery | |
dc.subject | compress signal | |
dc.title | Compressive Sensing for Inverse Scattering | en_US |
dc.type | info:eu-repo/semantics/article | |
dc.type | info:eu-repo/semantics/publishedVersion |