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1. |
Record Nr. |
UNISA990001040650203316 |
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Titolo |
Ludus vitalis : Revista de filosofia de las ciencias de la vida = journal of philosophy of life sciences = revue de philosophie des sciences de la vie |
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Pubbl/distr/stampa |
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Sant Cugat del Vallès : Editorial Anthropos, 1993- |
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ISSN |
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Descrizione fisica |
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Disciplina |
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Soggetti |
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Scienze biologiche -- Periodici |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Periodico |
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2. |
Record Nr. |
UNINA9910619276503321 |
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Titolo |
Compressed Sensing in Information Processing / / edited by Gitta Kutyniok, Holger Rauhut, Robert J. Kunsch |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Birkhäuser, , 2022 |
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ISBN |
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Edizione |
[1st ed. 2022.] |
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Descrizione fisica |
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1 online resource (549 pages) |
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Collana |
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Applied and Numerical Harmonic Analysis, , 2296-5017 |
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Disciplina |
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Soggetti |
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Harmonic analysis |
Mathematics - Data processing |
Signal processing |
Image processing |
Abstract Harmonic Analysis |
Computational Mathematics and Numerical Analysis |
Digital and Analog Signal Processing |
Image Processing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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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). |
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Sommario/riassunto |
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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. |
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