Many of the grid compute resources (CPU cycles, network bandwidths, computing power, processor times, and software) exist as non-storable commodities, which we call grid compute commodities (gcc) and are distributed geographically across organizations. These organizations have dissimilar resource compositions and usage policies, which makes pricing grid resources and guaranteeing their availability a challenge. Several initiatives (Globus, Legion, Nimrod/G) have developed various frameworks for grid resource management. However, there has been a very little effort in pricing the resources. In this thesis, we propose financial option based model for pricing grid resources by devising three research threads: pricing the gcc as a problem of real option, modeling gcc spot price using a discrete time approach, and addressing uncertainty constraints in the provision of Quality of Service (QoS) using fuzzy logic. We used GridSim, a simulation tool for resource usage in a Grid to experiment and test our model. To further consolidate our model and validate our results, we analyzed usage traces from six real grids from across the world for which we priced a set of resources. We designed a Price Variant Function (PVF) in our model, which is a fuzzy value and its application attracts more patronage to a grid that has more resources to offer and also redirect patronage from a grid that is very busy to another grid. Our experimental results show that the application of the PVF has helped achieve equilibrium between users satisfaction measured as QoS and recovery of the infrastructure investment made by the providers. In the absence of pricing benchmarks, we setup Commodity Base Prices (CBP) and then integrated our PVF and CBP with GridSim to price grid compute resources. In summary, this thesis provides the design of a model to price grid compute resources using financial options theory. The model achieves mutual benefit for users and providers in the grid environment. The mutual benefit is expressed in terms of QoS to the users and recovery of investments on the grid infrastructure for the providers. This thesis has opened up many different opportunities for further research especially in the era of enterprise computing with clouds.

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Description logics (DLs) are used to represent knowledge of an application domain and provide standard reasoning services to infer consequences of this knowledge. However, classical DLs are not suited to represent vagueness in the description of the knowledge. We consider a combination of DLs and Fuzzy Logics to address this task. In particular, we consider the t-norm-based semantics for fuzzy DLs introduced by Hájek in 2005. Since then, many tableau algorithms have been developed for reasoning in fuzzy DLs. Another popular approach is to reduce fuzzy ontologies to classical ones and use existing highly optimized classical reasoners to deal with them. However, a systematic study of the computational complexity of the different reasoning problems is so far missing from the literature on fuzzy DLs. Recently, some of the developed tableau algorithms have been shown to be incorrect in the presence of general concept inclusion axioms (GCIs). In some fuzzy DLs, reasoning with GCIs has even turned out to be undecidable. This work provides a rigorous analysis of the boundary between decidable and undecidable reasoning problems in t-norm-based fuzzy DLs, in particular for GCIs. Existing undecidability proofs are extended to cover large classes of fuzzy DLs, and decidability is shown for most of the remaining logics considered here. Additionally, the computational complexity of reasoning in fuzzy DLs with semantics based on finite lattices is analyzed. For most decidability results, tight complexity bounds can be derived.

Technology such as television, computers, and video games are often in the line for reasons of why people lack physical activity and tend to gain weight and become obese. In the case of video games, with the advent of the so called “serious games initiative”, a new breed of video games have come into place. Such games are called “exergames” and they are intended to motivate the user to do physical activity. Although there is some evidence that some types of Exergames are more physically demanding than traditional sedentary games, there is also evidence that suggests that such games are not really providing the intensity of exert that is at the recommended levels for a daily exercise. Currently, most exergames have a passive approach. There is no real tracking of the players progress, there is no assessment of his/her level of exert, no contextual information, and there is no adaptability on the game itself to change the conditions of the game and prompt the desired physiological response on the player. In this thesis we present research work done towards the design and development of an architecture and related systems that support a shift in the exertion game paradigm. The contributions of this work are enablers in the design and development of exertion games with a strict serious game approach. Such games should have “exercising” as the primary goal, and a game engine that has been developed under this scheme should be aware of the exertion context of the player. The game should be aware of the level of exertion of the player and adapt the gaming context (in-game variables and exertion interface settings) so that the player can reach a predefined exertion rate as desired. To support such degree of adaptability in a multimedia, multimodal system, we have proposed a system architecture that lays down the general guidelines for the design and development of such systems.

In this thesis a Proactive Risk-Aware Robotic Sensor Network (RSN) is proposed for the application of Critical Infrastructure Protection (CIP). Each robotic member of the RSN is granted a perception of risk by means of a Risk Management Framework (RMF). A fuzzy-risk model is used to extract distress-based risk features and potential intrusion-based risk features for CIP. Detected high-risk events invoke a fuzzy-auction Multi-Robot Task Allocation (MRTA) algorithm to create a response group for each detected risk. Through Evolutionary Multi-Objective (EMO) optimization, a Pareto set of optimal robot configurations for a response group will be generated using the Non-Dominating Sorting Genetic Algorithm II (NSGA-II). The optimization objectives are to maximize sensor coverage of essential spatial regions and minimize the amount of energy exerted by the response group. A set of non-dominated solutions are produced from EMO optimization for a decision maker to select a single response. The RSN response group will re-organize based on the specifications of the selected response.

Many of the grid compute resources (CPU cycles, network bandwidths, computing power, processor times, and software) exist as non-storable commodities, which we call grid compute commodities (gcc) and are distributed geographically across organizations. These organizations have dissimilar resource compositions and usage policies, which makes pricing grid resources and guaranteeing their availability a challenge. Several initiatives (Globus, Legion, Nimrod/G) have developed various frameworks for grid resource management. However, there has been a very little effort in pricing the resources. In this thesis, we propose financial option based model for pricing grid resources by devising three research threads: pricing the gcc as a problem of real option, modeling gcc spot price using a discrete time approach, and addressing uncertainty constraints in the provision of Quality of Service (QoS) using fuzzy logic. We used GridSim, a simulation tool for resource usage in a Grid to experiment and test our model. To further consolidate our model and validate our results, we analyzed usage traces from six real grids from across the world for which we priced a set of resources. We designed a Price Variant Function (PVF) in our model, which is a fuzzy value and its application attracts more patronage to a grid that has more resources to offer and also redirect patronage from a grid that is very busy to another grid. Our experimental results show that the application of the PVF has helped achieve equilibrium between users satisfaction measured as QoS and recovery of the infrastructure investment made by the providers. In the absence of pricing benchmarks, we setup Commodity Base Prices (CBP) and then integrated our PVF and CBP with GridSim to price grid compute resources. In summary, this thesis provides the design of a model to price grid compute resources using financial options theory. The model achieves mutual benefit for users and providers in the grid environment. The mutual benefit is expressed in terms of QoS to the users and recovery of investments on the grid infrastructure for the providers. This thesis has opened up many different opportunities for further research especially in the era of enterprise computing with clouds.

The thesis presents comparative study of modern control methods for regulating under-actuates systems. The classical pendulum on a cart system, a 2-DOF under-actuated system was utilized as a benchmark system for analyzing controllers' performances. The work includes analysis, controller design and swing-up control simulation for the following three methods: Nonlinear state-space control The mathematical model based on the Lie theoretic approach with nonlinear output injection was developed. The controller accomplishes both swing-up and stabilization of the pendulum. The feasibility of the implementation is limited due to physical constraints (length of the cart rail). Fuzzy logic control A Sugeno type fuzzy inference engine implementing the control strategy was built over the system fuzzy model. The simulations showed successful controller behavior but the generated system control input exhibited some non-smoothness that could cause increase on the actuator demand. Method of embedded artificial constraints (EAC). The analysis reveals dependence between the pendulum motion and the cart acceleration. Using this artificial constraint a state link was developed and the nonlinear control problem was reduced to a linear controller design. A stabilizing linear state-space controller has been developed and methods for arbitrary pole placement and optimal linear quadratic regulator design were compared. A reduced order current estimator for velocity estimation has been studied and implemented. A software client/server controller application running on a QNX Neutrino 6.1 platform was developed. The real-time experiments conducted with the EAC/linear state-space controller confirmed good swing-up and stabilization system performance.

In the modern automotive industry, more and more manufacturers recognise that vehicle paint appearance makes an important contribution to customer satisfaction. Attractive appearance has become one of the important factors for customers in making a decision to purchase a car. Objective measurement of the quality of autobody paint appearance, as perceived by the customer, in a repeatable, reproducible, continuous scale manner is an important requirement for improving the paint appearance. It can provide car manufacturers a standard reference to evaluate the quality of the paint appearance. This thesis mainly deals with the measurement of paint appearance quality in the automotive industry by investigating, identifying and developing measurement methods in this area. First of all, the 'state of the art' in the area of paint appearance measurement was presented, which summarised the concept of appearance, models, attributes and definitions. To further identify the parameters and instruments used in the automotive industry, a round robin test was launched to perform visual assessment and instrument measurements on a set of panels in some European car manufacturers. A summary of the correlation found between measurable parameters and visual assessment provided the basis of the further work. Based on the literature survey and round robin test results, the next work is mainly concentrated on the two most important parameters, 'orange peel' and 'metal texture effect', how to separate and evaluate them. Digital signal processing technique, FFT and Filtering, have been employed to separate them and a set of measures have been provided for evaluation. At the same time, the technique for texture pattern recognition was introduced to evaluate the texture effect when a fine texture comparison was needed. A set of computable textural parameters based on grey-tone spatial-dependence matrices gives good correlation directly corresponding to visual perception. To resolve the overall appearance modelling problem, two novel and more powerful modelling tools, artificial neural networks and fuzzy logic, are introduced to model the overall appearance. The test results showed that both of them are able to reflect the correlation between overall appearance and the major parameters measured from a painted surface. Finally, an integrated measurement system, 'Smart Appearance', was developed using the image processing techniques and the artificial neural network model. The implement results show that this system can measure the major attributes of paint appearance and provide an overall appearance index corresponding to human visual perception. This system is helpful to product quality control on car body paint. It also could be used on the paint production line for dynamic measurement.

The primary aim of this research is to investigate methods of improving the effectiveness of current information retrieval systems. This aim can be achieved by accomplishing numerous supporting objectives. A foundational objective is to introduce a novel bidirectional, symmetrical fuzzy logic theory which may prove valuable to information retrieval, including internet searches of distributed data objects. A further objective is to design, implement and apply the novel theory to an experimental information retrieval system called ANACALYPSE, which automatically computes the relevance of a large number of unseen documents from expert relevance feedback on a small number of documents read. A further objective is to define a methodology used in this work as an experimental information retrieval framework consisting of multiple tables including various formulae which anow a plethora of syntheses of similarity functions, ternl weights, relative term frequencies, document weights, bidirectional relevance feedback and history adjusted term weights. The evaluation of bidirectional relevance feedback reveals a better correspondence between system ranking of documents and users' preferences than feedback free system ranking. The assessment of similarity functions reveals that the Cosine and Jaccard functions perform significantly better than the DotProduct and Overlap functions. The evaluation of history tracking of the documents visited from a root page reveals better system ranking of documents than tracking free information retrieval. The assessment of stemming reveals that system information retrieval performance remains unaffected, while stop word removal does not appear to be beneficial and can sometimes be harmful. The overall evaluation of the experimental information retrieval system in comparison to a leading edge commercial information retrieval system and also in comparison to the expert's golden standard of judged relevance according to established statistical correlation methods reveal enhanced system information retrieval effectiveness.

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