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The Resource Compressed sensing and its applications : MATHEON Workshop 2013, Holger Boche, Robert Calderbank, Gitta Kutyniok, Jan Vybíral, editors
Compressed sensing and its applications : MATHEON Workshop 2013, Holger Boche, Robert Calderbank, Gitta Kutyniok, Jan Vybíral, editors
Resource Information
The item Compressed sensing and its applications : MATHEON Workshop 2013, Holger Boche, Robert Calderbank, Gitta Kutyniok, Jan Vybíral, editors represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Oklahoma Libraries.This item is available to borrow from all library branches.
Resource Information
The item Compressed sensing and its applications : MATHEON Workshop 2013, Holger Boche, Robert Calderbank, Gitta Kutyniok, Jan Vybíral, editors represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Oklahoma Libraries.
This item is available to borrow from all library branches.
 Summary
 Since publication of the initial papers in 2006, compressed sensing has captured the imagination of the international signal processing community, and the mathematical foundations are nowadays quite well understood. Parallel to the progress in mathematics, the potential applications of compressed sensing have been explored by many international groups of, in particular, engineers and applied mathematicians, achieving very promising advances in various areas such as communication theory, imaging sciences, optics, radar technology, sensor networks, or tomography. Since many applications have reached a mature state, the research center MATHEON in Berlin focusing on "Mathematics for Key Technologies", invited leading researchers on applications of compressed sensing from mathematics, computer science, and engineering to the "MATHEON Workshop 2013: Compressed Sensing and its Applications3 in December 2013. It was the first workshop specifically focusing on the applications of compressed sensing. This book features contributions by the plenary and invited speakers of this workshop. To make this book accessible for those unfamiliar with compressed sensing, the book will not only contain chapters on various applications of compressed sensing written by plenary and invited speakers, but will also provide a general introduction into compressed sensing. The 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 interested in the potential and applications of the novel methodology of compressed sensing. For those readers who are not already familiar with compressed sensing, an introduction to the basics of this theory will be included
 Language
 eng
 Extent
 1 online resource (xv, 468 pages)
 Contents

 Survey on Compressed Sensing
 Temporal compressive sensing for video
 Compressed Sensing, Sparse Inversion, and Model Mismatch
 Recovering Structured Signals in Noise: LeastSquares Meets Compressed Sensing
 The Quest for Optimal Sampling: Computationally Efficient, Structureexploiting Measurements for Compressed Sensing
 Compressive Sensing in Acoustic Imaging
 Quantization and Compressive Sensing
 Compressive Gaussian Mixture Estimation
 Two Algorithms for Compressed Sensing of Sparse Tensors
 Sparse Model Uncertainties in Compressed Sensing with Application to Convolutions and Sporadic Communication
 Cosparsity in Compressed Sensing
 Structured Sparsity: Discrete and Convex Approaches
 Explicit Matrices with the Restricted Isometry Property: Breaking the SquareRoot Bottleneck
 Tensor Completion in Hierarchical Tensor Representations
 Compressive Classification: Where Wireless Communications Meets Machine Learning
 Isbn
 9783319160429
 Label
 Compressed sensing and its applications : MATHEON Workshop 2013
 Title
 Compressed sensing and its applications
 Title remainder
 MATHEON Workshop 2013
 Statement of responsibility
 Holger Boche, Robert Calderbank, Gitta Kutyniok, Jan Vybíral, editors
 Title variation
 MATHEON Workshop 2013
 Subject

 Compressed sensing (Telecommunication)  Congresses
 Maths for scientists
 Conference papers and proceedings
 Conference papers and proceedings
 Electronic books
 Signal processing  Mathematics
 Compressed sensing (Telecommunication)
 TECHNOLOGY & ENGINEERING  Mechanical
 Algebra
 Coding theory & cryptology
 Signal processing  Mathematics  Congresses
 Numerical analysis
 Imaging systems & technology
 Applied mathematics
 Language
 eng
 Summary
 Since publication of the initial papers in 2006, compressed sensing has captured the imagination of the international signal processing community, and the mathematical foundations are nowadays quite well understood. Parallel to the progress in mathematics, the potential applications of compressed sensing have been explored by many international groups of, in particular, engineers and applied mathematicians, achieving very promising advances in various areas such as communication theory, imaging sciences, optics, radar technology, sensor networks, or tomography. Since many applications have reached a mature state, the research center MATHEON in Berlin focusing on "Mathematics for Key Technologies", invited leading researchers on applications of compressed sensing from mathematics, computer science, and engineering to the "MATHEON Workshop 2013: Compressed Sensing and its Applications3 in December 2013. It was the first workshop specifically focusing on the applications of compressed sensing. This book features contributions by the plenary and invited speakers of this workshop. To make this book accessible for those unfamiliar with compressed sensing, the book will not only contain chapters on various applications of compressed sensing written by plenary and invited speakers, but will also provide a general introduction into compressed sensing. The 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 interested in the potential and applications of the novel methodology of compressed sensing. For those readers who are not already familiar with compressed sensing, an introduction to the basics of this theory will be included
 Cataloging source
 GW5XE
 Dewey number
 621.382/2
 Illustrations
 illustrations
 Index
 no index present
 LC call number
 TK5102.5
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 http://library.link/vocab/relatedWorkOrContributorDate
 1954
 http://library.link/vocab/relatedWorkOrContributorName

 Boche, Holger
 Calderbank, Robert
 Kutyniok, Gitta
 Vybíral, Jan
 Series statement
 Applied and numerical harmonic analysis,
 http://library.link/vocab/subjectName

 Compressed sensing (Telecommunication)
 Signal processing
 TECHNOLOGY & ENGINEERING
 Compressed sensing (Telecommunication)
 Signal processing
 Algebra
 Applied mathematics
 Coding theory & cryptology
 Imaging systems & technology
 Maths for scientists
 Numerical analysis
 Label
 Compressed sensing and its applications : MATHEON Workshop 2013, Holger Boche, Robert Calderbank, Gitta Kutyniok, Jan Vybíral, editors
 Antecedent source
 unknown
 Bibliography note
 Includes bibliographical references at the end of each chapters
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents
 Survey on Compressed Sensing  Temporal compressive sensing for video  Compressed Sensing, Sparse Inversion, and Model Mismatch  Recovering Structured Signals in Noise: LeastSquares Meets Compressed Sensing  The Quest for Optimal Sampling: Computationally Efficient, Structureexploiting Measurements for Compressed Sensing  Compressive Sensing in Acoustic Imaging  Quantization and Compressive Sensing  Compressive Gaussian Mixture Estimation  Two Algorithms for Compressed Sensing of Sparse Tensors  Sparse Model Uncertainties in Compressed Sensing with Application to Convolutions and Sporadic Communication  Cosparsity in Compressed Sensing  Structured Sparsity: Discrete and Convex Approaches  Explicit Matrices with the Restricted Isometry Property: Breaking the SquareRoot Bottleneck  Tensor Completion in Hierarchical Tensor Representations  Compressive Classification: Where Wireless Communications Meets Machine Learning
 Dimensions
 unknown
 Extent
 1 online resource (xv, 468 pages)
 File format
 unknown
 Form of item
 online
 Isbn
 9783319160429
 Level of compression
 unknown
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Note
 SpringerLink
 Other control number
 10.1007/9783319160429
 Other physical details
 color illustrations.
 Quality assurance targets
 not applicable
 Reformatting quality
 unknown
 Sound
 unknown sound
 Specific material designation
 remote
 System control number

 (OCoLC)913573202
 (OCoLC)ocn913573202
 Label
 Compressed sensing and its applications : MATHEON Workshop 2013, Holger Boche, Robert Calderbank, Gitta Kutyniok, Jan Vybíral, editors
 Antecedent source
 unknown
 Bibliography note
 Includes bibliographical references at the end of each chapters
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents
 Survey on Compressed Sensing  Temporal compressive sensing for video  Compressed Sensing, Sparse Inversion, and Model Mismatch  Recovering Structured Signals in Noise: LeastSquares Meets Compressed Sensing  The Quest for Optimal Sampling: Computationally Efficient, Structureexploiting Measurements for Compressed Sensing  Compressive Sensing in Acoustic Imaging  Quantization and Compressive Sensing  Compressive Gaussian Mixture Estimation  Two Algorithms for Compressed Sensing of Sparse Tensors  Sparse Model Uncertainties in Compressed Sensing with Application to Convolutions and Sporadic Communication  Cosparsity in Compressed Sensing  Structured Sparsity: Discrete and Convex Approaches  Explicit Matrices with the Restricted Isometry Property: Breaking the SquareRoot Bottleneck  Tensor Completion in Hierarchical Tensor Representations  Compressive Classification: Where Wireless Communications Meets Machine Learning
 Dimensions
 unknown
 Extent
 1 online resource (xv, 468 pages)
 File format
 unknown
 Form of item
 online
 Isbn
 9783319160429
 Level of compression
 unknown
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Note
 SpringerLink
 Other control number
 10.1007/9783319160429
 Other physical details
 color illustrations.
 Quality assurance targets
 not applicable
 Reformatting quality
 unknown
 Sound
 unknown sound
 Specific material designation
 remote
 System control number

 (OCoLC)913573202
 (OCoLC)ocn913573202
Subject
 Algebra
 Applied mathematics
 Coding theory & cryptology
 Compressed sensing (Telecommunication)
 Compressed sensing (Telecommunication)  Congresses
 Conference papers and proceedings
 Conference papers and proceedings
 Electronic books
 Imaging systems & technology
 Maths for scientists
 Numerical analysis
 Signal processing  Mathematics
 Signal processing  Mathematics  Congresses
 TECHNOLOGY & ENGINEERING  Mechanical
Genre
Member of
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History of Science CollectionsBorrow it401 W. Brooks St., Rm. 521NW, Norman, OK, 73019, US35.207487 97.447906

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