Yes / The potentially broad application area in engineering design using Genetic Algorithm (GA) has been widely adopted by many researchers due to its high consistency and accuracy. Presented here is the initial design of a wideband non-dispersive wire bow-tie antenna using GA for breast cancer detection applications. The ultimate goal of this design is to achieve minimal late-time ringing but at higher frequencies such as that located from 4 to 8 GHz, in which is desire to penetrate human tissue for near field imaging. Resistively loading method to reduce minimal ringing caused by the antenna internal reflections is implemented and discussed when the antenna is located in free space and surrounded by lossy medium. Results with optimised antenna geometry and different number of resistive loads are presented and compared with and without existence of scatterers.

Electromagnetic launchers (railguns) are set to replace traditional large caliber ship mounted cannons in the near future. The success of the railgun depends heavily upon a comprehensive understanding of beam behavior during periods of heavy dynamic loading. It is hypothesized that the combination of velocity transition effects, electromagnetic loading, and other non-linear or design specific effects contribute to areas of high stresses/strains over the length of the rail/beam during launch. This paper outlines the use of the Timoshenko beam model, a model which builds upon the traditional Bernoulli-Euler beam theory with the addition of shear deformation and rotary inertia effects, a necessity for high wave velocities. Real-world experimental setups are simplified and approximated by a series of linear springs and dampers for model prediction and validation. The Timoshenko beam model is solved using finite difference (FD) methods for the approximation of spatial derivatives and MATLAB ordinary differential equation (ODE) solvers. The model shows good convergence and precision over a large range of system parameters including load velocities, foundation stiffness values, and beam dimensions. Comparison to experimental strain data has validated model accuracy to an acceptable level. Accuracy is further enhanced with the inclusion of damping and non-linear or piecewise effects used to mimic experimental observations. The MATLAB software package presents a valid preliminary analysis tool for railgun beam and foundation design while offering advantages in ease of use, computation time, and system requirements when compared to traditional FEA tools. / Master of Science

Hyperthermia is a cancer treatment modality that raises cancerous tissue to cytotoxic temperature levels for roughly 30 to 45 minutes. Hyperthermia treatment planning refers to the use of computational models to optimize the heating protocol to be used in a hyperthermia treatment. This thesis presents a method to optimize a hyperthermia treatment heating protocol. An algorithm is developed which recovers a heating protocol that will cause a desired amount of thermal damage within a region of tissue. The optimization algorithm is validated experimentally on an albumen tissue phantom. The transient temperature distribution within the region is simulated using a two-dimensional, finite-difference model of the Pennes bioheat equation. The relationship between temperature and time is integrated to produce a damage field according to two different models; Henriques'' model and the thermal dose model (Moritz and Henriques (1947)), (Sapareto and Dewey (1984)). A minimization algorithm is developed which re duces the value of an objective function based on the squared difference between an optimal and calculated damage field. Either damage model can be used in the minimization algorithm. The adjoint problem in conjunction with the conjugate gradient method is used to minimize the objective function of the control problem. The flexibility of the minimization algorithm is proven experimentally and through a variety of simulations. With regards to the validation experiment, the optimal and recovered regions of permanent thermal damage are in good agreement for each test performed. A sensitivity analysis of the finite difference and damage models shows that the experimentally-obtained extent of damage is consistently within a tolerable error range. Excellent agreement between the optimal and recovered damage fields is also found in simulations of hyperthermia treatments on perfused tissue. A simplified and complex model of the human skin were created for use within the algorithm. Minimizations using both the Henriques'' model and the thermal dose model in the objective function are performed. The Henriques'' damage model was found to be more desirable for use in the minimization algorithm than the thermal dose model because it is less computationally intensive and includes a mechanism to predict the threshold of permanent thermal damage. The performance of the minimization algorithm was not hindered by adding complexity to the skin model. The method presented here for optimizing hyperthermia treatments is shown to be robust and merits further investigation using more complicated patient models. / Master of Science

Today, heterogeneous computing has truly reshaped the way scientists think and approach high-performance computing (HPC). Hardware accelerators such as general-purpose graphics processing units (GPUs) and Intel Many Integrated Core (MIC) architecture continue to make in-roads in accelerating large-scale scientific applications. These advancements, however, introduce new sets of challenges to the scientific community such as: selection of best processor for an application, effective performance optimization strategies, maintaining performance portability across architectures etc. In this thesis, we present our techniques and approach to address some of these significant issues. Firstly, we present a fully automated approach to project the relative performance of an OpenCL program over different GPUs. Performance projections can be made within a small amount of time, and the projection overhead stays relatively constant with the input data size. As a result, the technique can help runtime tools make dynamic decisions about which GPU would run faster for a given kernel. Usage cases of this technique include scheduling or migrating GPU workloads over a heterogeneous cluster with different types of GPUs. We then present our approach to accelerate a seismology modeling application that is based on the finite difference method (FDM), using MPI and CUDA over a hybrid CPU+GPU cluster. We describe the generic computational complexities involved in porting such applications to the GPUs and present our strategy of efficient performance optimization and characterization. We also show how performance modeling can be used to reason and drive the hardware-specific optimizations on the GPU. The performance evaluation of our approach delivers a maximum speedup of 23-fold with a single GPU and 33-fold with dual GPUs per node over the serial version of the application, which in turn results in a many-fold speedup when coupled with the MPI distribution of the computation across the cluster. We also study the efficacy of GPU-integrated MPI, with MPI-ACC as an example implementation, in a seismology modeling application and discuss the lessons learned. / Master of Science

<p dir="ltr">The Alpine Fault, located in the South Island, New Zealand, is a subject of intense geological study due to its potential to trigger large earthquakes. It encompasses a complex system with the interplay of mechanics, thermodynamics, and fluid. Gaining insights into these systems not only enhances our understanding of the fault but also holds the potential to guide risk mitigation efforts.</p><p dir="ltr">The damage extent and fracture networks within the metamorphic rock mass adjacent to the fault can be effectively characterized by seismic anisotropy, an elastic property of rock, where seismic waves travel at different speeds with variation directions. This thesis presents a comprehensive exploration of seismic anisotropy in the hanging wall immediately adjacent to the principal slip zone of the Alpine Fault in New Zealand. Leveraging the borehole seismic data from a unique scientific drilling project and advanced numerical modeling techniques, the ultimate goal is to invert and parameterize the bulk seismic anisotropy.</p><p dir="ltr">Motivated by these challenges, the thesis undertakes several key initiatives: The first effort focuses on gaining a comprehensive understanding of an innovative method for seismic measurement: Distributed Acoustic Sensing (DAS) – examining its operational principles, factors influencing observed wavelets, and how it contrasts with traditional point sensors for accurate interpretation. Subsequently, the research introduces the implementation of an open-source seismic wave solver designed for modeling elastic wave propagation in complicated anisotropic media. This solver is further optimized for computational efficiency with its performance rigorously benchmarked.</p><p dir="ltr">With this preparedness, the inversion is further facilitated by high-performance computing (HPC) and a deep-learning algorithm specifically designed for automatically picking transit times. The inverted bulk elastic constants, compared to the intact rock, reveal 28% to 35% reductions in qP-wave velocity, characterizing the damage due to mesoscale fracture. Further analysis sheds light on the existence of orthogonal fracture sets and an intricate geometrical arrangement that agree with the previous borehole image log. This represents an advancement in our ability to characterize and understand the geologic processes with seismic anisotropy.</p>

No / This paper presents an investigation into the impact of data dependencies in real-time high performance sequential and parallel processing. An adaptive active vibration control algorithm is considered to demonstrate the impact of data dependencies in real-time computing. The algorithm is analysed in detail to explore the inherent data dependencies. To minimize the impact of data dependencies, an investigation into reducing memory access in sequential computing is provided. The impact of data dependencies with various interconnections is also explored and demonstrated in real-time parallel processing through a set of experiments.

No / We present a new skin deformation method to create dynamic skin deformations in this paper. The core elements of our approach are a dynamic deformation model, an efficient data-driven finite difference solution, and a curve-based representation of 3D models.We first reconstruct skin deformation models at different poses from the taken photos of a male human arm movement to achieve real deformed skin shapes. Then, we extract curves from these reconstructed skin deformation models. A new dynamic deformation model is proposed to describe physics of dynamic curve deformations, and its finite difference solution is developed to determine shape changes of the extracted curves. In order to improve visual realism of skin deformations, we employ data-driven methods and introduce skin shapes at the initial and final poses in to our proposed dynamic deformation model. Experimental examples and comparisons made in this paper indicate that our proposed dynamic skin deformation technique can create realistic deformed skin shapes efficiently with a small data size.

This thesis presents the research results on the development of a microwave tomography imaging algorithm capable of reconstructing the dielectric properties of the unknown object. Our focus was on the theoretical aspects of the non-linear tomographic image reconstruction problem with particular emphasis on developing efficient numerical and non-linear optimization for solving the inverse scattering problem. A detailed description of a novel microwave tomography method based on frequency dependent finite difference time domain, a numerical method for solving Maxwell's equations and Genetic Algorithm (GA) as a global optimization technique is given. The proposed technique has the ability to deal with the heterogeneous and dispersive object with complex distribution of dielectric properties and to provide a quantitative image of permittivity and conductivity profile of the object. It is shown that the proposed technique is capable of using the multi-frequency, multi-view, and multi-incident planer techniques which provide useful information for the reconstruction of the dielectric properties profile and improve image quality. In addition, we show that when a-priori information about the object under test is known, it can be easily integrated with the inversion process. This provides realistic regularization of the solution and removes or reduces the possibility of non-true solutions. We further introduced application of the GA such as binary-coded GA, real-coded GA, hybrid binary and real coded GA, and neural-network/GA for solving the inverse scattering problem which improved the quality of the images as well as the conversion rate. The implications and possible advantages of each type of optimization are discussed, and synthetic inversion results are presented. The results showed that the proposed algorithm was capable of providing the quantitative images, although more research is still required to improve the image quality. In the proposed technique the computation time for solution convergence varies from a few hours to several days. Therefore, the parallel implementation of the algorithm was carried out to reduce the runtime. The proposed technique was evaluated for application in microwave breast cancer imaging as well as measurement data from university of Manitoba and Institut Frsenel's microwave tomography systems.

This thesis presents the research results on the development of a microwave tomography imaging algorithm capable of reconstructing the dielectric properties of the unknown object. Our focus was on the theoretical aspects of the non-linear tomographic image reconstruction problem with particular emphasis on developing efficient numerical and non-linear optimization for solving the inverse scattering problem. A detailed description of a novel microwave tomography method based on frequency dependent finite difference time domain, a numerical method for solving Maxwell's equations and Genetic Algorithm (GA) as a global optimization technique is given. The proposed technique has the ability to deal with the heterogeneous and dispersive object with complex distribution of dielectric properties and to provide a quantitative image of permittivity and conductivity profile of the object. It is shown that the proposed technique is capable of using the multi-frequency, multi-view, and multi-incident planer techniques which provide useful information for the reconstruction of the dielectric properties profile and improve image quality. In addition, we show that when a-priori information about the object under test is known, it can be easily integrated with the inversion process. This provides realistic regularization of the solution and removes or reduces the possibility of non-true solutions. We further introduced application of the GA such as binary-coded GA, real-coded GA, hybrid binary and real coded GA, and neural-network/GA for solving the inverse scattering problem which improved the quality of the images as well as the conversion rate. The implications and possible advantages of each type of optimization are discussed, and synthetic inversion results are presented. The results showed that the proposed algorithm was capable of providing the quantitative images, although more research is still required to improve the image quality. In the proposed technique the computation time for solution convergence varies from a few hours to several days. Therefore, the parallel implementation of the algorithm was carried out to reduce the runtime. The proposed technique was evaluated for application in microwave breast cancer imaging as well as measurement data from university of Manitoba and Institut Frsenel's microwave tomography systems.

This work deals with techniques of time-domain analysis of interconnects. After a studying crucial issue of time-domain analysis of interconnects methods of modeling and simulation simple interconnects in electrotechnics are described. For transient effect analysis two elementary methods can be used: the state variable method and the FDTD (Finite - Difference Time - Domain) method. The FDTD method can be used to solve partial differential equations in time domain, for instance equations of transmission lines. The method is very effective and delivers satisfactory results in case of linear and non-linear lines with a single “live” conductor. The method can be easily programmed in Matlab.