Coverart for item
The Resource Compressed Sensing and its Applications : MATHEON Workshop 2013, edited by Holger Boche, Robert Calderbank, Gitta Kutyniok, Jan Vybíral, (electronic resource)

Compressed Sensing and its Applications : MATHEON Workshop 2013, edited by Holger Boche, Robert Calderbank, Gitta Kutyniok, Jan Vybíral, (electronic resource)

Label
Compressed Sensing and its Applications : MATHEON Workshop 2013
Title
Compressed Sensing and its Applications
Title remainder
MATHEON Workshop 2013
Statement of responsibility
edited by Holger Boche, Robert Calderbank, Gitta Kutyniok, Jan Vybíral
Contributor
Editor
Subject
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 Applicationsy 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
Image bit depth
0
Literary form
non fiction
http://library.link/vocab/relatedWorkOrContributorName
  • Boche, Holger.
  • Calderbank, Robert.
  • Kutyniok, Gitta.
  • Vybíral, Jan.
  • SpringerLink (Online service)
Series statement
Applied and Numerical Harmonic Analysis,
http://library.link/vocab/subjectName
  • Mathematics
  • Coding theory
  • Matrix theory
  • Algebra
  • Information theory
  • Computer mathematics
  • Numerical analysis
Label
Compressed Sensing and its Applications : MATHEON Workshop 2013, edited by Holger Boche, Robert Calderbank, Gitta Kutyniok, Jan Vybíral, (electronic resource)
Link
http://ezproxy.eui.eu/login?url=http://dx.doi.org/10.1007/978-3-319-16042-9
Instantiates
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier.
Color
not applicable
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
Control code
978-3-319-16042-9
Dimensions
unknown
Extent
XII, 472 p. 105 illus., 70 illus. in color.
File format
multiple file formats
Form of item
electronic
Governing access note
Use of this electronic resource may be governed by a license agreement which restricts use to the European University Institute community. Each user is responsible for limiting use to individual, non-commercial purposes, without systematically downloading, distributing, or retaining substantial portions of information, provided that all copyright and other proprietary notices contained on the materials are retained. The use of software, including scripts, agents, or robots, is generally prohibited and may result in the loss of access to these resources for the entire European University Institute community
Isbn
9783319160429
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia.
Media type code
c
Other control number
10.1007/978-3-319-16042-9
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(OCoLC)1022041747
Label
Compressed Sensing and its Applications : MATHEON Workshop 2013, edited by Holger Boche, Robert Calderbank, Gitta Kutyniok, Jan Vybíral, (electronic resource)
Link
http://ezproxy.eui.eu/login?url=http://dx.doi.org/10.1007/978-3-319-16042-9
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier.
Color
not applicable
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
Control code
978-3-319-16042-9
Dimensions
unknown
Extent
XII, 472 p. 105 illus., 70 illus. in color.
File format
multiple file formats
Form of item
electronic
Governing access note
Use of this electronic resource may be governed by a license agreement which restricts use to the European University Institute community. Each user is responsible for limiting use to individual, non-commercial purposes, without systematically downloading, distributing, or retaining substantial portions of information, provided that all copyright and other proprietary notices contained on the materials are retained. The use of software, including scripts, agents, or robots, is generally prohibited and may result in the loss of access to these resources for the entire European University Institute community
Isbn
9783319160429
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia.
Media type code
c
Other control number
10.1007/978-3-319-16042-9
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(OCoLC)1022041747

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