dc.contributor.advisor | Otero, Carlos E. | |
dc.contributor.author | Egi, Yunus | |
dc.date.accessioned | 2019-05-06T19:29:04Z | |
dc.date.available | 2019-05-06T19:29:04Z | |
dc.date.created | 2019-05 | |
dc.date.issued | 2019-05 | |
dc.date.submitted | May 2019 | |
dc.identifier.uri | http://hdl.handle.net/11141/2799 | |
dc.description | Thesis (Ph.D.) - Florida Institute of Technology, 2019 | en_US |
dc.description.abstract | When 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.mimetype | application/pdf | |
dc.language.iso | en_US | en_US |
dc.rights | Copyright held by author. | en_US |
dc.title | A Sensor Fusion and Machine Learning Approach for Predicting Signal Power Path Loss in Wireless Communications | en_US |
dc.type | Dissertation | en_US |
dc.date.updated | 2019-05-06T18:53:54Z | |
thesis.degree.name | Doctor of Philosophy in Electrical Engineering | en_US |
thesis.degree.level | Doctoral | en_US |
thesis.degree.discipline | Electrical Engineering | en_US |
thesis.degree.department | Computer Engineering and Sciences | en_US |
thesis.degree.grantor | Florida Institute of Technology | en_US |
dc.type.material | text | |