Sampling Theory - Eldar, Yonina C.

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Résumé :
Covering the fundamental mathematical underpinnings together with key principles and applications, this book provides a comprehensive guide to the theory and practice of sampling from an engineering perspective. Beginning with traditional ideas such as uniform sampling in shift-invariant spaces and working through to the more recent fields of compressed sensing and sub-Nyquist sampling, the key concepts are addressed in a unified and coherent way. Emphasis is given to applications in signal processing and communications, as well as hardware considerations, throughout. With 200 worked examples and over 200 end-of-chapter problems, this is an ideal course textbook for senior undergraduate and graduate students. It is also an invaluable reference or self-study guide for engineers and students across industry and academia.

Biographie:
Yonina C. Eldar is a Professor in the Department of Electrical Engineering at the Technion ? Israel Institute of Technology (holding the Edwards Chair in Engineering), a Research Affiliate with the Research Laboratory of Electronics at the Massachusetts Institute of Technology and a Visiting Professor at Stanford University. She has received numerous awards for excellence in research and teaching, including the Wolf Foundation Krill Prize for Excellence in Scientific Research, the Hershel Rich Innovation Award, the Michael Bruno Memorial Award from the Rothschild Foundation, the Weismann Prize for Exact Sciences, and the Muriel and David Jacknow Award for Excellence in Teaching. She is the Editor in Chief of Foundations and Trends in Signal Processing and an Associate Editor for several journals in the areas of signal processing and mathematics. She is a Signal Processing Distinguished Lecturer, an IEEE Fellow, a member of the Young Israel Academy of Science and the Israel Committee for Higher Education.

Sommaire:
1. Introduction; 2. Introduction to linear algebra; 3. Fourier analysis; 4. Signal spaces; 5. Shift-invariant spaces; 6. Subspace priors; 7. Smoothness priors; 8. Nonlinear sampling; 9. Resampling; 10. Unions of subspaces; 11. Compressed sensing; 12. Sampling over finite unions; 13. Sampling over shift-invariant unions; 14. Multiband sampling; 15. Finite rate of innovation sampling; Appendix A. Finite linear algebra; Appendix B. Stochastic signals.