Construction of compressed sensing matrices for signal processing

Construction of compressed sensing matrices for signal processing Multimed Tools Appl https://doi.org/10.1007/s11042-018-6120-4 Construction of compressed sensing matrices for signal processing 1,2 2,3 2,3 Yingmo Jie & Cheng Guo & Mingchu Li & 2,3 Bin Feng Received: 21 August 2017 /Revised: 6 March 2018 /Accepted: 8 May 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract To cope with the huge expenditure associated with the fast growing sampling rate, compressed sensing (CS) is proposed as an effective technique of signal processing. In this paper, first, we construct a type of CS matrix to process signals based on singular linear spaces over finite fields. Second, we analyze two kinds of attributes of sensing matrices. One is the recovery performance corresponding to compressing and recovering signals. In particular, we apply two types of criteria, error-correcting pooling designs (PD) and restricted isometry property (RIP), to investigate this attribute. Another is the sparsity corresponding to storage and transmission signals. Third, in order to improve the ability associated with our matrices, we use an embedding approach to merge our binary matrices with some other matrices owing low coherence. At last, we compare our matrices with other existing ones via numerical simulations and the results show that ours outperform others. . . http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Construction of compressed sensing matrices for signal processing

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-018-6120-4
Publisher site
See Article on Publisher Site

Abstract

Multimed Tools Appl https://doi.org/10.1007/s11042-018-6120-4 Construction of compressed sensing matrices for signal processing 1,2 2,3 2,3 Yingmo Jie & Cheng Guo & Mingchu Li & 2,3 Bin Feng Received: 21 August 2017 /Revised: 6 March 2018 /Accepted: 8 May 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract To cope with the huge expenditure associated with the fast growing sampling rate, compressed sensing (CS) is proposed as an effective technique of signal processing. In this paper, first, we construct a type of CS matrix to process signals based on singular linear spaces over finite fields. Second, we analyze two kinds of attributes of sensing matrices. One is the recovery performance corresponding to compressing and recovering signals. In particular, we apply two types of criteria, error-correcting pooling designs (PD) and restricted isometry property (RIP), to investigate this attribute. Another is the sparsity corresponding to storage and transmission signals. Third, in order to improve the ability associated with our matrices, we use an embedding approach to merge our binary matrices with some other matrices owing low coherence. At last, we compare our matrices with other existing ones via numerical simulations and the results show that ours outperform others. . .

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: May 29, 2018

References

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