A mathematical introduction to compressive sensing
At the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. Based on the premise that data acquisition and compression can be performed simultaneously, compressive sensing finds applications in imaging, signal processing, and many other domai...
Wedi'i Gadw mewn:
| Prif Awduron: | , |
|---|---|
| Fformat: | Livre numérique |
| Iaith: | Anglais |
| Cyhoeddwyd: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| Rhifyn: | 1st ed. 2013. |
| Cyfres: | Applied and Numerical Harmonic Analysis
|
| Pynciau: | |
| Mynediad Ar-lein: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nodyn: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • A Mathematical Introduction to Compressive Sensing, Texte imprimé, 9780817649500 • A mathematical introduction to compressive sensing, Simon Foucart, Holger Rauhut, 2013, New York, Birkhäuser, Springer, 1 vol. (XVIII-625 p.), Applied and numerical harmonic analysis, 978-0-8176-4947-0 • A mathematical introduction to compressive sensing, Simon Foucart, Holger Rauhut, 2013, New York, Birkhäuser, Springer, 1 vol. (XVIII-625 p.), Applied and numerical harmonic analysis, 978-0-8176-4947-0 |
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| 100 | 1 | |a Foucart, Simon. | |
| 245 | 1 | 0 | |a A mathematical introduction to compressive sensing |c by Simon Foucart, Holger Rauhut. |
| 250 | |a 1st ed. 2013. | ||
| 260 | |a New York, NY : |b Springer New York. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Applied and Numerical Harmonic Analysis |x 2296-5017 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a 1 An invitation to compressive sensing 2 Sparse solutions of underdetermined systems 3 Basic algorithms 4 Basis pursuit 5 Coherence 6 restricted isometry property 7 Basic tools from probability theory 8 Advanced tools from probability theory 9 Sparse recovery with random matrices 10 Gelfand widths of l1-balls 11 Instance optimality and quotient property 12 Random sampling in bounded orthonormal systems 13 Lossless expanders in compressive sensing 14 Recovery of random signals using deterministic matrices 15 Algorithms for l1-minimization Appendix A Matrix analysis Appendix B Convex analysis Appendix C Miscellanea | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a At the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. Based on the premise that data acquisition and compression can be performed simultaneously, compressive sensing finds applications in imaging, signal processing, and many other domains. In the areas of applied mathematics, electrical engineering, and theoretical computer science, an explosion of research activity has already followed the theoretical results that highlighted the efficiency of the basic principles. The elegant ideas behind these principles are also of independent interest to pure mathematicians. A Mathematical Introduction to Compressive Sensing gives a detailed account of the core theory upon which the field is build. Key features include: · The first textbook completely devoted to the topic of compressive sensing · Comprehensive treatment of the subject, including background material from probability theory, detailed proofs of the main theorems, and an outline of possible applications · Numerous exercises designed to help students understand the material · An extensive bibliography with over 500 references that guide researchers through the literature With only moderate prerequisites, A Mathematical Introduction to Compressive Sensing is an excellent textbook for graduate courses in mathematics, engineering, and computer science. It also serves as a reliable resource for practitioners and researchers in these disciplines who want to acquire a careful understanding of the subject | ||
| 650 | |a Traitement du signal |x Techniques numériques | ||
| 650 | |a Algorithmes optimaux | ||
| 650 | |a Échantillonnage (statistique) | ||
| 650 | |a Analyse informatique | ||
| 650 | |a Informatique |x Mathématiques | ||
| 650 | |a Données |x Compression (télécommunications) | ||
| 700 | 1 | |a Rauhut, Holger. |4 aut | |
| 776 | 0 | |t A Mathematical Introduction to Compressive Sensing |b Texte imprimé |z 9780817649500 | |
| 776 | 0 | |0 172681324 |t A mathematical introduction to compressive sensing |f Simon Foucart, Holger Rauhut |d 2013 |c New York |n Birkhäuser |n Springer |p 1 vol. (XVIII-625 p.) |s Applied and numerical harmonic analysis |z 978-0-8176-4947-0 | |
| 776 | 0 | |0 172681324 |t A mathematical introduction to compressive sensing |f Simon Foucart, Holger Rauhut |d 2013 |c New York |n Birkhäuser |n Springer |p 1 vol. (XVIII-625 p.) |s Applied and numerical harmonic analysis |z 978-0-8176-4947-0 | |
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