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Compressed sensing in information processing

Record Type:	Electronic resources : Monograph/item
Title/Author:	Compressed sensing in information processingedited by Gitta Kutyniok, Holger Rauhut, Robert J. Kunsch.
other author:	Kutyniok, Gitta.
Published:	Cham :Springer International Publishing :2022.
Description:	xvii, 542 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Compressed sensing (Telecommunication)
Online resource:	https://doi.org/10.1007/978-3-031-09745-4
ISBN:	9783031097454$q(electronic bk.)

Compressed sensing in information processing
Compressed sensing in information processing[electronic resource] /edited by Gitta Kutyniok, Holger Rauhut, Robert J. Kunsch. - Cham :Springer International Publishing :2022. - xvii, 542 p. :ill. (some col.), digital ;24 cm. - Applied and numerical harmonic analysis,2296-5017. - Applied and numerical harmonic analysis..

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.

ISBN: 9783031097454$q(electronic bk.)

Standard No.: 10.1007/978-3-031-09745-4doiSubjects--Topical Terms:

769143
Compressed sensing (Telecommunication)

LC Class. No.: TA1638 / .C65 2022

Dewey Class. No.: 621.3678

Compressed sensing in information processing
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300 $a xvii, 542 p. : $b ill. (some col.), digital ; $c 24 cm.
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505 0 $a 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)
520 $a 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.
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650 1 4 $a Abstract Harmonic Analysis. $3 274074
650 2 4 $a Computational Mathematics and Numerical Analysis. $3 274020
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650 2 4 $a Image Processing. $3 273705
700 1 $a Kutyniok, Gitta. $3 561316
700 1 $a Rauhut, Holger. $3 937315
700 1 $a Kunsch, Robert J. $3 937316
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