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System design and first airborne experiment of sparse microwave imaging radar: initial results

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Science China(Information Sciences) 2015年第6期58卷 95-104页

作者： ZHANG BingChen ZHANG Zhe JIANG ChengLong ZHAO Yao HONG Wen WU YiRong Science and Technology on Microwave Imaging Laboratory Chinese Academy of Sciences Institute of Electronics Chinese Academy of Sciences University of Chinese Academy of Sciences

In this paper, we mainly study the system design of sparse microwave imaging radar and report some of the preliminary results of airborne experiments performed with it. Sparse microwave imaging radar is a novel concept which introduces the sparse signal processing theory to microwave imaging replacing the conventional matched filtering processing method. With the exploitation of the sparse microwave imaging, the radar system could achieve better performance. As a newly developed concept, the two main types of applications of sparse microwave imaging are found in adopting the sparse signal processing theory to current radar systems, and in designing an optimized sparse microwave imaging system. Here we are trying the latter that mainly aims at lower PRF and wider swath. We first introduce the theories of sparse microwave imaging radar and its imaging algorithm. Then we discuss the system designing principles, including the sampling scheme, signal bandwidth,SNR and multi-channel mode. Based on the relationships of these parameters, we provide a design example of radar parameters. In the end, we exploit an airborne experiment using our designed radar system with jittered azimuth sampling strategy. Some preliminary analysis from the experiment result is also provided.

关键词： sparse microwave imaging synthetic aperture radar(SAR) compressive sensing displaced phase center antenna(DPCA) system design ai

Multiple-image encryption by two-step phase-shifting interferometry and spatial multiplexing of smooth compressed signal

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Chinese Physics B 2018年第7期27卷 331-338页

作者： Xue Zhang Xiangfeng Meng Yurong Wang Xiulun Yang Yongkai Yin Department of Optics School of Information Science and Engineeringand Shandong Provincial Key Laboratory of Laser Technology and ApplicationShandong UniversityJinan 250100China

A multiple-image encryption method based on two-step phase-shifting interferometry （PSI） and spatial multiplexing of a smooth compressed signal is proposed. In the encoding and encryption process, with the help of four index matrices to store original pixel positions, all the pixels of four secret images are firstly reordered in an ascending order; then, the four reordered images are transformed by five-order Haar wavelet transform and performed sparseness operation. After Arnold transform and pixels sampling operation, one combined image can be grouped with the aid of compressive sensing （CS） and spatial multiplexing techniques. Finally, putting the combined image at the input plane of the PSI encryption scheme, only two interferograms ciphertexts can be obtained. During the decoding and decryption, utilizing all the secret key groups and index matrices keys, all the original secret images can be successfully decrypted by a wave-front retrieval algorithm of two-step PSI, spatial de-multiplexing, inverse Arnold transform, inverse discrete wavelet transform, and pixels reordering operation.

关键词： compressive sensing two-step phase-shifting interferometry image encryption

Coded aperture compressive imaging array applied for surveillance systems

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Journal of Systems Engineering and Electronics 2013年第6期24卷 1019-1028页

作者： Jing Chen Yongtian Wang Hanxiao Wu School of Optoelectronics Beijing Institute of Technology

This paper proposes an application of compressive imaging systems to the problem of wide-area video surveillance systems. A parallel coded aperture compressive imaging system and a corresponding motion target detection algorithm in video using compressive image data are developed. Coded masks with random Gaussian, Toeplitz and random binary are utilized to simulate the compressive image respectively. For compressive images, a mixture of the Gaussian distribution is applied to the compressed image field to model the background. A simple threshold test in compressive sampling image is used to declare motion objects. Foreground image retrieval from underdetermined measurement using the total variance optimization algorithm is explored. The signal-to-noise ratio （SNR） is employed to evaluate the image quality recovered from the compressive sampling signals, and receiver operation characteristic （ROC） curves are used to quantify the performance of the motion detection algorithm. Experimental results demonstrate that the low dimensional compressed imaging representation is sufficient to determine spatial motion targets. Compared with the random Gaussian and Toeplitz mask, motion detection algorithms using the random binary phase mask can yield better detection results. However using the random Gaussian and Toeplitz phase mask can achieve high resolution reconstructed images.

关键词： compressive imaging coded aperture compressive sensing motion detection

Sparse recovery: from vectors to tensors

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National Science Review 2018年第5期5卷 756-767页

作者： Yao Wang Deyu Meng Ming Yuan School of Mathematics and Statistics Xi'an Jiaotong University Shenyang Institute of Automation Chinese Academy of Sciences Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Department of Statistics Columbia University

Recent advances in various fields such as telecommunications, biomedicine and economics, among others, have created enormous amount of data that are often characterized by their huge size and high dimensionality. It has become evident, from research in the past couple of decades, that sparsity is a flexible and powerful notion when dealing with these data, both from empirical and theoretical viewpoints. In this survey, we review some of the most popular techniques to exploit sparsity, for analyzing high-dimensional vectors, matrices and higher-order tensors.

关键词： high-dimensional data sparsity compressive sensing low-rank matrix recovery tensors

Global sensitivity analysis based on high-dimensional sparse surrogate construction

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Applied Mathematics and Mechanics(English Edition) 2017年第6期38卷 797-814页

作者： Jun HU Shudao ZHANG Institute of Applied Physics and Computational Mathematics

Surrogate models are usually used to perform global sensitivity analysis （GSA） by avoiding a large ensemble of deterministic simulations of the Monte Carlo method to provide a reliable estimate of GSA indices. However, most surrogate models such as polynomial chaos （PC） expansions suffer from the curse of dimensionality due to the high-dimensional input space. Thus, sparse surrogate models have been proposed to alleviate the curse of dimensionality. In this paper, three techniques of sparse reconstruc- tion are used to construct sparse PC expansions that are easily applicable to computing variance-based sensitivity indices （Sobol indices）. These are orthogonal matching pursuit （OMP）, spectral projected gradient for L1 minimization （SPGL1）, and Bayesian compressive sensing with Laplace priors. By computing Sobol indices for several benchmark response models including the Sobol function, the Morris function, and the Sod shock tube problem, effective implementations of high-dimensional sparse surrogate construction are exhibited for GSA.

关键词： global sensitivity analysis (GSA) curse of dimensionality sparse surrogate construction polynomial chaos (PC) compressive sensing

Dual-compressed photoacoustic single-pixel imaging

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National Science Review 2023年第1期10卷 187-195页

作者： Yuning Guo Baowen Li Xiaobo Yin Department of Mechanical Engineering University of Colorado Department of Material Science and Engineering Department of Physics Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Materials Science and Engineering Program University of Colorado

Photoacoustic imaging,an acoustic imaging modality with potentially optical resolution in an optical turbid medium,has attracted great ***,the convergence of wavefront optimization and raster scanning in computational photoacoustic imaging leads to the challenge of fast mapping,especially for a spatial resolution approaching the acoustic deep-subwavelength *** a sparse sampling paradigm,compressive sensing has been applied in numerous fields to accelerate data acquisition without significant quality *** this work,we propose a dual-compressed approach for photoacoustic surface tomography that enables high-efficiency imaging with 3D spatial resolution unlimited by the acoustics in a turbid *** dual-compressed photoacoustic imaging with single-pixel detection,enabled by spatially optical modulation with synchronized temporally photoacoustic coding,allows decoding of the fine optical information from the modulated acoustic signal even when the variance of original photoacoustic signals is *** perform a proof-of-principle numerical demonstration of dual-compressed photoacoustic imaging, that resolves acoustic sub-acoustic-wavelength details with a significantly reduced number of measurements,revealing the potential for dynamic *** dual-compressed concept,which transforms unobtrusive spatial difference into spatio-temporal detectable information,can be generalized to other imaging modalities to re alize efficient,high-spatial-resolution imaging.

关键词： photoacoustic imaging compressive sensing single-pixel imaging surface tomography

Real-time object tracking via compressive feature selection

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Frontiers of Computer Science 2016年第4期10卷 689-701页

作者： Kang LI Fazhi HE Xiao CHEN School of Computer Science Wuhan University Wuhan 430072 China

Recently, compressive tracking （CT） has been widely proposed for its efficiency, accuracy and robustness on many challenging sequences. Its appearance model employs non-adaptive random projections that preserve the structure of the image feature space. A very sparse measurement matrix is used to extract features by multiplying it with the feature vector of the image patch. An adaptive Bayes classifier is trained using both positive samples and negative samples to separate the target from background. On the CT frame- work, however, some features used for classification have weak discriminative abilities, which reduces the accuracy of the strong classifier. In this paper, we present an online compressive feature selection algorithm（CFS） based on the CT framework. It selects the features which have the largest margin when using them to classify positive samples and negative samples. For features that are not selected, we define a random learning rate to update them slowly, It makes those weak classifiers preserve more target information, which relieves the drift when the appearance of the target changes heavily. Therefore, the classifier trained with those discriminative features couples its score in many challenging sequences, which leads to a more robust tracker. Numerous experiments show that our tracker could achieve superior result beyond many state-of-the-art trackers.

关键词： object tracking compressive sensing supervised learning real-time

Secure and Efficient Data Transmission Scheme Based on Physical Mechanism

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Computers, Materials & Continua 2023年第5期75卷 3589-3605页

作者： Ping Zhang Haoran Zhu Wenjun Li Osama Alfarraj Amr Tolba Gwang-jun Kim School of Computer Science Hunan University of Technology and BusinessChangsha410205China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea

Many Internet of things application scenarios have the characteristics of limited hardware resources and limited energy supply,which are not suitable for traditional security *** security technology based on the physicalmechanism has attracted extensive *** to improve the key generation rate has always been one of the urgent problems to be solved in the security technology based on the physical *** this paper,superlattice technology is introduced to the security field of Internet of things,and a high-speed symmetric key generation scheme based on superlattice for Internet of things is *** order to ensure the efficiency and privacy of data transmission,we also combine the superlattice symmetric key and compressive sensing technology to build a lightweight data transmission scheme that supports data compression and data encryption at the same *** analysis and experimental evaluation results show that the proposed scheme is superior to the most closely related work.

关键词： Data transmission key generation data privacy compressive sensing Internet of Things(IoT)

Recent Advances in Computational Photography

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Chinese Journal of Electronics 2019年第1期28卷 1-5页

作者： DAI Qionghai WU Jiamin FAN Jingtao XU Feng CAO Xun Department of Automation Tsinghua University Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP) Tsinghua University School of Software Tsinghua University School of Electronic Science and Engineering Nanjing University

Traditional photography focuses on the optimization of lenses for a perfect imaging ***, with the great developments of computational resources and optical modulation devices, we can achieve more powerful imaging abilities with concise *** photography is such an emerging interdisciplinary field by incorporating computational strategy in traditional imaging system to break the limitations in various dimensions such as spectrum, time and space. Recent advances in different aspects have aroused great interests and introduced tremendous applications in biology, material science and computer vision.

关键词： Computational photography Gigapixel imaging Super-resolution imaging compressive sensing Hyperspectral imaging