Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.760965
Title: Low conductivity magnetic induction tomography for landmine detection
Author: Li, Fang
Awarding Body: University of Bath
Current Institution: University of Bath
Date of Award: 2018
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Abstract:
The main objective of the dissertation is to improve the imaging results for magneticinductance tomography (MIT)as anon-destructive imaging technique. MIT is generallyused to display the imaging contains the conductivity properties of the object under test. The hardware and software are regarded as basic topics of the development of MIT. The hardware of MIT is briefly introduced but analyzing the software problem of MIT is the main purposeof this thesis. The working flow of this dissertation can be explained as the following sections. Firstly, the forward problem of MIT hasbeen studied theoretically, including eddy current modeling with Biot-Savart theory implemented and the simulation works for the validation of forward problem. Secondly, the algorithms of inverse problem solvers are presentedbased on the explanation of mathematical equations, including linear/non-linear or iterative/non-iterative inverse problem. Thirdly, improved image quality of reconstructed images obtained by total variation regularization as the inverse problem solver both in circular and planar array sensor MIT system are demonstrated by experimental results. Finally, the potential feasibility of planar MIT system assisting other imaging system such as electrical capacitance tomography for plastic landmine detection is illustrated by simulation works. Altogether, this thesis presents the author’s research interests on improving reconstruction performance of MIT trough analyzingoninverse problem algorithmsdevelopments and expand the potential application of planar low conductivity MIT system in plastic Landmine detection.
Supervisor: Soleimani, Manuchehr Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.760965  DOI: Not available
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