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Title: Compressed Sensing in Information Processing ( EBook/) / edited by Gitta Kutyniok, Holger Rauhut, Robert J. Kunsch. Dewey Class: 515.785 Edition statement: 1st ed. 2022. Added Personal Name: Kutyniok, Gitta. editor. Rauhut, Holger. editor. Kunsch, Robert J. editor. Publication: Cham : : Springer International Publishing : : Imprint: Birkhäuser,, 2022. Other name(s): SpringerLink (Online service) Physical Details: XVII, 542 p. 116 illus., 90 illus. in color. : online resource. Series: Applied and Numerical Harmonic Analysis,2296-5017 ISBN: 9783031097454 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). 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). ------------------------------ *** There are no holdings for this record *** -----------------------------------------------
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