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Titlebook: Compressed Sensing in Information Processing; Gitta Kutyniok,Holger Rauhut,Robert J. Kunsch Book 2022 The Editor(s) (if applicable) and Th

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發(fā)表于 2025-3-21 17:12:39 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Compressed Sensing in Information Processing
編輯Gitta Kutyniok,Holger Rauhut,Robert J. Kunsch
視頻videohttp://file.papertrans.cn/232/231982/231982.mp4
概述Chapters written by leading researchers in compressed sensing.Explores recent developments of compressed sensing, both in theory and practice.With appeal to a broad audience with research areas
叢書名稱Applied and Numerical Harmonic Analysis
圖書封面Titlebook: Compressed Sensing in Information Processing;  Gitta Kutyniok,Holger Rauhut,Robert J. Kunsch Book 2022 The Editor(s) (if applicable) and Th
描述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.
出版日期Book 2022
關(guān)鍵詞Compressed Sensing; Signal Processing; Random Matrix Theory; Sparsity; Information Processing
版次1
doihttps://doi.org/10.1007/978-3-031-09745-4
isbn_softcover978-3-031-09747-8
isbn_ebook978-3-031-09745-4Series ISSN 2296-5009 Series E-ISSN 2296-5017
issn_series 2296-5009
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 23:58:32 | 只看該作者
Strategisches Vertriebsmanagementtion, and phase retrieval. Over the last two decades, a number of works have rigorously analyzed the reconstruction performance for such scenarios, giving rise to a rather general understanding of the potential and the limitations of low-rank matrix models in sensing problems. In this chapter, we co
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Methoden der Produktentwicklung,We first show that the asymptotic analysis of a sparse recovery algorithm is mathematically equivalent to the problem of calculating the free energy of a . in the thermodynamic limit. We then use the . from statistical mechanics to evaluate the performance in the asymptotic regime. The asymptotic re
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Evidenz für strategisches Koalitionsw?hlene constraints may result from prior information on the measurement system and the sparse signal structure. They may involve the structure of the sensing matrix, the structure of the non-zero support values, the temporal structure of the sparse representation vector, and the nonlinear measurement str
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Strategisches W?hlen in Deutschlandhe present setup, we consider a scenario in which the .-array base station (BS) receives a number of noisy pilot signals from a single-antenna transmitter and has to estimate the ASF with high resolution using the pilot signals. In general, the ASF is a non-negative function that may consist not onl
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Strategisches und soziales Nischenmanagementa point . (transmitter location) to any point . on a planar domain. For applications such as user-cell site association and device-to-device link scheduling, an accurate knowledge of the pathloss function for all pairs of transmitter–receiver locations is very important. In realistic propagation env
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