Accurate Indoor Visible Light Positioning System utilizing Machine Learning Technique with Height Tolerance
| 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.contributor.department | 光電工程學系 | zh_TW |
| dc.contributor.department | Department of Photonics | 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.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.identifier.journal | 2018 OPTICAL FIBER COMMUNICATIONS CONFERENCE AND EXPOSITION (OFC) | en_US |
| dc.identifier.uri | https://ir.lib.nycu.edu.tw/handle/11536/146225 | |
| dc.identifier.wosnumber | WOS:000437286300113 | 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 |
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