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Conference Paper: Using Content Knowledge to Improve Reconstruction Performance by Semantic Compressive Sensing

TitleUsing Content Knowledge to Improve Reconstruction Performance by Semantic Compressive Sensing
Authors
Issue Date2019
PublisherIEEE. The Proceedings' web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800486
Citation
2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Suzhou, China, 29 July-2 August. 2019, p. 259-264 How to Cite?
AbstractCompressive sensing (CS) provides a method to perform sampling and compression simultaneously when a signal is sparse or compressible. The conventional CS is established on the prior knowledge of sparsity and cannot reconstruct the signals if they fail to be represented explicitly in a sparse form. Semantic CS is a new approach for sensing a signal based on its content knowledge, which can reduce the sensing rate and the sparsity constraint. However, the effects of content knowledge on the reconstruction quality is still not clear. In this paper, we provide an approach for designing a sensing matrix for semantic CS and experimentally study how content knowledge affects the reconstruction performance of the proposed CS.
Persistent Identifierhttp://hdl.handle.net/10722/282980
ISBN

 

DC FieldValueLanguage
dc.contributor.authorLi, C-
dc.contributor.authorWang, S-
dc.contributor.authorSun, Z-
dc.contributor.authorBi, S-
dc.contributor.authorXi, N-
dc.date.accessioned2020-06-05T06:23:43Z-
dc.date.available2020-06-05T06:23:43Z-
dc.date.issued2019-
dc.identifier.citation2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Suzhou, China, 29 July-2 August. 2019, p. 259-264-
dc.identifier.isbn978-1-7281-0771-4-
dc.identifier.urihttp://hdl.handle.net/10722/282980-
dc.description.abstractCompressive sensing (CS) provides a method to perform sampling and compression simultaneously when a signal is sparse or compressible. The conventional CS is established on the prior knowledge of sparsity and cannot reconstruct the signals if they fail to be represented explicitly in a sparse form. Semantic CS is a new approach for sensing a signal based on its content knowledge, which can reduce the sensing rate and the sparsity constraint. However, the effects of content knowledge on the reconstruction quality is still not clear. In this paper, we provide an approach for designing a sensing matrix for semantic CS and experimentally study how content knowledge affects the reconstruction performance of the proposed CS.-
dc.languageeng-
dc.publisherIEEE. The Proceedings' web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800486-
dc.relation.ispartofIEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)-
dc.rightsIEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). Copyright © IEEE.-
dc.rights©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.titleUsing Content Knowledge to Improve Reconstruction Performance by Semantic Compressive Sensing-
dc.typeConference_Paper-
dc.identifier.emailBi, S: shengbi@hku.hk-
dc.identifier.emailXi, N: xining@hku.hk-
dc.identifier.authorityXi, N=rp02044-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/CYBER46603.2019.9066586-
dc.identifier.hkuros310080-
dc.identifier.spage259-
dc.identifier.epage264-
dc.publisher.placeUnited States-

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