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Catalogue Information
Field name	Details
Dewey Class	515.785
Title	Compressed Sensing in Information Processing ( EBook/) / edited by Gitta Kutyniok, Holger Rauhut, Robert J. Kunsch.
Added Personal Name	Kutyniok, Gitta
	Rauhut, Holger
	Kunsch, Robert J.
Other name(s)	SpringerLink (Online service)
Edition statement	1st ed. 2022.
Publication	Cham : : Springer International Publishing : : Imprint: Birkhäuser, , 2022.
Physical Details	XVII, 542 p. 116 illus., 90 illus. in color. : online resource.
Series	Applied and numerical harmonic analysis 2296-5017
ISBN	9783031097454
Summary Note	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.:
Contents note	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).
Mode of acces to digital resource	Digital reproduction.-
	Cham :
	Springer International Publishing,
	2022. -
	Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format.
System details note	Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
Internet Site	https://doi.org/10.1007/978-3-031-09745-4
Links to Related Works	Subject References: Abstract Harmonic Analysis . Computational Mathematics and Numerical Analysis . Digital and Analog Signal Processing . Harmonic analysis . Image processing . Mathematics—Data processing . Signal processing . Authors: editor . Kunsch, Robert J. . Kutyniok, Gitta . Rauhut, Holger . Corporate Authors: SpringerLink (Online service) . Series: Applied and numerical harmonic analysis . Classification: 515.785 .

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Compressed Sensing in Information Processing

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