Compressive Sensing And Fast Simulation
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Handbook of Mathematical Methods in Imaging
Author | : Otmar Scherzer |
Publisher | : Springer Science & Business Media |
Total Pages | : 1626 |
Release | : 2010-11-23 |
Genre | : Mathematics |
ISBN | : 0387929193 |
The Handbook of Mathematical Methods in Imaging provides a comprehensive treatment of the mathematical techniques used in imaging science. The material is grouped into two central themes, namely, Inverse Problems (Algorithmic Reconstruction) and Signal and Image Processing. Each section within the themes covers applications (modeling), mathematics, numerical methods (using a case example) and open questions. Written by experts in the area, the presentation is mathematically rigorous. The entries are cross-referenced for easy navigation through connected topics. Available in both print and electronic forms, the handbook is enhanced by more than 150 illustrations and an extended bibliography. It will benefit students, scientists and researchers in applied mathematics. Engineers and computer scientists working in imaging will also find this handbook useful.
A Mathematical Introduction to Compressive Sensing
Author | : Simon Foucart |
Publisher | : Springer Science & Business Media |
Total Pages | : 634 |
Release | : 2013-08-13 |
Genre | : Computers |
ISBN | : 0817649484 |
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. With only moderate prerequisites, it 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. A Mathematical Introduction to Compressive Sensing uses a mathematical perspective to present the core of the theory underlying compressive sensing.
Compressed Sensing in Radar Signal Processing
Author | : Antonio De Maio |
Publisher | : Cambridge University Press |
Total Pages | : 381 |
Release | : 2019-10-17 |
Genre | : Computers |
ISBN | : 1108428290 |
Learn about the latest theoretical and practical advances in radar signal processing using tools from compressive sensing.
An Introduction to Compressed Sensing
Author | : M. Vidyasagar |
Publisher | : SIAM |
Total Pages | : 341 |
Release | : 2019-12-03 |
Genre | : Technology & Engineering |
ISBN | : 161197612X |
Compressed sensing is a relatively recent area of research that refers to the recovery of high-dimensional but low-complexity objects from a limited number of measurements. The topic has applications to signal/image processing and computer algorithms, and it draws from a variety of mathematical techniques such as graph theory, probability theory, linear algebra, and optimization. The author presents significant concepts never before discussed as well as new advances in the theory, providing an in-depth initiation to the field of compressed sensing. An Introduction to Compressed Sensing contains substantial material on graph theory and the design of binary measurement matrices, which is missing in recent texts despite being poised to play a key role in the future of compressed sensing theory. It also covers several new developments in the field and is the only book to thoroughly study the problem of matrix recovery. The book supplies relevant results alongside their proofs in a compact and streamlined presentation that is easy to navigate. The core audience for this book is engineers, computer scientists, and statisticians who are interested in compressed sensing. Professionals working in image processing, speech processing, or seismic signal processing will also find the book of interest.
Computational Science – ICCS 2022
Author | : Derek Groen |
Publisher | : Springer Nature |
Total Pages | : 778 |
Release | : 2022-06-21 |
Genre | : Computers |
ISBN | : 3031087607 |
The four-volume set LNCS 13350, 13351, 13352, and 13353 constitutes the proceedings of the 22ndt International Conference on Computational Science, ICCS 2022, held in London, UK, in June 2022.* The total of 175 full papers and 78 short papers presented in this book set were carefully reviewed and selected from 474 submissions. 169 full and 36 short papers were accepted to the main track; 120 full and 42 short papers were accepted to the workshops/ thematic tracks. *The conference was held in a hybrid format
Compressed Sensing in Information Processing
Author | : Gitta Kutyniok |
Publisher | : Springer Nature |
Total Pages | : 549 |
Release | : 2022-10-20 |
Genre | : Mathematics |
ISBN | : 3031097459 |
This contributed volume showcases the most significant results obtained from the DFG Priority Program on Compressed Sensing in Information Processing. Topics considered revolve around timely aspects of compressed sensing with a special focus on applications, including compressed sensing-like approaches to deep learning; bilinear compressed sensing - efficiency, structure, and robustness; structured compressive sensing via neural network learning; compressed sensing for massive MIMO; and security of future communication and compressive sensing.
Compressed Sensing and its Applications
Author | : Holger Boche |
Publisher | : Birkhäuser |
Total Pages | : 402 |
Release | : 2018-01-17 |
Genre | : Mathematics |
ISBN | : 3319698028 |
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. Article authors address their techniques for solving the problems of compressed sensing, as well as connections to related areas like detecting community-like structures in graphs, curbatures on Grassmanians, and randomized tensor train singular value decompositions. Some of the novel applications covered include dimensionality reduction, information theory, random matrices, sparse approximation, and sparse recovery. This book is aimed at both graduate students and researchers in the areas of applied mathematics, computer science, and engineering, as well as other applied scientists exploring the potential applications for the novel methodology of compressed sensing. An introduction to the subject of compressed sensing is also provided for researchers interested in the field who are not as familiar with it.
Compressive Sensing for Wireless Networks
Author | : Zhu Han |
Publisher | : Cambridge University Press |
Total Pages | : 308 |
Release | : 2013-06-06 |
Genre | : Computers |
ISBN | : 1107018838 |
This comprehensive reference delivers the understanding and skills needed to take advantage of compressive sensing in wireless networks.
An Introduction to Semi-tensor Product of Matrices and Its Applications
Author | : Dai-Zhan Cheng |
Publisher | : World Scientific |
Total Pages | : 610 |
Release | : 2012 |
Genre | : Mathematics |
ISBN | : 9814374695 |
A generalization of Conventional Matrix Product (CMP), called the Semi-Tensor Product (STP), is proposed. It extends the CMP to two arbitrary matrices and maintains all fundamental properties of CMP. In addition, it has a pseudo-commutative property, which makes it more superior to CMP. The STP was proposed by the authors to deal with higher-dimensional data as well as multilinear mappings. After over a decade of development, STP has been proven to be a powerful tool in dealing with nonlinear and logical calculations.This book is a comprehensive introduction to the theory of STP and its various applications, including logical function, fuzzy control, Boolean networks, analysis and control of nonlinear systems, amongst others.