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dc.contributor.authorChuang, Yu-Chengen_US
dc.contributor.authorChow, Chi-Waien_US
dc.contributor.authorLiu, Yangen_US
dc.contributor.authorYeh, Chien-Hungen_US
dc.contributor.authorLiao, Xin-Lanen_US
dc.contributor.authorLin, Kun-Hsienen_US
dc.contributor.authorChen, Yi-Yuanen_US
dc.date.accessioned2019-12-13T01:09:53Z-
dc.date.available2019-12-13T01:09:53Z-
dc.date.issued2019-10-14en_US
dc.identifier.issn1094-4087en_US
dc.identifier.urihttp://dx.doi.org/10.1364/OE.27.029924en_US
dc.identifier.urihttp://hdl.handle.net/11536/153007-
dc.description.abstractWe propose and experimentally demonstrated a light-panel and image sensor based visible light communication (VLC) system using machine learning (ML) algorithm. The ML algorithm is compared with the traditional demodulation scheme and the experimental results show that even at very high noise-ratio (NR) light-panel display content, the proposed ML algorithm shows significant bit error rate (BER) improvement. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreementen_US
dc.language.isoen_USen_US
dc.titleUsing logistic regression classification for mitigating high noise-ratio advisement light-panel in rolling-shutter based visible light communicationsen_US
dc.typeArticleen_US
dc.identifier.doi10.1364/OE.27.029924en_US
dc.identifier.journalOPTICS EXPRESSen_US
dc.citation.volume27en_US
dc.citation.issue21en_US
dc.citation.spage29924en_US
dc.citation.epage29929en_US
dc.contributor.department光電工程學系zh_TW
dc.contributor.departmentDepartment of Photonicsen_US
dc.identifier.wosnumberWOS:000489954500041en_US
dc.citation.woscount0en_US
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