Network decontamination is a well known mobile agent problem with many applications. We assume that all nodes of a network are contaminated (e.g., by a virus) and a set of agents is deployed to decontaminate them. An agent passing by a node decontaminates it, however a decontaminated node can be recontaminated if any of its neighbours is contaminated. In the vast literature a variety of models are considered and different assumptions are made on the power of the agents. In this thesis we study variation of the decontamination problem in mesh and tori topologies, under the assumption that when a node is decontaminated, it is immune to recontamination for a predefined amount of time t (called immunity time). After the immunity time is elapsed, recontamination can occur. We focus on three different models: mobile agents (MA), cellular automata (CA), and mobile cellular automata (MCA). The first two models are commonly studied and employed in several other contexts, the third model is introduced in this thesis for the first time. In each model we study the temporal decontamination problem (adapted to the particular setting) under a variety of assumptions on the capabilities of the decontaminating elements (agents for MA and MCA, decontaminating cells for CA). Some of the parameters we consider in this study are: visibility of the active elements, their ability to make copies of themselves, their ability to communicate, and the possibility to remember their past actions (memory). We describe several solutions in the various scenarios and we analyze their complexity. Efficiency is evaluated slightly differently in each model, but essentially the effort is in the minimization of the number of simultaneous decontaminating elements active in the system while performing the decontamination with a given immunity time.

Network decontamination is a well known mobile agent problem with many applications. We assume that all nodes of a network are contaminated (e.g., by a virus) and a set of agents is deployed to decontaminate them. An agent passing by a node decontaminates it, however a decontaminated node can be recontaminated if any of its neighbours is contaminated. In the vast literature a variety of models are considered and different assumptions are made on the power of the agents. In this thesis we study variation of the decontamination problem in mesh and tori topologies, under the assumption that when a node is decontaminated, it is immune to recontamination for a predefined amount of time t (called immunity time). After the immunity time is elapsed, recontamination can occur. We focus on three different models: mobile agents (MA), cellular automata (CA), and mobile cellular automata (MCA). The first two models are commonly studied and employed in several other contexts, the third model is introduced in this thesis for the first time. In each model we study the temporal decontamination problem (adapted to the particular setting) under a variety of assumptions on the capabilities of the decontaminating elements (agents for MA and MCA, decontaminating cells for CA). Some of the parameters we consider in this study are: visibility of the active elements, their ability to make copies of themselves, their ability to communicate, and the possibility to remember their past actions (memory). We describe several solutions in the various scenarios and we analyze their complexity. Efficiency is evaluated slightly differently in each model, but essentially the effort is in the minimization of the number of simultaneous decontaminating elements active in the system while performing the decontamination with a given immunity time.

Wireless sensor networks have been used in different applications due to the advancement of sensor technology. These uses also have raised different optimization issues. Most of the algorithms proposed as solutions to the various optimization problems are either centralized or distributed which are not ideal for these real life applications. Very few strictly local algorithms for wireless sensor networks exist in the literature. In this thesis, we consider some of these optimization problems of sensor networks, for example, sleep-wake scheduling, mobile dispersion, mobile object monitoring, and gathering problems. We also consider the depth adjustment problem of underwater sensor networks. We design cellular automaton based local algorithms for these problems. The cellular automaton is a bioinspired model used to model different physical systems including wireless sensor networks. One of the main advantages of using cellular automaton based algorithms is that they need very little local information to compute a solution. We perform different simulations and analysis and find that our algorithms are efficient in practice. / Thesis (Ph.D, Computing) -- Queen's University, 2012-11-25 13:37:36.854

The relatively new science of complex emergent processes is being applied to many fields including the study of organizations. There are many different models of the organization in current use, each with its own benefits. However, the science of complex emergent processes is able to deal with situations that conventional models have not been able to adequately describe. Wolfram's A New Kind of Science describes a comprehensive conceptual framework and scientific methodology which enables the study of organizations from a new perspective. These techniques create new ways of thinking about organizations and provide new insights into organizational behaviour. A particular class of complex emergent models are the cellular automata (CA). This thesis makes use of very basic cellular automata models described in Wolfram's A New Kind of Science to examine organizational behaviour. These models produce a variety of interesting patterns which can be easily interpreted and which graphically describe various characteristics of organizational behaviour. A variety of common types of organizational behaviour are examined and the organizational cultures which bring these behaviours about are investigated. The usefulness of using this method is considered. These techniques are then used to examine the reconstructing of Iraq in the period from the US led Coalition invasion in 2003 through to mid 2004. Several types of organizational behaviour are examined and the models are then used to examine various potential scenarios concerning the Iraq reconstruction process. The modelling outcomes about the Iraq reconstruction process are found to be comparable with the opinions of subject matter experts. Although limited in scope and only making use of a very limited class of models from all of those available in Wolfram's A New Kind of Science, this thesis demonstrated the usefulness of using such an approach in the understanding of organizational behaviour. The techniques used in this thesis, were able to demonstrate: the complex emergent properties of organizations; how organizational behaviour can be viewed as resulting from the interaction of individuals; the 'phase transitions' between different major classes of organizational behaviour; how different types of organizational behaviour are robust or otherwise to change; and how organizational behaviour forms naturally into certain common types. / Master of Science (Hons)

Thesis (M.Eng.(Mechanical Engineering)--University of Pretoria, 2001. / Summaries in Afrikaans and English. Includes bibliographical references.

Thesis (Ph. D.)--Ohio State University, 1986. / Includes bibliographical references (leaves 166-170). Available online via OhioLINK's ETD Center

Master of Science / Department of Computer Science / Daniel Andresen / Modern techniques for simulating granular matter can produce excellent quality simulations, but usually involve a great enough performance cost to render them ineffective for real time applications. This leaves something to be desired for low-cost systems and interactive simulations which are more forgiving to inaccurate simulations, but much more strict in regards to the performance of the simulation itself. What follows is a proposal for a method of simulating granular matter that could potentially support millions of particles and several types for each particle while maintaining acceptable frame rates on consumer level hardware. By leveraging the power of consumer level graphics cards, effective data representation, and a model built around Cellular Automata a simulation can be run in real time.

This thesis deals with the mathematical idealization denoted cellular automata (CA) and the applicability of this method to structural mechanics. When using CA, all aspects such as space and time are discrete. This discrete nature of CA allows for ease of interaction with digital computers, while physical phenomena which are essentially discrete in nature can be simulated in a realistic way. The application of such a novel numerical method opens up new possibilities in structural analysis. In this study, the fundamentals of CA are studied to determine how the parameters of the method are to be evaluated and applied to the established field of structural analysis. Attention is given to the underlying mathematics of structural mechanics, as well as approximate methods currently used in structural analysis, e.g. the finite element method (FEM) and the boundary element method (BEM). For structural simulations performed with the CA implemented in this study, machine learning based on a genetic algorithm (GA) is used to determine optimum rules for the CA, using finite element, boundary element and analytical approximations as the basis for machine learning. Rather unconventionally, symmetric problems in structural analysis are analyzed using asymmetric rules in the machine learning process, where the symmetry of the solution found is used as a quantitative indication of the quality of the solution. It is demonstrated that the quality of the asymmetric rules is superior to the quality of symmetric rules, even for those problems that are symmetric in nature. Finally, exploiting the inherent parallelism of CA, it is shown that distributed computing can greatly improve the efficiency of the CA simulation, even though the speed-up factor is not necessarily proportional to the number of sub lattices used. The distributed computing device itself is constructed by combining 18 obsolete Pentium computers in a single cluster. In terms of CPU performance the constructed distributed computer is not state-of-art, but it is constructed with no hardware costs whatsoever. In addition, the software used in assembling the cluster is in the public domain, and is also available free of charge. Such a parallel configuration is also known as the poor man’s computer. However, faster and more modern machines can simply be added to the existing cluster as and when they become available. While CA are recent additions to the “tools” used in structural analysis, increased use of CA as distributed computing becomes more widely available is envisaged, even though the CA rules are at this stage not transferable between different problems or even between meshes of varying refinement for a given problem. / Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2006. / Mechanical and Aeronautical Engineering / unrestricted

Multigrid acceleration is typically used for the iterative solution of partial differential equations in physics and engineering. A typical multigrid implementation uses a base discretization method, such as finite elements or finite differences, and a set of successively coarser grids that is used for accelerating the convergence of the iterative solution on the base grid. The presented thesis extends the use of multigrid acceleration to the design optimization of a sample structural system and demonstrates it within the context of the recently introduced Cellular Automata paradigm for design optimization. Within the design context, the multigrid scheme is not only used for accelerating the analysis iterations, but is also used to help refine the design across multiple grid levels to accelerate the design convergence. A comparison of computational efficiencies achieved by different multigrid implementations, including the multigrid accelerated nested design iteration scheme, is presented. The method is described in its generic form which can be applicable not only to the Cellular Automata paradigm but also to more general finite element analysis based design schemes as well. / Master of Science

The purpose of this study was to estimate the drivers' travel time with the occurrence of an incident on freeway. Three approaches, which were shock wave analysis, queuing theory and cellular automata models, were initially considered, however, the first two macroscopic models were indicated to underestimate travel time by previous literature. A microscopic simulation model based on cellular automata was developed to attain the goal. The model incorporated driving behaviors on the freeway with the presence of on-ramps, off-ramps, shoulder lanes, bottlenecks and incidents. The study area was a 16 mile eastbound section of I-66 between US-29 and I-495 in northern Virginia. The data for this study included loop detector data and incident data for the road segment for the year 2007. Flow and speed data from the detectors were used for calibration using quantitative and qualitative techniques. The cellular automata model properly reproduced the traffic flow under normal conditions and incidents. The travel time information was easily obtained from the model. The system is promising for travel time estimation in near real time. / Master of Science