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Generation and Display System of measurement matrix Based on DMD

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Journal of Beijing Institute of Technology 2018年第4期27卷 493-502页

作者： Wenzhao Gu Fu Zheng Guangjie Zhai National Space Science Center Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China

A measurement matrix is the key to sampling and signal reconstruction during the process of compressed *** the basis of digital light processing(DLP)technology,ageneration and display system of measurement matrix based on digital micro-mirror device(DMD)is proposed and well *** this system,the generation and controlling of measurement matrix are implemented on a PC,which reduces the hardware requirement to generate a random matrix and overcomes the difficulty of the hardware implementation for the random *** can set up the display number of the measurement matrix,the mode of display and display time according to the requirements from *** display information can be designed to complete the display of measurement matrix with a better *** system can be easily embedded into a variety of compressed sensing applications,which can be used to generate and display the corresponding measurement matrice with strong *** addition,the DMD of this system will be used as a spatial optical modulator to manipulate near-infrared light in a fast,accurate and efficient way in several applications such as in 3D scanning devices and spectrometers.

关键词： measurement matrix digital micro-mirror device(DMD) compressed sensing Gaussian random matrix

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Comparison and Analysis of the Performance on measurement matrix to Spectrum Estimation

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Comparison and Analysis of the Performance on Measurement Ma...

Near optimal condition of OMP algorithm in recovering sparse signal from noisy measurement

2012 National Conference on Information Technology and Computer Science

作者： Wang Keqing Institute of Communications Engineering PLA University of Science and Technology Nanjing,China Zhu Yong-gang Telecommunication Institute Nanjing,China Chen Jian-zhong Telecommunication Institute Nanjing,China Niu Ying-tao Telecommunication Institute Nanjing,China

In the spectrum estimation algorithm based on Compressed Sensing,the selection of measurement matrix has significant influence on whether can estimate the signal power spectrum with high *** the article,it briefly describes the basic principles of spectrum estimation algorithm and the common measurement *** addition,it presents a detailed comparison and analysis of the construction method,pros and cons among random measurement matrix,structured measurement matrix and deterministic measurement *** this basis,aiming at single-tone,multi-tone and QPSK signals,the feasibility of six kinds of measurement matrixes used in power spectrum estimation based on compressed sensing were validated and compared the estimation performance by *** simulation results show that Toeplitz matrix can obtain the minimum NMSE at the same compression ratio.

关键词： Compressed Sensing(CS) measurement matrix Power Spectrum Estimation(PSD)

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Journal of Systems Engineering and Electronics 2014年第4期25卷 547-553页

作者： Jia Li Qiang Wang Yi Shen School of Astronautics Harbin Institute of Technology

This paper analyzes the performance of the orthogonal matching pursuit （OMP） algorithm in recovering sparse signals from noisy measurement. Considering the fact that some matrices satisfy some restricted isometry properties （RIPs） but not the coherence condition, a superior RIP-based condition is proposed, which means that if the measurement matrix satisfies δk＋1 〈 1/（2 ＋ √k） and the minimum component signal-to-noise ratio （MCSNR） is bounded, the OMP algorithm can exactly identify the support of the original sparse signal within k iterations. Finally, the theoretical results are verified by numerical simulations con- cerning different values of MCSNR and noise levels.

关键词： compressive sensing (CS) orthogonal matching pur-suit (OMP) measurement matrix sufficient condition.

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An Optimal CDG Framework for Energy Efficient WSNs

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Chinese Journal of Electronics 2017年第1期26卷 137-144页

作者： YAN Wenjie DONG Yongfeng ZHANG Suqi WANG Zijuan School of Computer Science and Engineering Hebei University of Technology School of Information Engineering Tianjin University of Commerce School of Life Science Northeast Forestry University

Compressed sensing(CS) has been applied widely in Wireless sensor networks(WSNs) *** optimal Compressed data gathering(CDG) framework for energy efficient WSNs is proposed here. A novel measurement matrix optimization algorithm(MMOA) is proposed for compressed data measurement in WSNs. Diffusion wavelet transform matrix(DWTM) is chosen for sparse representation of the compressed data. An Optimal data aggregation tree(ODAT) algorithm is presented based on CS and routing technology. MMOA is to reduce the data transmissions under the same data reconstruction ratio. DWTM is to make the original data become more sparse and to increase the compressed data reconstruction ratio. The main purpose of ODAT is to minimize the energy consumption of the whole WSNs through the CDG technology and the optimal route. We validate the efficiency of the proposed CDG framework based on MMOA, DWTM and ODAT through extensive experiments.

关键词： Wireless sensor networks(WSNs) Compressed sensing(CS) measurement matrix Optimal data aggregation tree(ODAT)

A survey on distributed compressed sensing： theory and applications

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Frontiers of Computer Science 2014年第6期8卷 893-904页

作者： Hongpeng YIN Jinxing LI Yi CHAI Simon X. YANG College of Automation Chongqing University Chongqing 400030 China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing 400030 China State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing 400030 China School of Engineering University of Guelph Guelph N1G 2W1 Canada

The compressed sensing （CS） theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account. Distributed compressed sensing （DCS） is an extension of CS that takes advantage of both the inter- and intra-signal correlations, which is wildly used as a powerful method for the multi-signals sensing and compression in many fields. In this paper, the characteristics and related works of DCS are reviewed. The framework of DCS is introduced. As DCS＇s main portions, sparse representation, measurement matrix selection, and joint reconstruction are classified and summarized. The applications of DCS are also categorized and discussed. Finally, the conclusion remarks and the further research works are provided.

关键词： compressed sensing distributed compressed sensing sparse representation measurement matrix joint reconstruction joint sparsity model

Single Photon Compressive Imaging Based on Digital Grayscale Modulation Method

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Photonic Sensors 2021年第3期11卷 350-361页

作者： Chenglong Yuan Qiurong Yan Yiqiang Wu Yifan Wang Yuhao Wang School of Information Engineering Nanchang UniversityNanchang330031China

In single-pixel imaging or computational ghost imaging,the measurement matrix has a great impact on the performance of the imaging system,because it involves modulation of the optical signal and image *** measurement matrix reported in the existing literatures is first binarized and then loaded onto the digital micro-mirror device(DMD)for optical modulation,that is,each pixel can only be modulated into on-off *** this paper,we propose a digital grayscale modulation method for more efficient compressive *** the basis of this,we demonstrate a single photon compressive imaging system.A control and counting circuit,based on field-programmable gate array(FPGA),is developed to control DMD to conduct digital grayscale modulation and count single-photon pulse output from the photomultiplier tube(PMT)*** experimental results show that the imaging reconstruction quality can be improved by increasing the sparsity ratio properly and compressive sampling ratio(SR)of these gray-scale ***,when the compressive SR and sparsity ratio are increased appropriately to a certain value,the reconstruction quality is usually saturated,and the imaging reconstruction quality of the digital grayscale modulation is better than that of binary modulation.

关键词： Single photon imaging single pixel imaging measurement matrix grayscale modulation

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压缩感知中观测矩阵的构造及优化方法研究

压缩感知中观测矩阵的构造及优化方法研究

作者：刘莎东北大学

学位级别：硕士

With the information increases continuously, high speed of traditional signal sampling technologies which based on the conditions of signal-compressed and sparseness of the transforming dormin, can easily cause the verbose messages when dealing with the sampling. Candes and others provide that compressed sensing theory which lies on the Functional analysis and Approximation theory, is sampling compressed techniques of low sampling rate. It's well known for the unique compressed structure and relatively low sampling speed within several years, which solve the issues in traditional *** thesis propose the analysis and implementation for three core problems:sparse representation in compressed sensing, measurement matrix and algorithm of image reconstruction. In the representation of the sparse matrix, this thesis use DCT method. This thesis also apply the OMP to construct images. This thesis focus on selection and establishment of measurement matrix. First of all, this thesis provide compressed sensing method based on Beta distribution after considering the features of the beta distribution. Also, this thesis choose three different random ways to set up the measurement matrix. The result shows that Beta distribution based algorithms are no big deal compared with other conventional ways. This thesis also get conclusion of insensitiveness to random changes when use compressed sensing ***, this thesis provides a BA-based scale-free network model to construct the measurement matrix and use the single parameter change method to do the comparative experiment, thus proposing a BA-based scale-free model's optimization algorithm. The prior image can be reconfigured when compressed sensing rate reached at 0.8 during the experiments. With the comparison result, it concludes that relatively high or low network models are not suitable for compress sensing based sampling compression and reconstruction. To solve this issue, deeply, this thesis apply Orthogonal Tr

关键词： Compressed sensing measurement matrix Beta distribution complex network