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Compressed sensing in information pr...

Kutyniok, Gitta.

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Compressed sensing in information processing

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Compressed sensing in information processing/ edited by Gitta Kutyniok, Holger Rauhut, Robert J. Kunsch.
其他作者:	Kutyniok, Gitta.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xvii, 542 p. :ill. (some col.), digital ;24 cm.
內容註:	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
標題:	Compressed sensing (Telecommunication) -
電子資源:	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

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