Comparative Study of Some Population-based Optimization Algorithms on Inverse Scattering of a Two-Dimensional Perfectly Conducting Cylinder in Slab Medium

[[abstract]]The application of four techniques for the shape reconstruction of a 2-D metallic cylinder buried in dielectric slab medium by measured the cattered fields outside is studied in the paper. The finite-difference time-domain (FDTD) technique is employed for electromagnetic analyses for both the forward and inverse scattering problems, while the shape reconstruction problem is transformed into optimization one during the course of inverse scattering. Then, four techniques including asynchronous particle swarm optimization (APSO), PSO, dynamic differential evolution (DDE) and self-adaptive DDE (SADDE) are applied to reconstruct the location and shape of the 2-Dmetallic cylinder for comparative purposes. The statistical performances of these algorithms are compared. The results show that SADDE outperforms PSO, APSO and DDE in terms of the ability of exploring the optima. However, these results are considered to be indicative and do not generally apply to all optimization problems in electromagnetics.[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]紙本[[booktype]]電子

Time Domain Microwave Imaging for a Buried Dielectric Cylinder by Dynamic Differential Evolution

[[abstract]]This paper presents the studies of time domain inverse scattering for a two dimensional homogeneous dielectric cylinder buried in a half-space which are based on the finite difference time domain (FDTD) method and the dynamic differential evolution (DDE). For the forward scattering, the FDTD method is employed to calculate the scattered E fields, while for the inverse scattering the DDE scheme is utilized to determine the shape, location and the permittivity of the buried cylindrical scatterer with arbitrary cross section. The subgirdding technique is implemented for the FDTD code in order to model the shape of the cylinder more smoothly. In additions, in order to describe an unknown cylinder with arbitrary cross section more effectively during the course of searching, the closed cubic-spline expansion is adopted to represent the scatterer contour instead of the frequently used trigonometric series. Numerical results demonstrate that, even when the initial guess is far away from the exact one, good reconstruction can be obtained. In addition, the effects of Gaussian noise on the reconstruction results are investigated. Numerical results show that even the measured scattered fields are contaminated with Gaussian noise, DDE is able to yield good reconstructed quality.[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]紙

Microwave Tomography Using Stochastic Optimization And High Performance Computing

This thesis discusses the application of parallel computing in microwave tomography for detection and imaging of dielectric objects. The main focus is on microwave tomography with the use of a parallelized Finite Difference Time Domain (FDTD) forward solver in conjunction with non-linear stochastic optimization based inverse solvers. Because such solvers require very heavy computation, their investigation has been limited in favour of deterministic inverse solvers that make use of assumptions and approximations of the imaging target. Without the use of linearization assumptions, a non-linear stochastic microwave tomography system is able to resolve targets of arbitrary permittivity contrast profiles while avoiding convergence to local minima of the microwave tomography optimization space. This work is focused on ameliorating this computational load with the use of heavy parallelization. The presented microwave tomography system is capable of modelling complex, heterogeneous, and dispersive media using the Debye model. A detailed explanation of the dispersive FDTD is presented herein. The system uses scattered field data due to multiple excitation angles, frequencies, and observation angles in order to improve target resolution, reduce the ill-posedness of the microwave tomography inverse problem, and improve the accuracy of the complex permittivity profile of the imaging target. The FDTD forward solver is parallelized with the use of the Common Unified Device Architecture (CUDA) programming model developed by NVIDIA corporation. In the forward solver, the time stepping of the fields are computed on a Graphics Processing Unit (GPU). In addition the inverse solver makes use of the Message Passing Interface (MPI) system to distribute computation across multiple work stations. The FDTD method was chosen due to its ease of parallelization using GPU computing, in addition to its ability to simulate wideband excitation signals during a single forward simulation. We investigated the use of distributed Particle Swarm Optimization (PSO) and Differential Evolution (DE) methods in the inverse solver for this microwave tomography system. In these optimization algorithms, candidate solutions are farmed out to separate workstations to be evaluated. As fitness evaluations are returned asynchronously, the optimization algorithm updates the population of candidate solutions and gives new candidate solutions to be evaluated to open workstations. In this manner, we used a total of eight graphics processing units during optimization with minimal downtime. Presented in this thesis is a microwave tomography algorithm that does not rely on linearization assumptions, capable of imaging a target in a reasonable amount of time for clinical applications. The proposed algorithm was tested using numerical phantoms that with material parameters similar to what one would find in normal or malignant human tissue

Location Optimization for Antennas by Asynchronous Particle Swarm Optimization

[[abstract]]A novel optimisation procedure for the location of the transmitter in 3 × 3 multiple input multiple output wireless local area network wireless communication systems is presented. The optimal antenna location for maximising the channel capacity is searched by particle swarm optimiser (PSO) and asynchronous particle swarm optimisation (APSO). There are two different receiver locations considered in the simulation. These two cases are: (i) the transmitter is mobile in the whole indoor environment and the receivers are located on the tables spaced in intervals uniformly distributed (ii) the transmitter is mobile and the receivers are space in uniformly distributed intervals in the whole indoor environment. Numerical results have shown that the proposed PSO and APSO methods are transmit antenna location is optimised to increase channel capacity. The APSO has better optimisation results compared with the PSO and numerical results also show that the APSO outperforms the PSO in convergence speed.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子