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http://bura.brunel.ac.uk/handle/2438/9126
Title: | Fast and efficient compressive sensing using structurally random matrices |
Authors: | Do, T Gan, L Nguyen, N Tran, TD |
Keywords: | Compressed sensing;Fast and efficient algorithm;Random projection;Sparse reconstruction |
Issue Date: | 2012 |
Publisher: | IEEE |
Citation: | IEEE Transactions on Signal Processing, 60(1), 139 - 154, 2012 |
Abstract: | This paper introduces a new framework to construct fast and efficient sensing matrices for practical compressive sensing, called Structurally Random Matrix (SRM). In the proposed framework, we prerandomize the sensing signal by scrambling its sample locations or flipping its sample signs and then fast-transform the randomized samples and finally, subsample the resulting transform coefficients to obtain the final sensing measurements. SRM is highly relevant for large-scale, real-time compressive sensing applications as it has fast computation and supports block-based processing. In addition, we can show that SRM has theoretical sensing performance comparable to that of completely random sensing matrices. Numerical simulation results verify the validity of the theory and illustrate the promising potentials of the proposed sensing framework. |
Description: | This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
URI: | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6041037 http://bura.brunel.ac.uk/handle/2438/9126 |
DOI: | http://dx.doi.org/10.1109/TSP.2011.2170977 |
ISSN: | 1053-587X |
Appears in Collections: | Electronic and Electrical Engineering Dept of Electronic and Electrical Engineering Research Papers |
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