| Description |
1 online resource (xvii, 542 pages) : illustrations (some color) |
| Series |
Applied and numerical harmonic analysis |
|
Applied and numerical harmonic analysis.
|
| Contents |
Hierarchical compressed sensing / Jens Eisert, Axel Flinth, Benedikt Groß, Ingo Roth, and Gerhard Wunder -- Proof methods for robust low-rank matrix recovery / Tim Fuchs, David Gross, Peter Jung, Felix Krahmer, Richard Kueng, and Dominik Stöger -- New challenges in covariance estimation : multiple structures and coarse quantization / Johannes Maly, Tianyu Yang, Sjoerd Dirksen, Holger Rauhut, and Giuseppe Caire -- Sparse deterministic and stochastic channels : identification of spreading functions and covariances / Alihan Kaplan, Dae Gwan Lee, Götz E. Pfander, and Volker Pohl -- Analysis of sparse recovery algorithms via the replica method / Ali Bereyhi, Ralf R. Müller, and Hermann Schulz-Baldes -- Unbiasing in iterative reconstruction algorithms for discrete compressed sensing / Robert F. H. Fischer and Carmen Sippel -- Recovery under side constraints / Khaled Ardah, Martin Haardt, Tianyi Liu, Frederic Matter, Marius Pesavento, and Marc E. Pfetsch -- Compressive sensing and neural networks from a statistical learning perspective / Arash Behboodi, Holger Rauhut, and Ekkehard Schooner -- Angular scattering function estimation using deep neural networks / Yi Song and Giuseppe Caire -- Fast radio propagation prediction with deep learning / Ron Levie, C̦aǧkan Yapar, Giuseppe Caire, and Gitta Kutyniok -- Active channel sparsification : realizing frequency-division duplexing massive MIMO with minimal overhead / Mahdi Barzegar Khalilsarai, Saeid Haghighatshoar, Xinping Yi, Giuseppe Caire, and Gerhard Wunder -- Atmospheric radar imaging improvements using compressed sensing and MIMO / Jorge Luis Chau, Juan Miguel Urco, Tobias Weber, and Jeremy Olaore Aweda -- Over-the-aiir computation for distributed machine learning and consensus in large wireless networks / Matthias Frey, Igor Bjelakovič, and Slawomir Stańczak -- Information theory and recovery algorithms for data fusion in earth observation / Massimo Fornasier, Danfeng Hong, Gerhard Kramer, Lars Palzer, Michael Rauchensteiner, and Xiao Xiang Zhu -- Sparse recovery of sound fields using measurements from moving microphones / Fabrice Katzberg and Alfred Mertins -- Compressed sensing in the spherical near-field to far-field transformation / Cosme Culotta-López, Arya Bangun, Rudolf Mathar, and Dirk Heberling |
| Summary |
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 |
| Bibliography |
Includes bibliographical references |
| Notes |
Print version record |
| Subject |
Compressed sensing (Telecommunication)
|
|
Compresión de datos (Informática)
|
|
Compressed sensing (Telecommunication)
|
| Form |
Electronic book
|
| Author |
Kutyniok, Gitta, editor
|
|
Rauhut, Holger, editor
|
|
Kunsch, Robert J., editor
|
| ISBN |
9783031097454 |
|
3031097459 |
|
