dc.contributor.advisor | Twomey, Janet M. | |
dc.contributor.author | Rabanimotlagh, Ahmad | |
dc.date.accessioned | 2018-06-08T16:06:16Z | |
dc.date.available | 2018-06-08T16:06:16Z | |
dc.date.issued | 2017-12 | |
dc.identifier.other | d17040 | |
dc.identifier.uri | http://hdl.handle.net/10057/15290 | |
dc.description | Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Industrial, Systems and Manufacturing Engineering | |
dc.description.abstract | Within the context of environmental studies, environmental mapping addresses
the monitoring and analysis of physical processes appearing in the nature, such as atmospheric
or oceanic transformations. In such studies, often monitoring is fulfilled by
deployment of sensor infrastructures and analytics are legitimized by the existance of statistical
models known to represent the unknown nature of the physical process. Despite
a great number of researches existing in the field of environmental mapping, development
of analytical frameworks that well-utilize the advanced sensor platforms along with
theoratically proven novel mathematical ideas to address the existing research gaps is a
promising area of effort.
Therefore, the objective of this dissertation is evolved around development of novel
frameworks to address the analytical shortcomings of the previous researches in environmental
mapping by employing mathematical and statistical techniques on proper sensing
platforms. Our proposed frameworks incorporate the stochasticity of the model underlying
the physical processes in an online intelligent manner to progressively and adaptively
build an accurate representation of the physical process based on modern sensing
techonologies in a cost-effective practice. Our research asserts the validity of the proposed
frameworks through both theory and implementation. | |
dc.format.extent | viii, 58 pages | |
dc.language.iso | en_US | |
dc.publisher | Wichita State University | |
dc.rights | Copyright 2017 by Ahmad Rabanimotlagh
All Rights Reserved | |
dc.subject.lcsh | Electronic dissertations | |
dc.title | Large-scale online environmental mapping: from theory to
implementation | |
dc.type | Dissertation | |