Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hsu, Chin-Wei | en_US |
dc.contributor.author | Liu, Siming | en_US |
dc.contributor.author | Lu, Feng | en_US |
dc.contributor.author | Chow, Chi-Wai | en_US |
dc.contributor.author | Yeh, Chien-Hung | en_US |
dc.contributor.author | Chang, Gee-Kung | en_US |
dc.date.accessioned | 2018-08-21T05:56:28Z | - |
dc.date.available | 2018-08-21T05:56:28Z | - |
dc.date.issued | 2018-01-01 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/146225 | - |
dc.description.abstract | An accurate, low-cost indoor visible light positioning system utilizing machine learning technique is proposed and experimentally demonstrated. The average position resolution of the system can achieve 3.65 cm with height tolerance range of 15 cm. | en_US |
dc.language.iso | en_US | en_US |
dc.title | Accurate Indoor Visible Light Positioning System utilizing Machine Learning Technique with Height Tolerance | en_US |
dc.type | Proceedings Paper | en_US |
dc.identifier.journal | 2018 OPTICAL FIBER COMMUNICATIONS CONFERENCE AND EXPOSITION (OFC) | en_US |
dc.contributor.department | 光電工程學系 | zh_TW |
dc.contributor.department | Department of Photonics | en_US |
dc.identifier.wosnumber | WOS:000437286300113 | en_US |
Appears in Collections: | Conferences Paper |