Thesis (M.S.)--Ohio University, March, 2006. / Title from PDF t.p. Includes bibliographical references (p. 80-82)

Thesis (M.Eng.)--Memorial University of Newfoundland, 2003. / Bibliography: leaves 99-106. Also available online.

Thesis (M. Phil.)--University of Hong Kong, 2005. / Title proper from title frame. Also available in printed format.

This report presents the results of a thesis project that surveys and designs about the issue “Feature Model for Adaptive Computing”. In this project, there are two main issues, first one is about the Feature Modeling, and the second is how to use this Feature Modeling for adaptive computing. In this thesis report, at the beginning, we present the problem we expected to solve and introduce some background information, including the knowledge of feature model and adaptive computing. Then we explain our solution and evaluate this solution. At the end of this report, we give a short conclusion about our thesis project and feature work.

D.Phil. (Electrical and Electronic Engineering) / The complexity of multi-objective functions and diverse variables involved with optimization of parallel manipulator or parallel kinematic machine design has inspired the research conducted in this thesis to investigate techniques that are suitable to tackle this problem efficiently. Further the parallel manipulator dimensional synthesis problem is multimodal and has no explicit analytical expressions. This process requires optimization techniques which offer high level of accuracy and robustness. The goal of this work is to present method(s) based on Artificial Intelligence (AI) that may be applied in addressing the challenge stated above. The performance criteria considered include; stiffness, dexterity and workspace. The case studied in this work is a 6 degrees of freedom (DOF) parallel manipulator, particularly because it is considered much more complicated than the lesser DOF mechanisms, owing to the number of independent parameters or inputs needed to specify its configuration (i.e. the higher the DOFs, the more the number of independent variables to be considered). The first contribution in this thesis is a comparative study of several hybrid Multi- Objective Optimization (MOO) AI algorithms, in application of a parallel manipulator dimensional synthesis. Artificial neural networks are utilized to approximate a multiple function for the analytical solution of the 6 DOF parallel manipulator’s performance indices, followed by implementation of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as search algorithms. Further two hybrid techniques are proposed which implement Simulated Annealing and Random Forest in searching for optimum solutions in the Multi-objective Optimization problem. The final contribution in this thesis is ensemble machine learning algorithms for approximation of a multiple objective function for the 6 DOF parallel manipulator analytical solution. The results from the experiments demonstrated not only neural network (NN) but also other machine learning algorithms namely K- Nearest Neighbour (k-NN), M5 Prime (M5’), Zero R (ZR) and Decision Stump (DS) can effectively be implemented for the application of function approximation.

The major motivation for self-adaptive computing systems is the self-adjustment of the software according to a changing environment. Adaptive computing systems can add, remove, and replace their own components in response to changes in the system itself and in the operating environment of a software system. Although these systems may provide a certain degree of confidence against new environments, their structural and behavioral changes should be validated after adaptation occurs at runtime. Testing dynamically adaptive systems is extremely challenging because both the structure and behavior of the system may change during its execution. After self adaptation occurs in autonomic software, new components may be integrated to the software system. When new components are incorporated, testing them becomes vital phase for ensuring that they will interact and behave as expected. When self adaptation is about removing existing components, a predefined test set may no longer be applicable due to changes in the program structure. Investigating techniques for dynamically updating regression tests after adaptation is therefore necessary to ensure such approaches can be applied in practice. We propose a model-driven approach that is based on change propagation for synchronizing a runtime test model for a software system with the model of its component structure after dynamic adaptation. A workflow and meta-model to support the approach was provided, referred to as Test Information Propagation (TIP). To demonstrate TIP, a prototype was developed that simulates a reductive and additive change to an autonomic, service-oriented healthcare application. To demonstrate the generalization of our TIP approach to be instantiated into the domain of up-to-date runtime testing for self-adaptive software systems, the TIP approach was applied to the self-adaptive JPacman 3.0 system. To measure the accuracy of the TIP engine, we consider and compare the work of a developer who manually identifyied changes that should be performed to update the test model after self-adaptation occurs in self-adaptive systems in our study. The experiments show how TIP is highly accurate for reductive change propagation across self-adaptive systems. Promising results have been achieved in simulating the additive changes as well.

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Genetic research projects currently can require enormous computing power to processes the vast quantities of data available. Further, DNA sequencing projects are generating data at an exponential rate greater than that of the development microprocessor technology; thus, new, faster methods and techniques of processing this data are needed. One common type of processing involves searching a sequence database for the most similar sequences. Here we present a distributed database search system that utilizes adaptive computing technologies. The search is performed using the Smith-Waterman algorithm, a common sequence comparison algorithm. To reduce the total search time, an initial search is performed using a version of the algorithm, implemented in adaptive computing hardware, which is designed to efficiently perform the initial search. A final search is performed using a complete version of the algorithm. This two-stage search, employing adaptive and distributed hardware, achieves a performance increase of several orders of magnitude over similar processor based systems. / Master of Science

Adaptive computing systems have proven useful for implementing a wide range of algorithms. A limitation of current systems is the relatively small amount of reconfigurable hardware resources. Many algorithms require more hardware resources than are available. One solution to this problem is runtime reconfiguration (RTR). Using RTR techniques, a large algorithm is implemented as a collection of configurations for the reconfigurable hardware. These configurations are loaded onto the reconfigurable hardware as necessary to implement the algorithm. A primary limitation of RTR is that the reconfiguration process is slow. Therefore, methods of decreasing reconfiguration time are desirable. Another method of implementing large algorithms on small hardware is to use multiple configurable computing platforms connected via a communication network. RTR techniques can be used in conjunction with this method to further increase hardware availability. In this case reconfiguration time is increased by the overhead of transmitting data across the communication network. Methods of decreasing network overhead are desirable. This thesis discusses the use of caching techniques to decrease reconfiguration time. An architecture for caching configurations is implemented on a configurable computing system platform. The use of caching to decrease network overhead is discussed and exhibited. An example application is implemented and used to evaluate the effects of caching on reconfiguration time and algorithm performance. / Master of Science