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Compressed sensing : theory and applications /
"Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject, highlighting recent theoretical advances and a range o...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge ; New York :
Cambridge University Press,
2012.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Compressed sensing : |b theory and applications / |c edited by Yonina C. Eldar, Gitta Kutyniok. |
| 260 | |a Cambridge ; |a New York : |b Cambridge University Press, |c 2012. | ||
| 300 | |a 1 online resource | ||
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| 520 | |a "Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject, highlighting recent theoretical advances and a range of applications, as well as outlining numerous remaining research challenges. After a thorough review of the basic theory, many cutting-edge techniques are presented, including advanced signal modeling, sub-Nyquist sampling of analog signals, non-asymptotic analysis of random matrices, adaptive sensing, greedy algorithms and use of graphical models. All chapters are written by leading researchers in the field, and consistent style and notation are utilized throughout. Key background information and clear definitions make this an ideal resource for researchers, graduate students and practitioners wanting to join this exciting research area. It can also serve as a supplementary textbook for courses on computer vision, coding theory, signal processing, image processing and algorithms for efficient data processing"-- |c Provided by publisher | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |6 880-01 |a 1. Introduction to compressed sensing / Mark A. Davenport, Marco F. Duarte, Yonina C. Eldar, and Gitta Kutyniok -- 2. Second-generation sparse modeling: structured and collaborative signal analysis / Alexey Castrodad, Ignacio Ramirez, Guillermo Sapiro, Pablo Sprechmann, and Guoshen Yu -- 3. Xampling: compressed sensing of analog signals / Moshe Mishali and Yonina C. Eldar -- 4. Sampling at the rate of innovation: theory and applications / Jose Antonia Urigüen, Yonina C. Eldar, Pier Luigi Dragotta, and Zvika Ben-Haim -- 5. Introduction to the non-asymptotic analysis of random matrices / Roman Vershynin -- 6. Adaptive sensing for sparse recovery / Jarvis Haupt and Robert Nowak -- 7. Fundamental thresholds in compressed sensing: a high-dimensional geometry approach / Weiyu Xu and Babak Hassibi -- 8. Greedy algorithms for compressed sensing / Thomas Blumensath, Michael E. Davies, and Gabriel Rilling -- 9. Graphical models concepts in compressed sensing / Andrea Montanari -- 10. Finding needles in compressed haystacks / Robert Calderbank and Sina Jafarpour -- 11. Data separation by sparse representations / Gitta Kutyniok -- 12. Face recognition by sparse representation / Arvind Ganesh, Andrew Wagner, Zihan Zhou, Allen Y. Yang, Yi Ma, and John Wright. | |
| 546 | |a English. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Signal processing. | |
| 650 | 0 | |a Wavelets (Mathematics) | |
| 650 | 4 | |a Signal processing. | |
| 650 | 4 | |a Wavelets (Mathematics) | |
| 650 | 6 | |a Traitement du signal. | |
| 650 | 6 | |a Ondelettes. | |
| 650 | 7 | |a COMPUTERS |x Information Theory. |2 bisacsh | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Signals & Signal Processing. |2 bisacsh | |
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| 650 | 7 | |a Informationstheorie |2 gnd | |
| 650 | 7 | |a Signaltheorie |2 gnd | |
| 700 | 1 | |a Eldar, Yonina C. | |
| 700 | 1 | |a Kutyniok, Gitta. | |
| 776 | 0 | 8 | |i Print version: |t Compressed sensing. |d Cambridge ; New York : Cambridge University Press, 2012 |z 9781107005587 |w (DLC) 2011040519 |w (OCoLC)756578482 |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=438963 |z Texto completo |
| 880 | 8 | |6 505-01/(S |a 6.2.1.1 Single component generative model -- 6.2.1.2 Measurement adaptation -- 6.2.2 Bayesian inference using multi-component models -- 6.2.2.1 Multi-component generative model -- 6.2.2.2 Measurement adaptation -- 6.2.3 Quantifying performance -- 6.3 Quasi-Bayesian adaptive sensing -- 6.3.1 Denoising using non-adaptive measurements -- 6.3.2 Distilled sensing -- 6.3.2.1 Analysis of distilled sensing -- 6.3.3 Distillation in compressed sensing -- 6.4 Related work and suggestions for further reading -- References -- 7: Fundamental thresholds in compressed sensing: a high-dimensional geometry approach -- 7.1 Introduction -- 7.1.1 Threshold bounds for L1 minimization robustness -- 7.1.2 Weighted and iterative reweighted L1 minimization thresholds -- 7.1.3 Comparisons with other threshold bounds -- 7.1.4 Some concepts in high-dimensional geometry -- 7.1.5 Organization -- 7.2 The null space characterization -- 7.3 The Grassmann angle framework for the null space characterization -- 7.4 Evaluating the threshold bound ζ -- 7.5 Computing the internal angle exponent -- 7.6 Computing the external angle exponent -- 7.7 Existence and scaling of ρN(δ, C) -- 7.8 ``Weak, '' ``sectional, '' and ``strong'' robustness -- 7.9 Numerical computations on the bounds of ζ -- 7.10 Recovery thresholds for weighted L1 minimization -- 7.11 Approximate support recovery and iterative reweighted L1 -- 7.12 Conclusion -- 7.13 Appendix -- 7.13.1 Derivation of the internal angles -- 7.13.2 Derivation of the external angles -- 7.13.3 Proof of Lemma 7.7 -- 7.13.4 Proof of Lemma 7.8 -- Acknowledgement -- References -- 8: Greedy algorithms for compressed sensing -- 8.1 Greed, a flexible alternative to convexification -- 8.2 Greedy pursuits -- 8.2.1 General framework -- 8.2.2 Variations in coefficient updates -- 8.2.3 Variations in element selection -- 8.2.4 Computational considerations. | |
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