A Survey of Compressed Sensing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F15%3A10317821" target="_blank" >RIV/00216208:11320/15:10317821 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-16042-9_1" target="_blank" >http://dx.doi.org/10.1007/978-3-319-16042-9_1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-16042-9_1" target="_blank" >10.1007/978-3-319-16042-9_1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Survey of Compressed Sensing

Popis výsledku v původním jazyce

Compressed sensing was introduced some ten years ago as an effective way of acquiring signals, which possess a sparse or nearly sparse representation in a suitable basis or dictionary. Due to its solid mathematical backgrounds, it quickly attracted the attention of mathematicians from several different areas, so that the most important aspects of the theory are nowadays very well understood. In recent years, its applications started to spread out through applied mathematics, signal processing, and electrical engineering. The aim of this chapter is to provide an introduction into the basic concepts of compressed sensing. In the first part of this chapter, we present the basic mathematical concepts of compressed sensing, including the Null Space Property, Restricted Isometry Property, their connection to basis pursuit and sparse recovery, and construction of matrices with small restricted isometry constants. This presentation is easily accessible, largely self-contained, and includes pro

Název v anglickém jazyce

A Survey of Compressed Sensing

Popis výsledku anglicky

Compressed sensing was introduced some ten years ago as an effective way of acquiring signals, which possess a sparse or nearly sparse representation in a suitable basis or dictionary. Due to its solid mathematical backgrounds, it quickly attracted the attention of mathematicians from several different areas, so that the most important aspects of the theory are nowadays very well understood. In recent years, its applications started to spread out through applied mathematics, signal processing, and electrical engineering. The aim of this chapter is to provide an introduction into the basic concepts of compressed sensing. In the first part of this chapter, we present the basic mathematical concepts of compressed sensing, including the Null Space Property, Restricted Isometry Property, their connection to basis pursuit and sparse recovery, and construction of matrices with small restricted isometry constants. This presentation is easily accessible, largely self-contained, and includes pro

Druh

D - Stať ve sborníku

CEP obor

BA - Obecná matematika

OECD FORD obor

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Projekt

<a href="/cs/project/LL1203" target="_blank" >LL1203: Vlastnosti funkcí a zobrazení v Sobolevových prostorech</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Compressed Sensing and its Applications

ISBN

978-3-319-16041-2

ISSN

2296-5009

e-ISSN

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Počet stran výsledku

39

Strana od-do

1-39

Název nakladatele

Birkhäuser

Místo vydání

Basel

Místo konání akce

Berlin, Německo

Datum konání akce

9. 12. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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