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dc.contributor.authorChuang, Yu-Chengen_US
dc.contributor.authorLi, Zhi-Qingen_US
dc.contributor.authorHsu, Chin-Weien_US
dc.contributor.authorLiu, Yangen_US
dc.contributor.authorChow, Chi-Waien_US
dc.date.accessioned2019-08-02T02:18:34Z-
dc.date.available2019-08-02T02:18:34Z-
dc.date.issued2019-05-27en_US
dc.identifier.issn1094-4087en_US
dc.identifier.urihttp://dx.doi.org/10.1364/OE.27.016377en_US
dc.identifier.urihttp://hdl.handle.net/11536/152377-
dc.description.abstractWe propose and experimentally demonstrate a practical visible light position (VLP) system using repeated unit cells and machine learning (ML) algorithms. ML is employed to increase the positioning accuracy. Algorithms of the 2nd-order regression ML model and the polynomial trilateral ML model are discussed. More than 80% of the measurement data have position error within 4 cm when using the 2nd-order regression ML model, while the position error is within 5 cm when using the polynomial trilateral ML model. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreementen_US
dc.language.isoen_USen_US
dc.titleVisible light communication and positioning using positioning cells and machine learning algorithmsen_US
dc.typeArticleen_US
dc.identifier.doi10.1364/OE.27.016377en_US
dc.identifier.journalOPTICS EXPRESSen_US
dc.citation.volume27en_US
dc.citation.issue11en_US
dc.citation.spage16377en_US
dc.citation.epage16383en_US
dc.contributor.department光電工程學系zh_TW
dc.contributor.departmentDepartment of Photonicsen_US
dc.identifier.wosnumberWOS:000469227200104en_US
dc.citation.woscount0en_US
Appears in Collections:Articles