Compressive sensing: To compress or not to compress

Kirachaiwanich, Davis; Liang, Qilian

doi:10.1109/acssc.2011.6190119

Cited by 11 publications

(11 citation statements)

References 9 publications

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“…It can be seen from Table 2 that, with the increase of compression ratio, the image reconstruction precision of the measurement matrix is improving [14] which can be told from the increasing PSNR of the reconstructed image. The tendency and the image reconstruction accuracy comparison between each matrix under different compression ratios are shown in Figure 1.…”

Section: The Simulation Results Under Different Compression Ratiosmentioning

confidence: 90%

The research of Kronecker product-based measurement matrix of compressive sensing

Zhang

Xiang

Wang

et al. 2013

J Wireless Com Network

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The theory of compressive sensing is briefly introduced, and some construction methods for measurement matrix are deduced. A measurement matrix based on Kronecker product is devised, and it has been proved in mathematical proof. Numerical simulations on 2-D image verify that the proposed measurement matrix has better performance in storage space, construction time, and image reconstruction effect when compared with commonly used matrices in compressive sensing. This novel measurement matrix offers great potential for hardware implementation of compressive sensing in image and high-dimensional signal.

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Section: The Simulation Results Under Different Compression Ratiosmentioning

confidence: 90%

The research of Kronecker product-based measurement matrix of compressive sensing

Zhang

Xiang

Wang

et al. 2013

J Wireless Com Network

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“…A rich literature has been published to investigate the theoretic bounds [2][3][4] as well as its applications in radar sensor network and wireless communication systems [5][6][7][8][9][10][11][12][13][14]. Consider a discrete signal f 2 ℝ N which can be expanded in an orthonormal basis Ψ ¼ ½ψ 1 ψ 2 Á Á Áψ n as follows:…”

Section: Compressive Sensing Overviewmentioning

confidence: 99%

On the Security of Wireless Sensor Networks via Compressive Sensing

Liang

Zhang

et al. 2015

Lecture Notes in Electrical Engineering

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Due to energy limitation of sensor nodes, the conventional security algorithms with high computation complexity are not suitable for wireless sensor networks (WSNs). We propose a compressive sensing-based encryption for WSNs, which provides both signal compression and encryption guarantees, without introducing additional computational cost of a separate encryption protocol. In this paper, we also discuss the information-theoretical and computational secrecy of compressive sensing algorithm. For proposed WSN, if only a fraction of randomizer bits is stored by an eavesdropper, then the probability that he/she cannot obtain any information about the plaintext approaches zero. Simulation results show a trade-off can be made between the sparsity of a random measurement matrix and the number of sensor nodes used to reconstruct the original signal at the fusion center.Keywords Wireless sensor networks • Compressive sensing • Perfect secrecy and data reconstruction IntroductionSensor nodes in wireless sensor networks (WSNs) are inherently resourceconstrained. These battery-operated nodes have limited processing capability and low storage capacity. In most practical situations, sensor nodes are unattended and even deployed in the hostile environments, which demands careful security consideration in the design of WSNs. Because of those constraints, the conventional security mechanisms with high computation complexity are not suitable for WSNs.

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“…10 The relationship between CS and MI is established in Ref. 11. In the paper of Kirachaiwanich et al, 11 the information theoretic lower bound of error probability is shown using the eigenvalue properties of covariance matrices.…”

Section: Introductionmentioning

confidence: 98%

“…In particular, reconstruction bounds of CS have been shown using information theory. 10,11 In Ref. 10, two possible models of CS, called output and input noise models, are considered.…”

Section: Introductionmentioning

confidence: 99%

Target detection and reconstruction for compressive multiple-input, multiple-output ultra-wideband noise radar imaging

Kwon

Narayanan

Rangaswamy

2013

J. Electron. Imaging

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We propose a sample selection method for multiple-input, multiple-output ultra-wideband noise radar imaging using compressive sensing. The proposed sample selection is based on comparing the norm values of candidates among the potential received signal and selecting the largest M samples among N per antenna to obtain selection diversity. Moreover, we propose an adaptive weighting allocation that improves reconstruction accuracy of compressive sensing by maximizing the mutual information between target echoes and transmitted signals. This weighting scheme is applicable to both sample selection schemes, a conventional random sampling and the proposed selection. Further, the weighting allocation with the knowledge of recovery error is proposed for more practical scenarios. Simulations show that the proposed selection and weighting allocation enhance multiple target detection probability and reduce normalized mean square error.

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Compressive sensing: To compress or not to compress

Cited by 11 publications

References 9 publications

The research of Kronecker product-based measurement matrix of compressive sensing

The research of Kronecker product-based measurement matrix of compressive sensing

On the Security of Wireless Sensor Networks via Compressive Sensing

Target detection and reconstruction for compressive multiple-input, multiple-output ultra-wideband noise radar imaging

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