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

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
Contributor
Editor
Subject
Genre
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
Member of
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
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-16042-9
Instantiates
Publication
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: Least-Squares Meets Compressed Sensing -- The Quest for Optimal Sampling: Computationally Efficient, Structure-exploiting 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 Square-Root 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
10.1007/978-3-319-16042-9
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
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-16042-9
Publication
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: Least-Squares Meets Compressed Sensing -- The Quest for Optimal Sampling: Computationally Efficient, Structure-exploiting 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 Square-Root 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
10.1007/978-3-319-16042-9
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

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