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dc.contributor.advisorOtero, Carlos E.
dc.contributor.authorEgi, Yunus
dc.date.accessioned2019-05-06T19:29:04Z
dc.date.available2019-05-06T19:29:04Z
dc.date.created2019-05
dc.date.issued2019-05
dc.date.submittedMay 2019
dc.identifier.urihttp://hdl.handle.net/11141/2799
dc.descriptionThesis (Ph.D.) - Florida Institute of Technology, 2019en_US
dc.description.abstractWhen it comes to Wireless Communication systems and their optimization in different environments, estimation of SPPL for different terrain models becomes one of the most tedious problems for Radio Frequency engineers. Since every terrain has their complex terrain structures and contains micro-variations, they end up with an ambiguous SPPL via scattering and absorption. Also, modern SPPL prediction models are error free since they use predefined estimation parameters for classified terrain model. Sometimes, terrain-related estimation errors may cause over undesirable SPPL level which is much larger than 5% tolerance error. To avoid this problem, one can benefit from 3D map of the environment by using Light Detection and Ranging (LiDAR) and Artificial Neural Network which is one of the most common Machine Learning (ML) algorithm. This tools can be utilized to classify the objects and their structures which are the main reason for scattering and absorption. The fusion process of LiDAR and corresponding color classified satellite images can be fused to extract desired tree canopies. In this dissertation, Machine Learning and image processing techniques will be used to model SPPL for deployment of WCS.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.rightsCopyright held by author.en_US
dc.titleA Sensor Fusion and Machine Learning Approach for Predicting Signal Power Path Loss in Wireless Communicationsen_US
dc.typeDissertationen_US
dc.date.updated2019-05-06T18:53:54Z
thesis.degree.nameDoctor of Philosophy in Electrical Engineeringen_US
thesis.degree.levelDoctoralen_US
thesis.degree.disciplineElectrical Engineeringen_US
thesis.degree.departmentComputer Engineering and Sciencesen_US
thesis.degree.grantorFlorida Institute of Technologyen_US
dc.type.materialtext


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