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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Qianru Jiang Huang Bai Dan Li Xincai Huang |
| Copyright Year | 2014 |
| Description | Author affiliation: Zhejiang Provincial Key Lab. for Signal Process., Zhejiang Univ. of Technol., Hangzhou, China (Qianru Jiang; Huang Bai; Dan Li; Xincai Huang) |
| Abstract | Compressive sensing theory shows that sparse signals can be reconstructed from far less samples than those required by the classical Shannon-Nyquist Theorem. An optimized sensing matrix for a certain class of signals can further reduce the necessary number of samples. Additionally, in order to make the signals represented as sparse as possible, a dictionary can be optimized. In this paper, we introduce a framework for joint design of the sensing matrix and dictionary. A new design procedure is proposed, which is based on an alternative optimization of the sparsifying dictionary and the sensing matrix. Simulation results show that this alternate optimization yields a better performance in terms of signal recovery than that by those existing algorithms. |
| Starting Page | 510 |
| Ending Page | 515 |
| File Size | 1181450 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781479943159 |
| DOI | 10.1109/ICIEA.2014.6931217 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-06-09 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Dictionaries Sensors Sparse matrices Optimization Algorithm design and analysis Vectors Accuracy sparse representation Compressed sensing overcomplete dictionary sensing matrix |
| Content Type | Text |
| Resource Type | Article |
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