Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/106917
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dc.contributorDepartment of Electrical and Electronic Engineering-
dc.creatorHan, Sen_US
dc.creatorXu, Wen_US
dc.creatorYou, Sen_US
dc.creatorDong, Ben_US
dc.creatorYu, Cen_US
dc.creatorZhao, Wen_US
dc.date.accessioned2024-06-07T00:58:52Z-
dc.date.available2024-06-07T00:58:52Z-
dc.identifier.isbn978-1-943580-70-5en_US
dc.identifier.urihttp://hdl.handle.net/10397/106917-
dc.descriptionAsia Communications and Photonics Conference 2019, Chengdu China, 2-5 November 2019en_US
dc.language.isoenen_US
dc.publisherOptica Publishing Groupen_US
dc.rights© 2019 The Author(s)en_US
dc.rightsThe following publication S. Han, W. Xu, S. You, B. Dong, C. Yu, and W. Zhao, "Principle Component Analysis and Random Forest Based All-Fiber Activity Monitoring," in Asia Communications and Photonics Conference (ACPC) 2019, OSA Technical Digest (Optica Publishing Group, 2019), paper S4G.4 is available at https://opg.optica.org/abstract.cfm?uri=acpc-2019-S4G.4&origin=search.en_US
dc.titlePrinciple component analysis and random forest based all-fiber activity monitoringen_US
dc.typeConference Paperen_US
dcterms.abstractAn activity monitoring algorithm based on principle component analysis and random forest is proposed, identifying three kinds of activities obtained from Mach-Zehnder interferometer with accuracy of 99.5% within one second, namely, normal, nobody and movement.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationAsia Communications and Photonics Conference (ACPC) 2019, OSA Technical Digest (Optica Publishing Group, 2019), paper S4G.4en_US
dcterms.issued2019-
dc.relation.conferenceAsia Communications and Photonics Conference [ACPC]-
dc.identifier.artnS4G.4en_US
dc.description.validate202405 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberEIE-0292-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextHong Kong Polytechnic University; CAS Pioneer Hundred Talents Programen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS26113357-
dc.description.oaCategoryVoR alloweden_US
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