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Compressed Sensing
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  • Cited by 591
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Testa, Matteo Valsesia, Diego Bianchi, Tiziano and Magli, Enrico 2019. Compressed Sensing for Privacy-Preserving Data Processing. p. 25.

    Esmaeili, Hamid Rostami, Majid and Kimiaei, Morteza 2018. Combining line search and trust-region methods for ℓ1-minimization. International Journal of Computer Mathematics, Vol. 95, Issue. 10, p. 1950.

    Zhang, Ruoyu Zhao, Honglin Shan, Chengzhao and Jia, Shaobo 2018. A Low Complexity Correlation Algorithm for Compressive Channel Estimation in Massive MIMO System. International Journal of Wireless Information Networks, Vol. 25, Issue. 4, p. 371.

    Li, Gen and Gu, Yuantao 2018. Restricted Isometry Property of Gaussian Random Projection for Finite Set of Subspaces. IEEE Transactions on Signal Processing, Vol. 66, Issue. 7, p. 1705.

    Zhao, Yun-Bin Jiang, Houyuan and Luo, Zhi-Quan 2018. Weak Stability of ℓ1-Minimization Methods in Sparse Data Reconstruction. Mathematics of Operations Research,

    Cruz Hernández, Heriberto and de la Fraga, Luis Gerardo 2018. NEO 2016. Vol. 731, Issue. , p. 141.

    Oktem, Figen S. Gao, Liang and Kamalabadi, Farzad 2018. Handbook of Convex Optimization Methods in Imaging Science. p. 105.

    Golovanov, D. Yu. and Parfenov, V. I. 2018. Detection Efficiency of Signal with Unknown Non-Power Parameter Using Algorithms Based on the Compressive Sensing Theory. Radioelectronics and Communications Systems, Vol. 61, Issue. 8, p. 361.

    Li, Qinxue Xu, Bugong Li, Shanbin Liu, Yonggui and Cui, Delong 2018. Reconstruction of measurements in state estimation strategy against deception attacks for cyber physical systems. Control Theory and Technology, Vol. 16, Issue. 1, p. 1.

    Fardad, Mohammad Sayedi, Sayed Masoud and Yazdian, Ehsan 2018. A Low-Complexity Hardware for Deterministic Compressive Sensing Reconstruction. IEEE Transactions on Circuits and Systems I: Regular Papers, Vol. 65, Issue. 10, p. 3349.

    Routtenberg, Tirza and Eldar, Yonina C. 2018. Centralized Identification of Imbalances in Power Networks With Synchrophasor Data. IEEE Transactions on Power Systems, Vol. 33, Issue. 2, p. 1981.

    Jafari, Saeed Kashani, Farokh Hodjat and Ghorbani, Ayaz 2018. ISAR Image Reconstruction with Heavily Corrupted Data Based on Normal Inverse Gaussian Model. Journal of the Indian Society of Remote Sensing,

    O’Connor, Nolan J. Jonayat, A. S. M. Janik, Michael J. and Senftle, Thomas P. 2018. Interaction trends between single metal atoms and oxide supports identified with density functional theory and statistical learning. Nature Catalysis, Vol. 1, Issue. 7, p. 531.

    Guilment, Thomas Socheleau, Francois-Xavier Pastor, Dominique and Vallez, Simon 2018. Sparse representation-based classification of mysticete calls. The Journal of the Acoustical Society of America, Vol. 144, Issue. 3, p. 1550.

    Choi, Jinho 2018. Compressive Random Access With Coded Sparse Identification Vectors for MTC. IEEE Transactions on Communications, Vol. 66, Issue. 2, p. 819.

    Yang, Chengshuai Qi, Dalong Wang, Xing Cao, Fengyan He, Yilin Wen, Wenlong Jia, Tianqing Tian, Jinshou Sun, Zhenrong Gao, Liang Zhang, Shian and Wang, Lihong V. 2018. Optimizing codes for compressed ultrafast photography by the genetic algorithm. Optica, Vol. 5, Issue. 2, p. 147.

    Rusu, Cristian Thompson, John and Robertson, Neil M. 2018. Sensor Scheduling With Time, Energy, and Communication Constraints. IEEE Transactions on Signal Processing, Vol. 66, Issue. 2, p. 528.

    Zhao, Run Zhang, Qian Li, Dong Chen, Haonan and Wang, Dong 2018. PRTS: A Passive RFID Real-Time Tracking System Under the Conditions of Sparse Measurements. IEEE Sensors Journal, Vol. 18, Issue. 5, p. 2097.

    Nagananda, K. G. and Varshney, Pramod K. 2018. On Weak Signal Detection With Compressive Measurements. IEEE Signal Processing Letters, Vol. 25, Issue. 1, p. 125.

    Yang, Huayong Lin, Xiaoli Jiang, Xudong and Hwang, Jenq-Neng 2018. A novel variation-based block compressed sensing restoration method. p. 325.

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Book description

Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject, highlighting theoretical advances and a range of applications, as well as outlining numerous remaining research challenges. After a thorough review of the basic theory, many cutting-edge techniques are presented, including advanced signal modeling, sub-Nyquist sampling of analog signals, non-asymptotic analysis of random matrices, adaptive sensing, greedy algorithms and use of graphical models. All chapters are written by leading researchers in the field, and consistent style and notation are utilized throughout. Key background information and clear definitions make this an ideal resource for researchers, graduate students and practitioners wanting to join this exciting research area. It can also serve as a supplementary textbook for courses on computer vision, coding theory, signal processing, image processing and algorithms for efficient data processing.

Reviews

'… a charming encouragement to fascinating scientific adventure for talented students. Also … a solid reference platform for researchers in many fields.'

Artur Przelaskowski Source: IEEE Communications Magazine

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