A survey on the low-dimensional-model-based electromagnetic imaging
- Autores
- Li, Lianlin; Hurtado, Martin; Xu, Feng; Zhang, Bing Chen; Jin, Tian; Cui, Tie Jun; Stevanovic, Marija Nikolic; Nehorai, Arye
- Año de publicación
- 2018
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- The low-dimensional-model-based electromagnetic imaging is an emerging member of the big family of computational imaging, by which the low-dimensional models of underlying signals are incorporated into both data acquisition systems and reconstruction algorithms for electromagnetic imaging, in order to improve the imaging performance and break the bottleneck of existing electromagnetic imaging methodologies. Over the past decade, we have witnessed profound impacts of the low-dimensional models on electromagnetic imaging. However, the low-dimensional-model-based electromagnetic imaging remains at its early stage, and many important issues relevant to practical applications need to be carefully investigated. Especially, we are in the big-data era of booming electromagnetic sensing, by which massive data are being collected for retrieving very detailed information of probed objects. This survey gives a comprehensive overview on the low-dimensional models of structure signals, along with its relevant theories and low-complexity algorithms of signal recovery. Afterwards, we review the recent advancements of low-dimensional-model-based electromagnetic imaging in various applied areas. We hope this survey could bridge the gap between the model-based signal processing and the electromagnetic imaging, advance the development of low-dimensional-model-based electromagnetic imaging, and serve as a basic reference in the future research of the electromagnetic imaging across various frequency ranges.
Fil: Li, Lianlin. Peking University; China
Fil: Hurtado, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Xu, Feng. Fudan University; China
Fil: Zhang, Bing Chen. Chinese Academy of Sciences; República de China
Fil: Jin, Tian. National University of defense Technology; China
Fil: Cui, Tie Jun. Southeast University; Bangladesh
Fil: Stevanovic, Marija Nikolic. University of Belgrade; Serbia
Fil: Nehorai, Arye. University of Washington; Estados Unidos - Materia
-
Electromagnetic imaging
Compressive sensing
Inverse scattering
Radar imaging - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/93765
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A survey on the low-dimensional-model-based electromagnetic imagingLi, LianlinHurtado, MartinXu, FengZhang, Bing ChenJin, TianCui, Tie JunStevanovic, Marija NikolicNehorai, AryeElectromagnetic imagingCompressive sensingInverse scatteringRadar imaginghttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2The low-dimensional-model-based electromagnetic imaging is an emerging member of the big family of computational imaging, by which the low-dimensional models of underlying signals are incorporated into both data acquisition systems and reconstruction algorithms for electromagnetic imaging, in order to improve the imaging performance and break the bottleneck of existing electromagnetic imaging methodologies. Over the past decade, we have witnessed profound impacts of the low-dimensional models on electromagnetic imaging. However, the low-dimensional-model-based electromagnetic imaging remains at its early stage, and many important issues relevant to practical applications need to be carefully investigated. Especially, we are in the big-data era of booming electromagnetic sensing, by which massive data are being collected for retrieving very detailed information of probed objects. This survey gives a comprehensive overview on the low-dimensional models of structure signals, along with its relevant theories and low-complexity algorithms of signal recovery. Afterwards, we review the recent advancements of low-dimensional-model-based electromagnetic imaging in various applied areas. We hope this survey could bridge the gap between the model-based signal processing and the electromagnetic imaging, advance the development of low-dimensional-model-based electromagnetic imaging, and serve as a basic reference in the future research of the electromagnetic imaging across various frequency ranges.Fil: Li, Lianlin. Peking University; ChinaFil: Hurtado, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Xu, Feng. Fudan University; ChinaFil: Zhang, Bing Chen. Chinese Academy of Sciences; República de ChinaFil: Jin, Tian. National University of defense Technology; ChinaFil: Cui, Tie Jun. Southeast University; BangladeshFil: Stevanovic, Marija Nikolic. University of Belgrade; SerbiaFil: Nehorai, Arye. University of Washington; Estados UnidosNow Publishers2018-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/93765Li, Lianlin; Hurtado, Martin; Xu, Feng; Zhang, Bing Chen; Jin, Tian; et al.; A survey on the low-dimensional-model-based electromagnetic imaging; Now Publishers; Foundations and Trends in Signal Processing; 12; 2; 6-2018; 107-1991932-8354CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.nowpublishers.com/article/Details/SIG-103info:eu-repo/semantics/altIdentifier/doi/10.1561/2000000103info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:51:06Zoai:ri.conicet.gov.ar:11336/93765instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:51:06.348CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
A survey on the low-dimensional-model-based electromagnetic imaging |
title |
A survey on the low-dimensional-model-based electromagnetic imaging |
spellingShingle |
A survey on the low-dimensional-model-based electromagnetic imaging Li, Lianlin Electromagnetic imaging Compressive sensing Inverse scattering Radar imaging |
title_short |
A survey on the low-dimensional-model-based electromagnetic imaging |
title_full |
A survey on the low-dimensional-model-based electromagnetic imaging |
title_fullStr |
A survey on the low-dimensional-model-based electromagnetic imaging |
title_full_unstemmed |
A survey on the low-dimensional-model-based electromagnetic imaging |
title_sort |
A survey on the low-dimensional-model-based electromagnetic imaging |
dc.creator.none.fl_str_mv |
Li, Lianlin Hurtado, Martin Xu, Feng Zhang, Bing Chen Jin, Tian Cui, Tie Jun Stevanovic, Marija Nikolic Nehorai, Arye |
author |
Li, Lianlin |
author_facet |
Li, Lianlin Hurtado, Martin Xu, Feng Zhang, Bing Chen Jin, Tian Cui, Tie Jun Stevanovic, Marija Nikolic Nehorai, Arye |
author_role |
author |
author2 |
Hurtado, Martin Xu, Feng Zhang, Bing Chen Jin, Tian Cui, Tie Jun Stevanovic, Marija Nikolic Nehorai, Arye |
author2_role |
author author author author author author author |
dc.subject.none.fl_str_mv |
Electromagnetic imaging Compressive sensing Inverse scattering Radar imaging |
topic |
Electromagnetic imaging Compressive sensing Inverse scattering Radar imaging |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/2.2 https://purl.org/becyt/ford/2 |
dc.description.none.fl_txt_mv |
The low-dimensional-model-based electromagnetic imaging is an emerging member of the big family of computational imaging, by which the low-dimensional models of underlying signals are incorporated into both data acquisition systems and reconstruction algorithms for electromagnetic imaging, in order to improve the imaging performance and break the bottleneck of existing electromagnetic imaging methodologies. Over the past decade, we have witnessed profound impacts of the low-dimensional models on electromagnetic imaging. However, the low-dimensional-model-based electromagnetic imaging remains at its early stage, and many important issues relevant to practical applications need to be carefully investigated. Especially, we are in the big-data era of booming electromagnetic sensing, by which massive data are being collected for retrieving very detailed information of probed objects. This survey gives a comprehensive overview on the low-dimensional models of structure signals, along with its relevant theories and low-complexity algorithms of signal recovery. Afterwards, we review the recent advancements of low-dimensional-model-based electromagnetic imaging in various applied areas. We hope this survey could bridge the gap between the model-based signal processing and the electromagnetic imaging, advance the development of low-dimensional-model-based electromagnetic imaging, and serve as a basic reference in the future research of the electromagnetic imaging across various frequency ranges. Fil: Li, Lianlin. Peking University; China Fil: Hurtado, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina Fil: Xu, Feng. Fudan University; China Fil: Zhang, Bing Chen. Chinese Academy of Sciences; República de China Fil: Jin, Tian. National University of defense Technology; China Fil: Cui, Tie Jun. Southeast University; Bangladesh Fil: Stevanovic, Marija Nikolic. University of Belgrade; Serbia Fil: Nehorai, Arye. University of Washington; Estados Unidos |
description |
The low-dimensional-model-based electromagnetic imaging is an emerging member of the big family of computational imaging, by which the low-dimensional models of underlying signals are incorporated into both data acquisition systems and reconstruction algorithms for electromagnetic imaging, in order to improve the imaging performance and break the bottleneck of existing electromagnetic imaging methodologies. Over the past decade, we have witnessed profound impacts of the low-dimensional models on electromagnetic imaging. However, the low-dimensional-model-based electromagnetic imaging remains at its early stage, and many important issues relevant to practical applications need to be carefully investigated. Especially, we are in the big-data era of booming electromagnetic sensing, by which massive data are being collected for retrieving very detailed information of probed objects. This survey gives a comprehensive overview on the low-dimensional models of structure signals, along with its relevant theories and low-complexity algorithms of signal recovery. Afterwards, we review the recent advancements of low-dimensional-model-based electromagnetic imaging in various applied areas. We hope this survey could bridge the gap between the model-based signal processing and the electromagnetic imaging, advance the development of low-dimensional-model-based electromagnetic imaging, and serve as a basic reference in the future research of the electromagnetic imaging across various frequency ranges. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-06 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/93765 Li, Lianlin; Hurtado, Martin; Xu, Feng; Zhang, Bing Chen; Jin, Tian; et al.; A survey on the low-dimensional-model-based electromagnetic imaging; Now Publishers; Foundations and Trends in Signal Processing; 12; 2; 6-2018; 107-199 1932-8354 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/93765 |
identifier_str_mv |
Li, Lianlin; Hurtado, Martin; Xu, Feng; Zhang, Bing Chen; Jin, Tian; et al.; A survey on the low-dimensional-model-based electromagnetic imaging; Now Publishers; Foundations and Trends in Signal Processing; 12; 2; 6-2018; 107-199 1932-8354 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://www.nowpublishers.com/article/Details/SIG-103 info:eu-repo/semantics/altIdentifier/doi/10.1561/2000000103 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Now Publishers |
publisher.none.fl_str_mv |
Now Publishers |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1842269073328570368 |
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13.13397 |