Compressed Sensing & Sparse Filtering:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
2014
|
| Schriftenreihe: | Signals and Communication Technology
|
| Schlagworte: | |
| Online-Zugang: | BTU01 FHA01 FHI01 FHN01 FHR01 FKE01 FRO01 FWS01 FWS02 UBY01 Volltext Inhaltsverzeichnis Abstract |
| Beschreibung: | This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary. Apart from compressed sensing this book contains other related approaches. Each methodology has its own formalities for dealing with such problems. As an example, in the Bayesian approach, sparseness promoting priors such as Laplace and Cauchy are normally used for penalising improbable model variables, thus promoting low complexity solutions. Compressed sensing techniques and homotopy-type solutions, such as the LASSO, utilise l1-norm penalties for obtaining sparse solutions using fewer observations than conventionally needed. The book emphasizes on the role of sparsity as a machinery for promoting low complexity representations and likewise its connections to variable selection and dimensionality reduction in various engineering problems. This book is intended for researchers, academics and practitioners with interest in various aspects and applications of sparse signal processing. |
| Beschreibung: | 1 Online-Ressource (XII, 502 p.) 135 illus |
| ISBN: | 9783642383984 |
| DOI: | 10.1007/978-3-642-38398-4 |
Internformat
MARC
| LEADER | 00000nmm a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV041471026 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 131210s2014 |||| o||u| ||||||eng d | ||
| 020 | |a 9783642383984 |9 978-3-642-38398-4 | ||
| 024 | 7 | |a 10.1007/978-3-642-38398-4 |2 doi | |
| 035 | |a (OCoLC)862985548 | ||
| 035 | |a (DE-599)BVBBV041471026 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-Aug4 |a DE-92 |a DE-634 |a DE-859 |a DE-898 |a DE-573 |a DE-861 |a DE-706 |a DE-863 |a DE-862 | ||
| 082 | 0 | |a 621.382 |2 23 | |
| 100 | 1 | |a Carmi, Avishy Y. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Compressed Sensing & Sparse Filtering |c edited by Avishy Y. Carmi, Lyudmila Mihaylova, Simon J. Godsill |
| 264 | 1 | |c 2014 | |
| 300 | |a 1 Online-Ressource (XII, 502 p.) |b 135 illus | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Signals and Communication Technology | |
| 500 | |a This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary. Apart from compressed sensing this book contains other related approaches. Each methodology has its own formalities for dealing with such problems. As an example, in the Bayesian approach, sparseness promoting priors such as Laplace and Cauchy are normally used for penalising improbable model variables, thus promoting low complexity solutions. Compressed sensing techniques and homotopy-type solutions, such as the LASSO, utilise l1-norm penalties for obtaining sparse solutions using fewer observations than conventionally needed. The book emphasizes on the role of sparsity as a machinery for promoting low complexity representations and likewise its connections to variable selection and dimensionality reduction in various engineering problems. This book is intended for researchers, academics and practitioners with interest in various aspects and applications of sparse signal processing. | ||
| 505 | 0 | |a Introduction to Compressed Sensing and Sparse Filtering -- The Geometry of Compressed Sensing -- Sparse Signal Recovery with Exponential-Family Noise -- Nuclear Norm Optimization and its Application to Observation Model Specification -- Nonnegative Tensor Decomposition -- Sub-Nyquist Sampling and Compressed Sensing in Cognitive Radio Networks -- Sparse Nonlinear MIMO Filtering and Identification -- Optimization Viewpoint on Kalman Smoothing with Applications to Robust and Sparse Estimation -- Compressive System Identification -- Distributed Approximation and Tracking using Selective Gossip -- Recursive Reconstruction of Sparse Signal Sequences -- Estimation of Time-Varying Sparse Signals in Sensor Networks -- Sparsity and Compressed Sensing in Mono-static and Multi-static Radar Imaging -- Structured Sparse Bayesian Modelling for Audio Restoration -- Sparse Representations for Speech Recognition | |
| 650 | 4 | |a Engineering | |
| 650 | 4 | |a Electronic data processing | |
| 650 | 4 | |a Physics | |
| 650 | 4 | |a Signal, Image and Speech Processing | |
| 650 | 4 | |a Numeric Computing | |
| 650 | 4 | |a Mathematics of Algorithmic Complexity | |
| 650 | 4 | |a Complexity | |
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Ingenieurwissenschaften | |
| 650 | 0 | 7 | |a Komprimierte Abtastung |0 (DE-588)1036377245 |2 gnd |9 rswk-swf |
| 655 | 7 | |8 1\p |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
| 689 | 0 | 0 | |a Komprimierte Abtastung |0 (DE-588)1036377245 |D s |
| 689 | 0 | |8 2\p |5 DE-604 | |
| 700 | 1 | |a Mihaylova, Lyudmila |e Sonstige |4 oth | |
| 700 | 1 | |a Godsill, Simon J. |e Sonstige |4 oth | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-642-38398-4 |x Verlag |3 Volltext |
| 856 | 4 | 2 | |m Springer Fremddatenuebernahme |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026917168&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 856 | 4 | 2 | |m Springer Fremddatenuebernahme |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026917168&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |3 Abstract |
| 912 | |a ZDB-2-ENG | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-026917168 | ||
| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 883 | 1 | |8 2\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 966 | e | |u https://doi.org/10.1007/978-3-642-38398-4 |l BTU01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-642-38398-4 |l FHA01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-642-38398-4 |l FHI01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-642-38398-4 |l FHN01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-642-38398-4 |l FHR01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-642-38398-4 |l FKE01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-642-38398-4 |l FRO01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-642-38398-4 |l FWS01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-642-38398-4 |l FWS02 |p ZDB-2-ENG |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/978-3-642-38398-4 |l UBY01 |p ZDB-2-ENG |x Verlag |3 Volltext | |
Datensatz im Suchindex
| DE-BY-FWS_katkey | 1015409 |
|---|---|
| _version_ | 1806174832169058304 |
| adam_text | COMPRESSED SENSING & SPARSE FILTERING
/
: 2014
TABLE OF CONTENTS / INHALTSVERZEICHNIS
INTRODUCTION TO COMPRESSED SENSING AND SPARSE FILTERING
THE GEOMETRY OF COMPRESSED SENSING
SPARSE SIGNAL RECOVERY WITH EXPONENTIAL-FAMILY NOISE
NUCLEAR NORM OPTIMIZATION AND ITS APPLICATION TO OBSERVATION MODEL
SPECIFICATION
NONNEGATIVE TENSOR DECOMPOSITION
SUB-NYQUIST SAMPLING AND COMPRESSED SENSING IN COGNITIVE RADIO NETWORKS
SPARSE NONLINEAR MIMO FILTERING AND IDENTIFICATION
OPTIMIZATION VIEWPOINT ON KALMAN SMOOTHING WITH APPLICATIONS TO ROBUST
AND SPARSE ESTIMATION
COMPRESSIVE SYSTEM IDENTIFICATION
DISTRIBUTED APPROXIMATION AND TRACKING USING SELECTIVE GOSSIP
RECURSIVE RECONSTRUCTION OF SPARSE SIGNAL SEQUENCES
ESTIMATION OF TIME-VARYING SPARSE SIGNALS IN SENSOR NETWORKS
SPARSITY AND COMPRESSED SENSING IN MONO-STATIC AND MULTI-STATIC RADAR
IMAGING
STRUCTURED SPARSE BAYESIAN MODELLING FOR AUDIO RESTORATION
SPARSE REPRESENTATIONS FOR SPEECH RECOGNITION
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
COMPRESSED SENSING & SPARSE FILTERING
/
: 2014
ABSTRACT / INHALTSTEXT
THIS BOOK IS AIMED AT PRESENTING CONCEPTS, METHODS AND ALGORITHMS ABLETO
COPE WITH UNDERSAMPLED AND LIMITED DATA. ONE SUCH TREND THAT RECENTLY
GAINED POPULARITY AND TO SOME EXTENT REVOLUTIONISED SIGNAL PROCESSING IS
COMPRESSED SENSING. COMPRESSED SENSING BUILDS UPON THE OBSERVATION THAT
MANY SIGNALS IN NATURE ARE NEARLY SPARSE (OR COMPRESSIBLE, AS THEY ARE
NORMALLY REFERRED TO) IN SOME DOMAIN, AND CONSEQUENTLY THEY CAN BE
RECONSTRUCTED TO WITHIN HIGH ACCURACY FROM FAR FEWER OBSERVATIONS THAN
TRADITIONALLY HELD TO BE NECESSARY. APART FROM COMPRESSED SENSING THIS
BOOK CONTAINS OTHER RELATED APPROACHES. EACH METHODOLOGY HAS ITS OWN
FORMALITIES FOR DEALING WITH SUCH PROBLEMS. AS AN EXAMPLE, IN THE
BAYESIAN APPROACH, SPARSENESS PROMOTING PRIORS SUCH AS LAPLACE AND
CAUCHY ARE NORMALLY USED FOR PENALISING IMPROBABLE MODEL VARIABLES, THUS
PROMOTING LOW COMPLEXITY SOLUTIONS. COMPRESSED SENSING TECHNIQUES AND
HOMOTOPY-TYPE SOLUTIONS, SUCH AS THE LASSO, UTILISE L1-NORM PENALTIES
FOR OBTAINING SPARSE SOLUTIONS USING FEWER OBSERVATIONS THAN
CONVENTIONALLY NEEDED. THE BOOK EMPHASIZES ON THE ROLE OF SPARSITY AS A
MACHINERY FOR PROMOTING LOW COMPLEXITY REPRESENTATIONS AND LIKEWISE ITS
CONNECTIONS TO VARIABLE SELECTION AND DIMENSIONALITY REDUCTION IN
VARIOUS ENGINEERING PROBLEMS. THIS BOOK IS INTENDED FOR RESEARCHERS,
ACADEMICS AND PRACTITIONERS WITH INTEREST IN VARIOUS ASPECTS AND
APPLICATIONS OF SPARSE SIGNAL PROCESSING.
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
| any_adam_object | 1 |
| author | Carmi, Avishy Y. |
| author_facet | Carmi, Avishy Y. |
| author_role | aut |
| author_sort | Carmi, Avishy Y. |
| author_variant | a y c ay ayc |
| building | Verbundindex |
| bvnumber | BV041471026 |
| collection | ZDB-2-ENG |
| contents | Introduction to Compressed Sensing and Sparse Filtering -- The Geometry of Compressed Sensing -- Sparse Signal Recovery with Exponential-Family Noise -- Nuclear Norm Optimization and its Application to Observation Model Specification -- Nonnegative Tensor Decomposition -- Sub-Nyquist Sampling and Compressed Sensing in Cognitive Radio Networks -- Sparse Nonlinear MIMO Filtering and Identification -- Optimization Viewpoint on Kalman Smoothing with Applications to Robust and Sparse Estimation -- Compressive System Identification -- Distributed Approximation and Tracking using Selective Gossip -- Recursive Reconstruction of Sparse Signal Sequences -- Estimation of Time-Varying Sparse Signals in Sensor Networks -- Sparsity and Compressed Sensing in Mono-static and Multi-static Radar Imaging -- Structured Sparse Bayesian Modelling for Audio Restoration -- Sparse Representations for Speech Recognition |
| ctrlnum | (OCoLC)862985548 (DE-599)BVBBV041471026 |
| dewey-full | 621.382 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621.382 |
| dewey-search | 621.382 |
| dewey-sort | 3621.382 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik |
| doi_str_mv | 10.1007/978-3-642-38398-4 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>05731nmm a2200685zc 4500</leader><controlfield tag="001">BV041471026</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">131210s2014 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783642383984</subfield><subfield code="9">978-3-642-38398-4</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-3-642-38398-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)862985548</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV041471026</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-Aug4</subfield><subfield code="a">DE-92</subfield><subfield code="a">DE-634</subfield><subfield code="a">DE-859</subfield><subfield code="a">DE-898</subfield><subfield code="a">DE-573</subfield><subfield code="a">DE-861</subfield><subfield code="a">DE-706</subfield><subfield code="a">DE-863</subfield><subfield code="a">DE-862</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">621.382</subfield><subfield code="2">23</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Carmi, Avishy Y.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Compressed Sensing & Sparse Filtering</subfield><subfield code="c">edited by Avishy Y. Carmi, Lyudmila Mihaylova, Simon J. Godsill</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (XII, 502 p.)</subfield><subfield code="b">135 illus</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Signals and Communication Technology</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary. Apart from compressed sensing this book contains other related approaches. Each methodology has its own formalities for dealing with such problems. As an example, in the Bayesian approach, sparseness promoting priors such as Laplace and Cauchy are normally used for penalising improbable model variables, thus promoting low complexity solutions. Compressed sensing techniques and homotopy-type solutions, such as the LASSO, utilise l1-norm penalties for obtaining sparse solutions using fewer observations than conventionally needed. The book emphasizes on the role of sparsity as a machinery for promoting low complexity representations and likewise its connections to variable selection and dimensionality reduction in various engineering problems. This book is intended for researchers, academics and practitioners with interest in various aspects and applications of sparse signal processing. </subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Introduction to Compressed Sensing and Sparse Filtering -- The Geometry of Compressed Sensing -- Sparse Signal Recovery with Exponential-Family Noise -- Nuclear Norm Optimization and its Application to Observation Model Specification -- Nonnegative Tensor Decomposition -- Sub-Nyquist Sampling and Compressed Sensing in Cognitive Radio Networks -- Sparse Nonlinear MIMO Filtering and Identification -- Optimization Viewpoint on Kalman Smoothing with Applications to Robust and Sparse Estimation -- Compressive System Identification -- Distributed Approximation and Tracking using Selective Gossip -- Recursive Reconstruction of Sparse Signal Sequences -- Estimation of Time-Varying Sparse Signals in Sensor Networks -- Sparsity and Compressed Sensing in Mono-static and Multi-static Radar Imaging -- Structured Sparse Bayesian Modelling for Audio Restoration -- Sparse Representations for Speech Recognition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Engineering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Electronic data processing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Physics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Signal, Image and Speech Processing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Numeric Computing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematics of Algorithmic Complexity</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Complexity</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Datenverarbeitung</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ingenieurwissenschaften</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Komprimierte Abtastung</subfield><subfield code="0">(DE-588)1036377245</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="8">1\p</subfield><subfield code="0">(DE-588)4143413-4</subfield><subfield code="a">Aufsatzsammlung</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Komprimierte Abtastung</subfield><subfield code="0">(DE-588)1036377245</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="8">2\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mihaylova, Lyudmila</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Godsill, Simon J.</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/978-3-642-38398-4</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Springer Fremddatenuebernahme</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026917168&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Springer Fremddatenuebernahme</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026917168&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Abstract</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-ENG</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-026917168</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">2\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-642-38398-4</subfield><subfield code="l">BTU01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-642-38398-4</subfield><subfield code="l">FHA01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-642-38398-4</subfield><subfield code="l">FHI01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-642-38398-4</subfield><subfield code="l">FHN01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-642-38398-4</subfield><subfield code="l">FHR01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-642-38398-4</subfield><subfield code="l">FKE01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-642-38398-4</subfield><subfield code="l">FRO01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-642-38398-4</subfield><subfield code="l">FWS01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-642-38398-4</subfield><subfield code="l">FWS02</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-3-642-38398-4</subfield><subfield code="l">UBY01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| genre | 1\p (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV041471026 |
| illustrated | Not Illustrated |
| indexdate | 2024-08-01T10:55:48Z |
| institution | BVB |
| isbn | 9783642383984 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026917168 |
| oclc_num | 862985548 |
| open_access_boolean | |
| owner | DE-Aug4 DE-92 DE-634 DE-859 DE-898 DE-BY-UBR DE-573 DE-861 DE-706 DE-863 DE-BY-FWS DE-862 DE-BY-FWS |
| owner_facet | DE-Aug4 DE-92 DE-634 DE-859 DE-898 DE-BY-UBR DE-573 DE-861 DE-706 DE-863 DE-BY-FWS DE-862 DE-BY-FWS |
| physical | 1 Online-Ressource (XII, 502 p.) 135 illus |
| psigel | ZDB-2-ENG |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| record_format | marc |
| series2 | Signals and Communication Technology |
| spellingShingle | Carmi, Avishy Y. Compressed Sensing & Sparse Filtering Introduction to Compressed Sensing and Sparse Filtering -- The Geometry of Compressed Sensing -- Sparse Signal Recovery with Exponential-Family Noise -- Nuclear Norm Optimization and its Application to Observation Model Specification -- Nonnegative Tensor Decomposition -- Sub-Nyquist Sampling and Compressed Sensing in Cognitive Radio Networks -- Sparse Nonlinear MIMO Filtering and Identification -- Optimization Viewpoint on Kalman Smoothing with Applications to Robust and Sparse Estimation -- Compressive System Identification -- Distributed Approximation and Tracking using Selective Gossip -- Recursive Reconstruction of Sparse Signal Sequences -- Estimation of Time-Varying Sparse Signals in Sensor Networks -- Sparsity and Compressed Sensing in Mono-static and Multi-static Radar Imaging -- Structured Sparse Bayesian Modelling for Audio Restoration -- Sparse Representations for Speech Recognition Engineering Electronic data processing Physics Signal, Image and Speech Processing Numeric Computing Mathematics of Algorithmic Complexity Complexity Datenverarbeitung Ingenieurwissenschaften Komprimierte Abtastung (DE-588)1036377245 gnd |
| subject_GND | (DE-588)1036377245 (DE-588)4143413-4 |
| title | Compressed Sensing & Sparse Filtering |
| title_auth | Compressed Sensing & Sparse Filtering |
| title_exact_search | Compressed Sensing & Sparse Filtering |
| title_full | Compressed Sensing & Sparse Filtering edited by Avishy Y. Carmi, Lyudmila Mihaylova, Simon J. Godsill |
| title_fullStr | Compressed Sensing & Sparse Filtering edited by Avishy Y. Carmi, Lyudmila Mihaylova, Simon J. Godsill |
| title_full_unstemmed | Compressed Sensing & Sparse Filtering edited by Avishy Y. Carmi, Lyudmila Mihaylova, Simon J. Godsill |
| title_short | Compressed Sensing & Sparse Filtering |
| title_sort | compressed sensing sparse filtering |
| topic | Engineering Electronic data processing Physics Signal, Image and Speech Processing Numeric Computing Mathematics of Algorithmic Complexity Complexity Datenverarbeitung Ingenieurwissenschaften Komprimierte Abtastung (DE-588)1036377245 gnd |
| topic_facet | Engineering Electronic data processing Physics Signal, Image and Speech Processing Numeric Computing Mathematics of Algorithmic Complexity Complexity Datenverarbeitung Ingenieurwissenschaften Komprimierte Abtastung Aufsatzsammlung |
| url | https://doi.org/10.1007/978-3-642-38398-4 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026917168&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026917168&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT carmiavishyy compressedsensingsparsefiltering AT mihaylovalyudmila compressedsensingsparsefiltering AT godsillsimonj compressedsensingsparsefiltering |