Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Electrical & Computer Engineering, 2005. / Includes bibliographical references.

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2006. / Thesis Advisor(s): Monique P. Furgues, Roberto Cristi, Carlos Borges. "September 2006." Includes bibliographical references (p. 133-136). Also available in print.

Thesis (Ph.D.)--Montana State University--Bozeman, 2006. / Typescript. Chairperson, Graduate Committee: Tomás̆ Gedeon. Includes bibliographical references (leaves 84-86).

Thesis (M. Phil.)--City University of Hong Kong, 2005. / "Submitted to Department of Computer Engineering and Information Technology in partial fulfillment of the requirements for the degree of Master of Philosophy." Includes bibliographical references (leaves 54-58).

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 93-94). Also available in electronic version.

The study of cycles in the context of economic time series has been active for many decades, if not centuries; however, it was only in recent decades that more formal approaches for identifying cycles have been developed. Litvine and Bismans (2015) proposed a new approach for dating cycles in financial time series, for purposes of optimising buysell strategies. In this approach, cycle dating is presented as an optimisation problem. They also introduced a method for optimising this problem, known as the hierarchical method (using full evaluation 2, or HR-FE2). However, this method may be impractical for large data sets as it may require unacceptably long computation time. In this study, new procedures that date cycles using the approach proposed by Litvine and Bismans (2015), were introduced, and were speciffically developed to be feasible for large time series data sets. These procedures are the stochastic generation and adaptation (SGA), buy-sell adapted Extrema importance identity sequence retrieval (BSA-EIISR) and buysell adapted bottom-up (BSA-BU) methods. An existing optimisation technique, known as particle swarm optimisation (PSO), was also employed. A statistical comparison was then made between these methods, including HR-FE2. This involved evaluating, on simulated data, the performance of the algorithms in terms of objective function value and computation time on different time series lengths, Hurst exponent, and number of buy-sell points. The SRace methodology (T. Zhang, Georgiopoulos, and Anagnostopoulos 2013) was then applied to these results in order to determine the most effcient methods. It was determined that, statistically, SGA, BSA-EIISR and BSA-BU are the most effcient methods. Number of buysell points was found to have the largest effect on relative performance of these methods. In some cases, the Hurst exponent also has a small effect on relative performance.

Graphs have become very crucial as they can represent a wide variety of systems in different areas. One interesting structure called community in graphs has attracted considerable attention from both academia and industry. Community detection is meaningful, but typically hard in arbitrary networks. A lot of research has been done based on structural information, but we would like to find communities which are not only cohesive but also influential or important. A k-influential community model based on k-core provided by Li, Qin, Yu, and Mao is helpful to discover these cohesive and important communities. They organize the problem as finding top-r most important communities in a given graph. In this thesis, our goal is to detect top-r most important communities using efficient and memory-saving algorithms running on a consumer-grade machine. We analyze two existing algorithms, then propose multiple new efficient algorithms for this problem. To test their performance, we conduct extensive experiments on some real-world graph datasets. Experimental results show that our algorithms are able to compute top-r most important communities within a very reasonable amount of time and space in a consumer-grade machine. / Graduate

Two methods for identifying multiple-input mutliple-output linear time-invariant discrete systems from input-output data are derived. The selector matrix principle of Gopinath is used and two special classes of selector matrices that lead to canonical system models are introduced. Very general input sequences can be applied. However, a few restrictions exist for the initial system state. The input matrix is identified columnwise. Both methods are considerably better than Gopinath's method in terms of storage requirements and numerical accuracy. Tests on a large computer are performed. One method is implemented on a minicomputer with good results. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

Uncertainty is prevalent in diverse datasets. This uncertainty is modelled by a string generalization termed as indeterminate string – a string containing one or more sub- sets of the alphabet as letters (known as indeterminate letters). In this thesis we revisit the pattern matching problem on indeterminate strings. We introduce innovative algorithms leveraging established techniques like KMP and BM, coupled with an exhaustive experimental evaluation focusing on both time complexity and runtime performance. Additionally, the thesis explores a novel encoding methodology for indeterminate strings, assessing its impact on runtime efficiency. Through rigorous analysis and experimentation, this study not only expands the theoretical framework of indeterminate pattern matching but also provides practical insights that will impact data processing in real-world applications. / Thesis / Master of Science (MSc) / In my thesis, I proposed novel algorithms for pattern matching on indeterminate strings — special strings that allow character uncertainties at specific positions. By addressing uncertainties in character positions, my work has implications in computa- tional biology, data mining, and various applications, with more precise and efficient pattern recognition in real-world scenarios.

Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Mavris, Dimitri; Committee Member: Costello, Mark; Committee Member: German, Brian. Part of the SMARTech Electronic Thesis and Dissertation Collection.