Please use this identifier to cite or link to this item: http://oaps.umac.mo/handle/10692.1/222
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dc.contributor.authorKONG, MAN LONG(龔文龍)-
dc.contributor.authorHO, CHON HEI(何竣浠)-
dc.contributor.authorIEONG, IO NENG(楊耀寧)-
dc.date.accessioned2021-07-01T10:36:29Z-
dc.date.available2021-07-01T10:36:29Z-
dc.date.issued2020-
dc.identifier.citationKong, M. L., Ho, C. H., Ieong, I. N. (2020). Indoor Positioning for RFID using Machine Learning (Outstanding Academic Papers by Students (OAPS)). Retrieved from University of Macau, Outstanding Academic Papers by Students Repository.en_US
dc.identifier.urihttp://oaps.umac.mo/handle/10692.1/222-
dc.description.abstractNowadays, with the rapid development of wireless technology, people cannot live without wireless communication technology. Among many wireless communication technologies, wireless positioning technology plays an important role in people's life and travel, etc. In recent years, in addition to the well-known GPS positioning, wireless positioning technology also includes many positioning technologies. This paper mainly focuses on indoor positioning. Because GPS positioning technology is generally only applicable to outdoor positioning, this paper will introduce other commonly used indoor positioning technology, and then focus on our theme: RFID indoor positioning and positioning methods. In here, some positioning methods or techniques can be applied in RFID indoor positioning. The project team will choose to use the Received Signal Strength Indication (RSSI) positioning and the positioning algorithm (LANDMARC) as our main positioning method and applied it to RFID indoor positioning for experimental research. The main purpose of this project is to try to use some more accurate methods for positioning in the process of learning RFID and collect some data in several positioning-related experiments to plan the positioning method. As well as to build up the positioning programs as our basic program (input is RSSI reading, output is possible to position), and finally try to use LANDMARC algorithm and machine learning to make positioning results more accurate.en_US
dc.language.isoenen_US
dc.titleIndoor Positioning for RFID using Machine Learningen_US
dc.typeOAPSen_US
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.description.instructorDr. Wai Wa Choien_US
dc.contributor.facultyFaculty of Science and Technologyen_US
dc.description.programmeBachelor of Science in Electrical and Computer Engineeringen_US
Appears in Collections:FST OAPS 2020

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