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  • In Jour Smart Sensing And Intelligent Systems

 

Article | 01-June-2015

COMPRESSIVE SENSING BY COLPITTS CHAOTIC OSCILLATOR FOR IMAGE SENSORS

Compressive sensing uses simultaneous sensing and compression to provide an efficient image acquisition technique and it has been demonstrated in optical and electrical image sensors. To guarantee exact recovery from sparse measurements, specific sensing matrix, which satisfies the Restricted Isometry Property (RIP), should be well chosen. Toeplitz-structured chaotic sensing matrix constructed by Logistic map has been proved to satisfy RIP with high probability. In this paper, we propose that

Li Liu, Peng Yang, Jianguo Zhang, Guifen Wei

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 2, 1225–1243

Article | 01-December-2014

APPLICATIONS OF COMPRESSIVE SENSING OVER WIRELESS FADING CHANNELS

reconstruction algorithm of compressive sensing can control high-intensity gaussian noise in the channels well preceding WSDN, but also the effect of BPDN denoising the common impulse noise is not obvious, to denoise impulse noise, we adopt IBPDN (Improved Basis Pursuit Denoising) that changes L2 form to noise to L1 form, which was proposed in 2006 by Guosheng Bing. The experimental data show that IBPDN has good anti-noise ability to both gaussian noise and impulse noise; furthermore, more satisfactory

Shan Wang, YongHong Hu, HaoJun Zhou, Yu Sun, TingYa Yan, Chi Zhang

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 4, 1683–1700

Research Article | 27-December-2017

IMPROVED MEASURE ALGORITHM BASED ON CoSaMP FOR IMAGE RECOVERY

In order to improve the quality of the reconstruction image which using Compressive sensing(CS) algorithm. Based on improved measurement matrix combined with CS Matching Pursuit(CoSaMP)algorithm, this paper presents a kind of Fourier Ring Compressive Sampling Matching Pursuit (FR-CoSaMP) algorithm. The algorithm superimposed deterministic ring measurement matrix to optimize measurement process on the basis of Fourier measurement matrix. And solve the iterative inverse operation by using FFT

Guohui Wu, Xingkun Li, Jiyang Dai

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 2, 724–739

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