Faculty Advisor

Yakovlev, Vadim V.

Abstract

This project contributes to the field of computational techniques for processing data in microwave imaging inside closed cavities. A computational procedure for imaging of a spherical inhomogeneity in a dielectric sample is outlined. It uses an artificial neural network capable of reconstructing geometrical and material parameters. The network uses data from an FDTD model. Computational experiments are reported for the 4-port waveguide element containing a Teflon sample with a hidden inclusion. The error in reconstruction of four geometrical parameters of a dielectric sphere is 3.3%; the error in finding complex permittivity of the inclusion is 9.8%. The project makes a solid theoretical background for the experimental program dedicated to multiport systems for practical applications.

Publisher

Worcester Polytechnic Institute

Date Accepted

April 2017

Major

Mathematical Sciences

Project Type

Major Qualifying Project

Accessibility

Unrestricted

Advisor Department

Mathematical Sciences

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