Compressive Sensing for Inverse Scattering
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 (...
| Autores Principales: | Marengo, Edwin A., Hernández, R. D., Citron, Y. R., Gruber, F. K., Zambrano, M., Lev-Ari, H. |
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| Formato: | Artículo |
| Idioma: | Inglés Inglés |
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2017
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| Acceso en línea: |
http://ridda2.utp.ac.pa/handle/123456789/2413 http://ridda2.utp.ac.pa/handle/123456789/2413 |
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RepoUTP2413 |
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RepoUTP24132021-07-06T15:34:52Z Compressive Sensing for Inverse Scattering Marengo, Edwin A. Hernández, R. D. Citron, Y. R. Gruber, F. K. Zambrano, M. Lev-Ari, H. inverse scattering signal processing random linear projection applied mathematics compressive measurement sparse representation new field known basis compressive sensing original signal linear programming subsequent recovery compress signal inverse scattering signal processing random linear projection applied mathematics compressive measurement sparse representation new field known basis compressive sensing original signal linear programming subsequent recovery compress signal 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 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 2017-08-01T20:56:15Z 2017-08-01T20:56:15Z 2017-08-01T20:56:15Z 2017-08-01T20:56:15Z 2008-06-30 2008-06-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://ridda2.utp.ac.pa/handle/123456789/2413 http://ridda2.utp.ac.pa/handle/123456789/2413 eng eng https://creativecommons.org/licenses/by-nc-sa/4.0/ info:eu-repo/semantics/openAccess application/pdf |
| institution |
Universidad Tecnológica de Panamá |
| collection |
Repositorio UTP – Ridda2 |
| language |
Inglés Inglés |
| topic |
inverse scattering signal processing random linear projection applied mathematics compressive measurement sparse representation new field known basis compressive sensing original signal linear programming subsequent recovery compress signal inverse scattering signal processing random linear projection applied mathematics compressive measurement sparse representation new field known basis compressive sensing original signal linear programming subsequent recovery compress signal |
| spellingShingle |
inverse scattering signal processing random linear projection applied mathematics compressive measurement sparse representation new field known basis compressive sensing original signal linear programming subsequent recovery compress signal inverse scattering signal processing random linear projection applied mathematics compressive measurement sparse representation new field known basis compressive sensing original signal linear programming subsequent recovery compress signal Marengo, Edwin A. Hernández, R. D. Citron, Y. R. Gruber, F. K. Zambrano, M. Lev-Ari, H. Compressive Sensing for Inverse Scattering |
| 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 |
| format |
Artículo |
| author |
Marengo, Edwin A. Hernández, R. D. Citron, Y. R. Gruber, F. K. Zambrano, M. Lev-Ari, H. |
| author_sort |
Marengo, Edwin A. |
| title |
Compressive Sensing for Inverse Scattering |
| title_short |
Compressive Sensing for Inverse Scattering |
| title_full |
Compressive Sensing for Inverse Scattering |
| title_fullStr |
Compressive Sensing for Inverse Scattering |
| title_full_unstemmed |
Compressive Sensing for Inverse Scattering |
| title_sort |
compressive sensing for inverse scattering |
| publishDate |
2017 |
| url |
http://ridda2.utp.ac.pa/handle/123456789/2413 http://ridda2.utp.ac.pa/handle/123456789/2413 |
| _version_ |
1796210016953303040 |
| score |
12.235305 |