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010   $a3-031-09745-9
024 7 $a10.1007/978-3-031-09745-4
035   $a(MiAaPQ)EBC7120434
035   $a(Au-PeEL)EBL7120434
035   $a(CKB)25181489100041
035   $a(PPN)26585654X
035   $a(OCoLC)1535978992
035   $a(DE-He213)978-3-031-09745-4
035   $a(EXLCZ)9925181489100041
100   $a20221020d2022      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aCompressed Sensing in Information Processing /$fedited by Gitta Kutyniok, Holger Rauhut, Robert J. Kunsch
205   $a1st ed. 2022.
210  1$aCham :$cSpringer International Publishing :$cImprint: Birkhäuser,$d2022.
215   $a1 online resource (549 pages)
225 1 $aApplied and Numerical Harmonic Analysis,$x2296-5017
311 08$aPrint version: Kutyniok, Gitta Compressed Sensing in Information Processing Cham : Springer International Publishing AG,c2022 9783031097447 
320   $aIncludes bibliographical references.
327   $aHierarchical 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).
330   $aThis contributed volume showcases the most significant results obtained from the DFG Priority Program on Compressed Sensing in Information Processing. Topics considered revolve around timely aspects of compressed sensing with a special focus on applications, including compressed sensing-like approaches to deep learning; bilinear compressed sensing - efficiency, structure, and robustness; structured compressive sensing via neural network learning; compressed sensing for massive MIMO; and security of future communication and compressive sensing.
410  0$aApplied and Numerical Harmonic Analysis,$x2296-5017
606   $aHarmonic analysis
606   $aMathematics$xData processing
606   $aSignal processing
606   $aImage processing
606   $aAbstract Harmonic Analysis
606   $aComputational Mathematics and Numerical Analysis
606   $aDigital and Analog Signal Processing
606   $aImage Processing
615  0$aHarmonic analysis.
615  0$aMathematics$xData processing.
615  0$aSignal processing.
615  0$aImage processing.
615 14$aAbstract Harmonic Analysis.
615 24$aComputational Mathematics and Numerical Analysis.
615 24$aDigital and Analog Signal Processing.
615 24$aImage Processing.
676   $a621.3678
676   $a621.3822
702   $aKutyniok$b Gitta
702   $aRauhut$b Holger
702   $aKunsch$b Robert J.
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910619276503321
996   $aCompressed sensing in information processing$93041518
997   $aUNINA