Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/106948
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dc.contributorDepartment of Electrical and Electronic Engineeringen_US
dc.creatorZeng, Qen_US
dc.creatorXu, Wen_US
dc.creatorYu, Cen_US
dc.creatorZhang, Nen_US
dc.creatorYu, Cen_US
dc.date.accessioned2024-06-07T00:59:05Z-
dc.date.available2024-06-07T00:59:05Z-
dc.identifier.isbn978-1-943580-45-3en_US
dc.identifier.urihttp://hdl.handle.net/10397/106948-
dc.descriptionConference on Lasers and Electro-Optics/Pacific Rim 2018, Hong Kong China, 29 July-3 August 2018en_US
dc.language.isoenen_US
dc.publisherOptica Publishing Groupen_US
dc.rights© 2018 The Author(s)en_US
dc.rightsThe following publication Q. Zeng, W. Xu, C. Yu, N. Zhang, and C. Yu, "Fiber-optic Activity Monitoring with Machine Learning," in CLEO Pacific Rim Conference 2018, OSA Technical Digest (Optica Publishing Group, 2018), paper W4K.5 is available at https://doi.org/10.1364/cleopr.2018.w4k.5.en_US
dc.titleFiber-optic activity monitoring with machine learningen_US
dc.typeConference Paperen_US
dc.identifier.doi10.1364/cleopr.2018.w4k.5en_US
dcterms.abstractUnobtrusive activity monitoring based on fiber-optic Mach-Zehnder interferometer is proposed, employing deep bi-directional long short-term memory network, realizing three activities recognition with accuracy of 99.2% and resolution of 0.5s.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationCLEO Pacific Rim Conference 2018, OSA Technical Digest (Optica Publishing Group, 2018), paper W4K.5en_US
dcterms.issued2018-
dc.identifier.scopus2-s2.0-85065605238-
dc.relation.conferenceCLEO Pacific Rim [CLEOPR]en_US
dc.identifier.artnW4K.5en_US
dc.description.validate202405 bcchen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberEIE-0505-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of China; HKPUen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS26118578-
dc.description.oaCategoryVoR alloweden_US
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