Utilize este identificador para referenciar este registo:
https://hdl.handle.net/10216/85157Registo completo
| Campo DC | Valor | Idioma |
|---|---|---|
| dc.creator | André Luiz Pilastri | |
| dc.creator | João Manuel R. S. Tavares | |
| dc.date.accessioned | 2019-02-01T08:00:22Z | - |
| dc.date.available | 2019-02-01T08:00:22Z | - |
| dc.date.issued | 2016-02-03 | |
| dc.identifier.other | sigarra:139664 | |
| dc.identifier.uri | https://repositorio-aberto.up.pt/handle/10216/85157 | - |
| dc.description.abstract | The theory Compressive Sensing (CS) has provided a newacquisition strategy and recovery with good in the image processing area.This theory guarantees to recover a signal with high probability from areduced sampling rate below the Nyquist-Shannon limit. The problem ofrecovering the original signal from the samples consists in solving an optimizationproblem. This article presents an overview of reconstructionalgorithms for sparse signal recovery in CS, these algorithms may bebroadly divided into six types. We have provided a comprehensive surveyof the numerous reconstruction algorithms in CS aiming to achievecomputational efficiency | |
| dc.language.iso | eng | |
| dc.relation.ispartof | 11th edition of the Doctoral Symposium in Informatics Engineering (DSIE-16) | |
| dc.rights | openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | |
| dc.subject | Ciências Tecnológicas, Ciências da engenharia e tecnologias | |
| dc.subject | Technological sciences, Engineering and technology | |
| dc.title | Reconstruction Algorithms in Compressive Sensing: An Overview | |
| dc.type | Artigo em Livro de Atas de Conferência Nacional | |
| dc.contributor.uporto | Faculdade de Engenharia | |
| dc.subject.fos | Ciências da engenharia e tecnologias | |
| dc.subject.fos | Engineering and technology | |
| Aparece nas coleções: | FEUP - Artigo em Livro de Atas de Conferência Nacional | |
Ficheiros deste registo:
| Ficheiro | Descrição | Tamanho | Formato | |
|---|---|---|---|---|
| 139664.pdf | Paper | 342.33 kB | Adobe PDF |  Ver/Abrir |
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