東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Compressed sensing in information pr...

Kutyniok, Gitta.

Linked to FindBook

Google Book

Amazon

博客來

Compressed sensing in information processing

Record Type:	Electronic resources : Monograph/item
Title/Author:	Compressed sensing in information processing/ edited 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.
[NT 15003449]:	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)
Contained By:	Springer Nature eBook
Subject:	Compressed sensing (Telecommunication) -
Online resource:	https://doi.org/10.1007/978-3-031-09745-4
ISBN:	9783031097454

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

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

3214582
Compressed sensing (Telecommunication)

LC Class. No.: TA1638 / .C65 2022

Dewey Class. No.: 621.3678

Compressed sensing in information processing
LDR:03055nmm a2200337 a 4500 001 2305069
003 DE-He213
005 20221020213644.0
006 m d
007 cr nn 008maaau
008 230409s2022 sz s 0 eng d
020 $a 9783031097454 $q (electronic bk.)
020 $a 9783031097447 $q (paper)
024 7 $a 10.1007/978-3-031-09745-4 $2 doi
035 $a 978-3-031-09745-4
040 $a GP $c GP
041 0 $a eng
050 4 $a TA1638 $b .C65 2022
072 7 $a PBKD $2 bicssc
072 7 $a MAT034000 $2 bisacsh
072 7 $a PBKD $2 thema
082 0 4 $a 621.3678 $2 23
090 $a TA1638 $b .C737 2022
245 0 0 $a Compressed sensing in information processing $h [electronic resource] / $c edited by Gitta Kutyniok, Holger Rauhut, Robert J. Kunsch.
260 $a Cham : $b Springer International Publishing : $b Imprint: Birkhäuser, $c 2022.
300 $a xvii, 542 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Applied and numerical harmonic analysis, $x 2296-5017
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.
650 0 $a Compressed sensing (Telecommunication) $3 3214582
650 1 4 $a Abstract Harmonic Analysis. $3 891093
650 2 4 $a Computational Mathematics and Numerical Analysis. $3 891040
650 2 4 $a Digital and Analog Signal Processing. $3 3538815
650 2 4 $a Image Processing. $3 891209
700 1 $a Kutyniok, Gitta. $3 836514
700 1 $a Rauhut, Holger. $3 3607833
700 1 $a Kunsch, Robert J. $3 3607834
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Applied and numerical harmonic analysis. $3 625188
856 4 0 $u https://doi.org/10.1007/978-3-031-09745-4
950 $a Mathematics and Statistics (SpringerNature-11649)