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Sparse Signal Recovery and Acquisition with Graphical Models

Volkan Cevher, Piotr Indyk, Lawrence Carin, Richard Baraniuk
2010 IEEE Signal Processing Magazine  
Coding this structure using an appropriate model enables JPEG2000 and other similar algorithms to compress images close to the maximum amount possible, and significantly better than a naive coder that  ...  An application du jour of the sparse signal recovery problem is compressive sensing (CS), which integrates the sparse representations with two other key aspects of the linear dimensionality reduction:  ...  Fig. 4 . 4 exploits the Ising model and demonstrates that it naturally motivates a greedy search algorithm for background subtraction with clustered sparsity, dubbed Lattice matching pursuit (LAMP).  ... 
doi:10.1109/msp.2010.938029 fatcat:6bopdzqr4bda3dzpaof2tqnqlq

Fast Adaptive Character Animation Synthesis Based on Greedy Algorithm

Yanqiu Zhu, Qixing Chen, Jing Liu, Xiaoying Tian, Wei Wang
2021 Complexity  
Secondly, the multipath matching tracking algorithm of the greedy algorithm is studied to generate multiple candidate sets through multiple paths, and finally, the candidate set with the minimum residual  ...  Finite state machine (FSM) is an efficient behavior modeling method, which can describe the behavioral decisions of fast adaptive character animation synthesis in a complex virtual environment.  ...  By using the excess criterion of sparsity estimation, the range of sparsity is estimated first, and then, the estimated sparsity is corrected, which speeds up the execution speed of the algorithm and is  ... 
doi:10.1155/2021/6685861 fatcat:vrfihqbienhk5fean6ztnut4ci

Pilot-Assisted MIMO-V-OFDM systems: Compressed Sensing and Deep Learning Approaches

Wei Zhang, Xuyang Gao, Zhipeng Li, Yibing Shi
2020 IEEE Access  
Simulation data are used to evaluate the classical sparsity adaptive matching (SAMP) algorithms and the proposed Variable Threshold SAMP (VTSAMP) algorithm, and the results show that the improved method  ...  For the optimization of the sensing matrix, we discuss the influence of pilot search algorithms and evaluation criteria and propose a particle swarm optimization (PSO) based pilot search algorithm with  ...  Li from the Center for Public Security Information and Equipment Integration Technology for his technological help.  ... 
doi:10.1109/access.2020.2964046 fatcat:obqxbe6bz5glxlmz3bbjahvoxu

Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems

Luisa F. Polania, Rafael E. Carrillo, Manuel Blanco-Velasco, Kenneth E. Barner
2015 IEEE journal of biomedical and health informatics  
In this paper, we propose to exploit the structure of the wavelet representation of the ECG signal to boost the performance of CS-based methods for compression and reconstruction of ECG signals.  ...  However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls short of the performance attained by state-of-the-art wavelet based  ...  Barner for providing the right combination of supervision, encouragement, and support during my Ph.D. studies. I thank him for his efforts on helping me become a better researcher and writer.  ... 
doi:10.1109/jbhi.2014.2325017 pmid:24846672 fatcat:ea4dfl3r2bgxphvsecyvqweh6m

2021 Index IEEE Transactions on Signal Processing Vol. 69

2021 IEEE Transactions on Signal Processing  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  Zhang, X., +, TSP 2021 6055-6070 Compressed sensing A Two Stage Generalized Block Orthogonal Matching Pursuit (TSGBOMP) Algorithm.  ... 
doi:10.1109/tsp.2022.3162899 fatcat:kcubj566gzb4zkj7xb5r5we3ri

Minimax Reconstruction Risk of Convolutional Sparse Dictionary Learning

Shashank Singh, Barnabás Póczos, Jian Ma
2018 International Conference on Artificial Intelligence and Statistics  
We compare our results to similar results for IID SDL and verify our theory with synthetic experiments.  ...  Sparse dictionary learning (SDL) has become a popular method for learning parsimonious representations of data, a fundamental problem in machine learning and signal processing.  ...  The work was also partly supported by NSF IIS-1563887, Darpa D3M program, and AFRL (to B.P.), and NSF IIS-1717205 and NIH HG007352 (to J.M.).  ... 
dblp:conf/aistats/SinghPM18 fatcat:vktn443mcfbevntm37xvpqat5u

Maximum Correntropy Adaptive Filtering Approach for Robust Compressive Sensing Reconstruction [article]

Yicong He, Fei Wang, Shiyuan Wang, Jiuwen Cao, Badong Chen
2017 arXiv   pre-print
Robust compressive sensing(CS) reconstruction has become an attractive research topic in recent years.  ...  Most of existing robust CS reconstruction algorithms are based on greedy pursuit method or convex relaxation approach.  ...  In the last decade, many algorithms have been proposed to show accurate reconstruction performance, including greedy pursuit approaches such as matching pursuit(MP) [12] , orthogonal matching pursuit(  ... 
arXiv:1706.03226v1 fatcat:lbimnssrbnbo7cqoumz3qs75lm

A User's Guide to Compressed Sensing for Communications Systems

Kazunori HAYASHI, Masaaki NAGAHARA, Toshiyuki TANAKA
2013 IEICE transactions on communications  
After summarizing several major algorithms to obtain a sparse solution focusing on the 1 optimization and the greedy approaches, we introduce applications of compressed sensing to communications systems  ...  and its various applications to communications systems.  ...  Tropp developed a greedy algorithm for compressed sensing and proposed CoSaMP (Compressive Sampling Matching Pursuit) in [90] .  ... 
doi:10.1587/transcom.e96.b.685 fatcat:6v3rtpp2uncutehn57yyqt6hju

On the Error Probability of Support Recovery for Orthogonal Matching Pursuit with a Random Measurement Matrix

Yonggu Lee, Jinho Choi, Euiseok Hwang.
2020 IEEE Access  
In this paper, an asymptotic bound on the recovery error probability of a sparse signal is derived for the orthogonal matching pursuit algorithm.  ...  The proposed bound is based on the support recovery analysis with a random measurement matrix, which gets closer to the empirical bound tightly in a large system and high signal-to-noise ratio regime.  ...  Besides, there are low-complexity greedy algorithms to recover a sparse signal such as orthogonal matching pursuit (OMP) and compressive sampling matching pursuit (CoSaMP) [4] .  ... 
doi:10.1109/access.2020.2995912 fatcat:6mxlramw4ng2feh7hap7dxcrvq

Snapshot compressive spectral - depth imaging based on light field

Xiaoming Ding, QiangQiang Yan, Liang Hu, Shubo Zhou, Ruyi Wei, Xiaocheng Wang, Yupeng Li
2022 EURASIP Journal on Advances in Signal Processing  
The datacube containing depth estimation can be recovered with the compressed sensing and digital refocus framework.  ...  estimation and spectral intensity of input scenes simultaneously.  ...  [26] , Orthogonal Matching Pursuit (OMP) algorithm [27] and some learning methods based on deep network [28] [29] [30] .  ... 
doi:10.1186/s13634-022-00834-x fatcat:k6m7g3zogfgqpdzlslrmr6szfq

2020 Index IEEE Transactions on Industrial Informatics Vol. 16

2020 IEEE Transactions on Industrial Informatics  
Dustdar, S., Guest Editorial:Special Section on End-Edge-Cloud Orchestrated Algorithms, Systems and Applications; TII July 2020 4788-4790 Jiang, J., see Jia, G., 1993-2002 Jiang, J., see Liu, L., 2072  ...  -5149 Meng, K., see Zhang, Y., TII July 2020 4390-4402 Meng, K., Jia, Y., Yang, H., Niu, F., Wang, Y., and Sun, D., Motion Planning and Robust Control for the Endovascular Navigation of a Microrobot  ...  ., +, TII Feb. 2020 959-968 Secure Transmission of Compressed Sampling Data Using Edge Clouds. Surface Defect Detection via Entity Sparsity Pursuit With Intrinsic Priors.  ... 
doi:10.1109/tii.2021.3053362 fatcat:blfvdtsc3fdstnk6qoaazskd3i

Introduction to the Issue on Robust Subspace Learning and Tracking: Theory, Algorithms, and Applications

T. Bouwmans, N. Vaswani, P. Rodriguez, R. Vidal, Z. Lin
2018 IEEE Journal on Selected Topics in Signal Processing  
2015-2018, and AAAI Conference on Artificial Intelligence 2019.  ...  Thus, the most commonly used subspace clustering algorithms, i.e. sparse SC (SSC), SSC-Orthogonal Matching Pursuit (SSC-OMP), and thresholding based SC (TSC) are investigated in terms of robustness to  ... 
doi:10.1109/jstsp.2018.2879245 fatcat:z3ohqdl37nat3pjo65fzsf2ady

A Survey of Sparse Representation: Algorithms and Applications

Zheng Zhang, Yong Xu, Jian Yang, Xuelong Li, David Zhang
2015 IEEE Access  
Sparse representation also has a good reputation in both theoretical research and practical applications. Many different algorithms have been proposed for sparse representation.  ...  The main purpose of this article is to provide a comprehensive study and an updated review on sparse representation and to supply a guidance for researchers.  ...  ACKNOWLEDGMENT The authors would like to thank Jian Wu for many inspiring discussions and he is ultimately responsible for many of the ideas in the algorithm and analysis.  ... 
doi:10.1109/access.2015.2430359 fatcat:fdi57s5xxfc3jekrgbgxigkt2q

Traditional and recent approaches in background modeling for foreground detection: An overview

Thierry Bouwmans
2014 Computer Science Review  
A B S T R A C T Background modeling for foreground detection is often used in different applications to model the background and then detect the moving objects in the scene like in video surveillance.  ...  Furthermore, we present the available resources, datasets and libraries. Then, we conclude with several promising directions for future research.  ...  Acknowledgments I would like to acknowledge Megan Cook for her help in proofreading this paper.  ... 
doi:10.1016/j.cosrev.2014.04.001 fatcat:wccwuwltk5fr7lsgmsu5qbxclm

Explainable Artificial Intelligence (XAI) for 6G: Improving Trust between Human and Machine [article]

Weisi Guo
2019 arXiv   pre-print
In this review, we outline the core concepts of Explainable Artificial Intelligence (XAI) for 6G, including: public and legal motivations, definitions of explainability, performance vs. explainability  ...  Our review is grounded in cases studies for both PHY and MAC layer optimisation, and provide the community with an important research area to embark upon.  ...  Acknowledgements: The author wishes to acknowledge EC H2020 grant 778305: DAWN4IoE -Data Aware Wireless Network for Internet-of-Everything, and The Alan Turing Institute under the EPSRC grant EP/N510129  ... 
arXiv:1911.04542v2 fatcat:2lm7iyoyunbkhkfya5txeos3zm
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