H. Boche, G. Caire, R. Calderbank, G. Kutyniok, R. Mathar and Ph. Petersen published a further volume from the series "Compressed Sensing and Its Application" in August 2019 on the occasion of the 3rd International MATHEON Conference 2017.
This volume contains contributions from the plenary and invited speakers from the third International MATHEON Workshop 2017.
Compressed sensing and many research activities associated with it can be seen as a framework for signal processing of low-complexity structures. A cornerstone of the underlying theory is the study of inverse problems with linear or nonlinear measurements. Whether it is sparsity, low-rankness, or other familiar notions of low complexity, the theory addresses necessary and sufficient conditions behind the measurement process to guarantee signal reconstruction with efficient algorithms. This includes consideration of robustness to measurement noise and stability with respect to signal model inaccuracies.
This introduction aims to provide an overall view of some of the most important results in this direction. After discussing various examples of low-complexity signal models, two approaches to linear inverse problems are introduced which, respectively, focus on the recovery of individual signals and recovery of all low-complexity signals simultaneously. In particular, we focus on the former setting, giving rise to so-called nonuniform signal recovery problems. We discuss different necessary and sufficient conditions for stable and robust signal reconstruction using convex optimization methods. Appealing to concepts from non-asymptotic random matrix theory, we outline how certain classes of random sensing matrices, which fully govern the measurement process, satisfy certain sufficient conditions for signal recovery. Finally, we review some of the most prominent algorithms for signal recovery proposed in the literature.
Exploring many of the novel applications of compressed sensing developed since the previous MATHEON Workshop in 2013, H. Boche, G. Caire, R. Calderbank, M. März, G. Kutyniok, and R. Mathar have published the book, Compressed Sensing and its Application: Second International MATHEON Conference 2015. This contributed volume contains articles written by the plenary and invited speakers from the second international MATHEON Workshop 2015 that focus on applications of compressed sensing.
Information Theoretic Security and Privacy of Information Systems
Hoger Boche, Ashish Khisti (Department of Electrical and Computer Engineering, University of Toronto), H. Vincent Poor (School of Engineering and Applied Science, Princeton University) and Rafael F. Schaefer (School of Engineering and Applied Science, Princeton University) are editors of a forthcoming book on “Information Theoretic Approaches to Security & Privacy” which will be published in 2016 by Cambridge University Press.
Following the 1st MATHEON Workshop on Compressed Sensing and its Applications in 2013, H. Boche, R. Calderbank, G. Kutyniok and J. Vybiral have published this book of topical research articles.
In the introduction to the book, the editors discuss important developments in compressed sensing.
Presenting state-of-the-art research into methods of wireless spectrum allocation based on game theory and mechanism design, this innovative and comprehensive book provides a strong foundation for the design of future wireless mechanisms and spectrum markets. Prominent researchers showcase a diverse range of novel insights and approaches to the increasing demand for limited spectrum resources, with a consistent emphasis on theoretical methods, analytical results and practical examples. [more]