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＜電子ブック＞
Compressed Sensing in Information Processing / edited by Gitta Kutyniok, Holger Rauhut, Robert J. Kunsch
(Applied and Numerical Harmonic Analysis. ISSN:22965017)

版	1st ed. 2022.
出版者	(Cham : Springer International Publishing : Imprint: Birkhäuser)
出版年	2022
本文言語	英語
大きさ	XVII, 542 p. 116 illus., 90 illus. in color : online resource
著者標目	Kutyniok, Gitta editor Rauhut, Holger editor Kunsch, Robert J editor SpringerLink (Online service)
件　名	LCSH:Harmonic analysis LCSH:Mathematics -- Data processing  全ての件名で検索 LCSH:Signal processing LCSH:Image processing FREE:Abstract Harmonic Analysis FREE:Computational Mathematics and Numerical Analysis FREE:Digital and Analog Signal Processing FREE:Image Processing
一般注記	Hierarchical compressed sensing (G. Wunder) -- Proof Methods for Robust Low-Rank Matrix Recovery (T. Fuchs) -- New Challenges in Covariance Estimation: Multiple Structures and Coarse Quantization (J. Maly) -- Sparse Deterministic and Stochastic Channels: Identification of Spreading Functions and Covariances (Dae Gwan Lee) -- Analysis of Sparse Recovery Algorithms via the Replica Method (A. Bereyhi) -- Unbiasing in Iterative Reconstruction Algorithms for Discrete Compressed Sensing (F.H. Fischer) -- Recovery under Side Constraints (M. Pesavento) -- Compressive Sensing and Neural Networks from a Statistical Learning Perspective (E. Schnoor) -- Angular Scattering Function Estimation Using Deep Neural Networks (Y. Song) -- Fast Radio Propagation Prediction with Deep Learning (R. Levie) -- Active Channel Sparsification: Realizing Frequency Division Duplexing Massive MIMO with Minimal Overhead (M. B. Khalilsarai) -- Atmospheric Radar Imaging Improvements Using Compressed Sensing and MIMO (J. O. Aweda) -- Over-the-Air Computation for Distributed Machine Learning and Consensus in Large Wireless Networks (M. Frey) -- Information Theory and Recovery Algorithms for Data Fusion in Earth Observation (M. Fornasier) -- Sparse Recovery of Sound Fields Using Measurements from Moving Microphones (A. Mertins) -- Compressed Sensing in the Spherical Near-Field to Far-Field Transformation (C. Culotta-López) This contributed volume showcases the most significant results obtained from the DFG Priority Program on Compressed Sensing in Information Processing. Topics considered revolve around timely aspects of compressed sensing with a special focus on applications, including compressed sensing-like approaches to deep learning; bilinear compressed sensing - efficiency, structure, and robustness; structured compressive sensing via neural network learning; compressed sensing for massive MIMO; and security of future communication and compressive sensing HTTP:URL=https://doi.org/10.1007/978-3-031-09745-4

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