Compressed Sensing

Yonina C. Eldar is a Professor in the Department of Electrical Engineering at the Technion, Israel Institute of Technology, 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 Weizmann Prize for Exact Sciences, the Michael Bruno Memorial Award from the Rothschild Foundation, and the Muriel and David Jacknow Award for Excellence in Teaching. She is an Associate Editor for several journals in the areas of signal processing and mathematics and a Signal Processing Society Distinguished Lecturer. Gitta Kutyniok is an Einstein Professor in the Department of Mathematics at the Technische Universitat Berlin, Germany. She has been a Postdoctoral Fellow at Princeton, Stanford, and Yale Universities, and a Full Professor at the Universitat Osnabruck, Germany. Her research and teaching has been recognized by various awards, including a Heisenberg Fellowship and the von Kaven Prize by the German Research Foundation, an Einstein Chair by the Einstein Foundation in Berlin, awards by the Universitat Paderborn and the Justus-Liebig Universitat Giessen for Excellence Research, as well as the Weierstrass Prize for Outstanding Teaching. She is an Associate Editor and also Corresponding Editor for several journals in the areas of applied mathematics.

A detailed presentation of compressed sensing by leading researchers, covering the most significant theoretical and application-oriented advances.

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 Uriguen, 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, Sina Jafarpour and Jeremy Kent; 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.