Pitting corrosion is a form of highly localized corrosion that can lead to crack and failure of a structure. Study on pitting corrosion is necessary in order to predict and prevent the risk of failure of structure susceptible to corrosion. In this thesis, a combination of Cellular Automata (CA) and Boundary Element Method (BEM) was developed to simulate pitting corrosion growth under certain environment. It is assumed that pitting corrosion can be simplified to electrochemical corrosion cell. The distribution of potential around this corrosion cell can then be simulated by BEM. This distribution potential represents cathodic and anodic reactions around the corrosion cell. A CA model was developed that uses transition rules reflecting mechanism of pitting corrosion. The CA model has two types of cell states, one reflecting BEM simulation results and the other reflecting the status of corrosion cell (anode, cathode, and passive metal’s surface). For every CA iteration, the CA decides the state of the corrosion cells (the location and size of anode, cathode) while BEM simulate the level of electrochemical activity at discrete location on the surface (represented by potential distribution). In order to demonstrate the methodology, a simple case of rectangular corrosion cell with varied dimensions and under different polarization functions is considered. Results show certain shapes tend to grow at certain type environment and these pits are comparable to commonly observed pit shapes. In addition, stress analysis was carried out to investigate the severity of corrosion pits of varying shapes and sizes. Results show that certain pits induced highly varying stress concentration as it grows over time, while others have more steady increase of stress concentration.

Many complex systems in nature are governed by simple local interactions, although a number are also described by global interactions. For example, within the field of hydraulics the Navier-Stokes equations describe free-surface water flow, through means of the global preservation of water volume, momentum and energy. However, solving such partial differential equations (PDEs) is computationally expensive when applied to large 2D flow problems. An alternative which reduces the computational complexity, is to use a local derivative to approximate the PDEs, such as finite difference methods, or Cellular Automata (CA). The high speed processing of such simulations is important to modern scientific investigation especially within urban flood modelling, as urban expansion continues to increase the number of impervious areas that need to be modelled. Large numbers of model runs or large spatial or temporal resolution simulations are required in order to investigate, for example, climate change, early warning systems, and sewer design optimisation. The recent introduction of the Graphics Processor Unit (GPU) as a general purpose computing device (General Purpose Graphical Processor Unit, GPGPU) allows this hardware to be used for the accelerated processing of such locally driven simulations. A novel CA transformation for use with GPUs is proposed here to make maximum use of the GPU hardware. CA models are defined by the local state transition rules, which are used in every cell in parallel, and provide an excellent platform for a comparative study of possible alternative state transition rules. Writing local state transition rules for CA systems is a difficult task for humans due to the number and complexity of possible interactions, and is known as the ‘inverse problem’ for CA. Therefore, the use of Genetic Programming (GP) algorithms for the automatic development of state transition rules from example data is also investigated in this thesis. GP is investigated as it is capable of searching the intractably large areas of possible state transition rules, and producing near optimal solutions. However, such population-based optimisation algorithms are limited by the cost of many repeated evaluations of the fitness function, which in this case requires the comparison of a CA simulation to given target data. Therefore, the use of GPGPU hardware for the accelerated learning of local rules is also developed. Speed-up factors of up to 50 times over serial Central Processing Unit (CPU) processing are achieved on simple CA, up to 5-10 times speedup over the fully parallel CPU for the learning of urban flood modelling rules. Furthermore, it is shown GP can generate rules which perform competitively when compared with human formulated rules. This is achieved with generalisation to unseen terrains using similar input conditions and different spatial/temporal resolutions in this important application domain.

Today, there is a substantial number of software and research groups that focus on the development of image processing software to extract useful information from medical images, in order to assist and improve patient diagnosis. The work presented in this thesis is centred on processing of images of blood and bone marrow smears of patients suffering from leukaemia, a common type of cancer. In general, cancer is due to aberrant gene expression, which is caused by either mutations or epigenetic changes in DNA. Poor diet and unhealthy lifestyle may trigger or contribute to these changes, although the underlying mechanism is often unknown. Importantly, many cancer types including leukaemia are curable and patient survival and treatment can be improved, subject to prompt diagnosis. In particular, this study focuses on Acute Myeloid Leukaemia (AML), which can be of eight distinct types (M0 to M7), with the main objective to develop a methodology to automatically detect and classify leukaemia cells into one of the above types. The data was collected from the Department of Haematology, Universiti Sains Malaysia, in Malaysia. Three main methods, namely Cellular Automata, Heuristic Search and classification using Neural Networks are facilitated. In the case of Cellular Automata, an improved method based on the 8-neighbourhood and rules were developed to remove noise from images and estimate the radius of the potential blast cells contained in them. The proposed methodology selects the starting points, corresponding to potential blast cells, for the subsequent seeded heuristic search. The Seeded Heuristic employs a new fitness function for blast cell detection. Furthermore, the WEKA software is utilised for classification of blast cells and hence images, into AML subtypes. As a result accuracy of 97.22% was achieved in the classification of blasts into M3 and other AML subtypes. Finally, these algorithms are integrated into an automated system for image processing. In brief, the research presented in this thesis involves the use of advanced computational techniques for processing and classification of medical images, that is, images of blood samples from patients suffering from leukaemia.

Les automates cellulaires sont un modèle de calcul massivement parallèle introduit dans les années 50. De nombreuses variantes peuvent être considérées par exemple en faisant varier la dimension de l’espace de calcul, ou les possibilités de communication entre les différentes cellules. En effet, chaque cellule ne peut communiquer qu’avec un nombre fini d’autres cellules que l’on appelle son voisinage. Mes travaux s’intéressent principalement à l’impact du choix du voisinage sur les capacités algorithmiques de ce modèle. Cet impact étant bien compris en une dimension, mes travaux portent majoritairement sur les automates cellulaires bidimensionnels. J’ai tout d’abord essayé de généraliser des propriétés classiques de certaines classes de complexité au plus de voisinages possibles. On arrive notamment à un théorème d’accélération linéaire valable pour tous les voisinages. J’ai ensuite étudié les différences entre les classes de faibles complexités en fonction du voisinage choisi. Ces travaux ont permis d’exhiber des voisinages définissant des classes incomparables, ainsi que des ensembles de voisinages définissant exactement les mêmes classes de complexité. Enfin, je présente aussi des travaux sur les différences de puissance de calcul entre les automates de dimensions différentes. / Cellular automata were introduced in the 50s by J. von Neumann and S. Ulamas an efficient way of modeling massively parallel computation. Many variations of the model can be considered such as varying the dimension of the computation space or the communication capabilities of the computing cells. In a cellular automaton each cell can communicate only with a finite number of other cells called its neighbors. My work focuses on the impact of the choice of the neighbors on the algorithmic properties of the model. My first goal was to generalize some classical properties of computation models to the widest possible class of neighborhoods, in particular I prove a linear speedup theorem for any two dimensional neighborhood. I then study the difference between the complexity classes defined by different neighborhoods, show the existence of neighborhoods defining incomparable classes, and some sets of neighborhoods defining identical classes. Finally, I also discuss the impact of the dimension of the automata on their computational power.

Some people believe that IC densities are approaching the fundamental limits inherent to semiconductor technologies. One alternative to semiconductors is Quantum-dot Cellular Automata (QCA); QCA is a nanotechnology that offers the potential to build denser IC's that switch at higher frequencies and run on lower power. QCA's most basic building block, the QCA cell, is inherently binary; digital circuits are implemented by arranging these QCA cells in pre-defined configurations on a two dimensional plane. This paper proposes a logic formulation that describes arranging QCA cells on a two dimensional plane; it is presented as a set of rules that can be implemented with basic Boolean variables and operators. This Boolean formulation is general and can be applied to any given specification. In addition, an optimization constraint is defined so that the logic formulation will only validate the most efficient QCA cell arrangements. The correctness of the logic formulation has been empirically verified by testing it with a SAT solver. The effectiveness of the minimization constraint in conjunction with the logic formulation has been tested with a Pseudo-Boolean ILP solver.

Marroquin and Ramirez (1990) have recently discovered a class of discrete stochastic cellular automata with Gibbsian invariant measures that have a non-reversible dynamic behavior. Practical applications include more powerful algorithms than the Metropolis algorithm to compute MRF models. In this paper we describe a large class of stochastic dynamical systems that has a Gibbs asymptotic distribution but does not satisfy reversibility. We characterize sufficient properties of a sub-class of stochastic differential equations in terms of the associated Fokker-Planck equation for the existence of an asymptotic probability distribution in the system of coordinates which is given. Practical implications include VLSI analog circuits to compute coupled MRF models.

We give a necessary and sufficient condition for the existence of an increasing coupling of N (N >= 2) synchronous dynamics on S-Zd (PCA). Increasing means the coupling preserves stochastic ordering. We first present our main construction theorem in the case where S is totally ordered; applications to attractive PCAs are given. When S is only partially ordered, we show on two examples that a coupling of more than two synchronous dynamics may not exist. We also prove an extension of our main result for a particular class of partially ordered spaces.

This paper details the design and simulation of a fault-tolerant Quantum-dot Cellular Automata (QCA) NOT gate. A version of the standard NOT gate can be constructed to take advantage to the ability to easily integrate redundant structures into a QCA design. The fault-tolerant characteristics of this inverter are analyzed with QCADesigner v2.0.3 (Windows version) simulation software. These characteristics are then compared with the characteristics of two other non-redundant styles of NOT gates. The redundant version of the gate is more robust than the standard style for the inverter. However, another simple inverter style seems to be even more than this fault-tolerant design. Both versions of the gate will need to be studied further in the future to determine which design is most practical. / Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical and Computer Engineering / "July 2006." / Includes bibliographic references (leaves 31-33)

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Extensive oil drilling and transportation activities increase the possibility of oil spills and the consequent threat of oil pollution to the regional environment. Oil spills are hazards for marine and freshwater environments. Oil spill/slick detection, monitoring and management has received considerable attention over the past few years. It is a highly non-linear problem to predict oil slick movement and spreading, and it is a subject of complicated science. Oil spill behavior become more complicated while involving the effects of winds, surface currents, oil evaporation, shoreline deposition, land boundary, vertical dispersion and underwater current. Cellular Automata is a solution for problems of complicated science. A three-dimensional model for the prediction of oil slick movement and spreading using Cellular Automata is presented in this paper. The effects of winds, surface currents, oil evaporation, shoreline deposition, land boundary and vertical dispersion have been taken into account while processing the surface cells. At the same time, the effects of underwater currents and land boundary have been taken into account while processing the underwater cells. The algorithm has been used to simulate hypothetical oil slick movement and spreading in hypothetical geographic regions. The results of the simulation are qualitative agreed with real oil slick movement and spreading.