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EnglishThe quest for optimal sensing matrices is crucial in the design of efficient Compressed Sensing architectures. In this paper we propose a maximum entropy criterion for the design of optimal Hadamard sensing matrices (and similar deterministic ensembles) when the signal being acquired is sparse and non-white. Since the resulting design strategy entails a combinatorial step, we devise a fast evolutionary algorithm to find sensing matrices that yield high-entropy measurements. Experimental results exploiting this strategy show quality gains when performing the recovery of optimally sensed small images and electrocardiographic signals. © 2014 IEEE.
| Titolo: | Maximum entropy hadamard sensing of sparse and localized signals | |
| Autore/i: | CAMBARERI, VALERIO; ROVATTI, RICCARDO; Setti, Gianluca | |
| Autore/i Unibo: | ||
| Anno: | 2014 | |
| Rivista: | ||
| Titolo del libro: | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | |
| Pagina iniziale: | 2357 | |
| Pagina finale: | 2361 | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1109/ICASSP.2014.6854021 | |
| Abstract: | The quest for optimal sensing matrices is crucial in the design of efficient Compressed Sensing architectures. In this paper we propose a maximum entropy criterion for the design of optimal Hadamard sensing matrices (and similar deterministic ensembles) when the signal being acquired is sparse and non-white. Since the resulting design strategy entails a combinatorial step, we devise a fast evolutionary algorithm to find sensing matrices that yield high-entropy measurements. Experimental results exploiting this strategy show quality gains when performing the recovery of optimally sensed small images and electrocardiographic signals. © 2014 IEEE. | |
| Data stato definitivo: | 12-set-2016 | |
| Appare nelle tipologie: | 4.01 Contributo in Atti di convegno |
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http://hdl.handle.net/11585/562419
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