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- W2326149770 abstract "Location Awareness is key capability of Ubiquitous Environments. Although the development of GPS are more and more mature, its accuracy is just acceptable for outdoor positioning. For indoor positioning application, GPS even cannot achieve that accuracy while the requirement for accurately positioning a person is necessary. RSS based on LE is increasingly popular choice especially for indoor scenarios after pervasive adoption of IEEE 802.11 WSN. Fundamental requirement of such LE is to estimate location from RSS at a particular location. MPFE make RSS to fluctuate in unpredictable manner, introducing uncertainty in LE. Moreover, in practical situations, RSS values are not available at some locations all the time making the problem more difficult. To deal with this problem, ML have been applied so that the carried along devices can learn and make decision where they are in the building. Recent ML based on techniques remain many unsolved problem such as high cost of computation, high complexity of model structures and scalability. In this paper, we will introduce a few methods which give high accuracy and overcome other methods' disadvantages, such as MMLP, SVM, PPE with NN." @default.
- W2326149770 created "2016-06-24" @default.
- W2326149770 creator A5013311549 @default.
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- W2326149770 date "2014-10-26" @default.
- W2326149770 modified "2023-09-23" @default.
- W2326149770 title "Location Estimation based on Received Signal Strength from Access Pointer and Machine Learning Techniques" @default.
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- W2326149770 doi "https://doi.org/10.14257/astl.2014.63.45" @default.
- W2326149770 hasPublicationYear "2014" @default.
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