Compressed Sensing in Information Processing / / edited by Gitta Kutyniok, Holger Rauhut, Robert J. Kunsch

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Birkhäuser, , 2022
• Edizione: 1st ed. 2022.
• Descrizione fisica: 1 online resource (549 pages)
• Disciplina: 621.3678; 621.3822
• Soggetto topico: Harmonic analysis; Mathematics - Data processing; Signal processing; Image processing; Abstract Harmonic Analysis; Computational Mathematics and Numerical Analysis; Digital and Analog Signal Processing; Image Processing
• Persona (resp. second.): KutyniokGitta; RauhutHolger; KunschRobert J.
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: 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).
• Sommario/riassunto: 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.
• Titolo autorizzato: Compressed sensing in information processing
• ISBN: 3-031-09745-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910619276503321
• Lo trovi qui: Univ. Federico II
• Serie: Applied and Numerical Harmonic Analysis, . 2296-5017