Sparse signal recovery and acquisition with graphical models
A 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, rea...
| Principais autores: | , , , |
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| Outros Autores: | |
| Formato: | Artigo |
| Idioma: | en_US |
| Publicado em: |
Institute of Electrical and Electronics Engineers (IEEE)
2012
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| Acesso em linha: | http://hdl.handle.net/1721.1/71888 https://orcid.org/0000-0002-7983-9524 |
| _version_ | 1826212018093293568 |
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| author | Cevher, Volkan Indyk, Piotr Carin, Lawrence Baraniuk, Richard G. |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Cevher, Volkan Indyk, Piotr Carin, Lawrence Baraniuk, Richard G. |
| author_sort | Cevher, Volkan |
| collection | MIT |
| description | A 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. |
| first_indexed | 2024-09-23T15:15:11Z |
| format | Article |
| id | mit-1721.1/71888 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:15:11Z |
| publishDate | 2012 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/718882022-09-29T13:38:22Z Sparse signal recovery and acquisition with graphical models Cevher, Volkan Indyk, Piotr Carin, Lawrence Baraniuk, Richard G. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Indyk, Piotr Indyk, Piotr A 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. 2012-07-30T15:40:30Z 2012-07-30T15:40:30Z 2010-11 Article http://purl.org/eprint/type/JournalArticle 1053-5888 http://hdl.handle.net/1721.1/71888 Cevher, Volkan et al. “Sparse Signal Recovery and Acquisition with Graphical Models.” IEEE Signal Processing Magazine (2010). © Copyright 2010 IEEE https://orcid.org/0000-0002-7983-9524 en_US http://dx.doi.org/10.1109/MSP.2010.938029 IEEE Signal Processing Magazine Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers (IEEE) IEEE |
| spellingShingle | Cevher, Volkan Indyk, Piotr Carin, Lawrence Baraniuk, Richard G. Sparse signal recovery and acquisition with graphical models |
| title | Sparse signal recovery and acquisition with graphical models |
| title_full | Sparse signal recovery and acquisition with graphical models |
| title_fullStr | Sparse signal recovery and acquisition with graphical models |
| title_full_unstemmed | Sparse signal recovery and acquisition with graphical models |
| title_short | Sparse signal recovery and acquisition with graphical models |
| title_sort | sparse signal recovery and acquisition with graphical models |
| url | http://hdl.handle.net/1721.1/71888 https://orcid.org/0000-0002-7983-9524 |
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