A dissertation submitted to the Faculty of Engineering, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering / A multi-layered expert system shell that specifically addresses real-time issues is designed and implemented. The architecture of this expert system shell supports the concepts of parallelism, concurrent computation and competitive reasoning in that it allows several alternatives to be explored simultaneously. An inference engine driven by a hybrid of forward and backward chanining methods is used to achieve real-time response, and certainty factors are used for uncertainty management. Real-time responsiveness is improved by allowing the coexistence of procedural and declarative knowledge within the same system. A test bed that was set up in order to investigate the performance of the implemented shell is described. It was found in the performance analysis that the proposed system meets the real-time requirements as specified in this research. / Andrew Chakane 2018

Cloud Computing is a new computing model consists of a large pool of hardware and software resources on remote datacenters that are accessed through the Internet. Cloud Computing faces significant obstacles to its acceptance, such as security, virtualization, and lack of standardization. For Cloud standards, there is a long debate about their role, and more demands for Cloud standards are put on the table. The Cloud standardization landscape is so ambiguous. To model and analyze security standards for Cloud Computing and web services, we have surveyed Cloud standards focusing more on the standards for security, and we classified them by groups of interests. Cloud Computing leverages a number of technologies such as: Web 2.0, virtualization, and Service Oriented Architecture (SOA). SOA uses web services to facilitate the creation of SOA systems by adopting different technologies despite their differences in formats and protocols. Several committees such as W3C and OASIS are developing standards for web services; their standards are rather complex and verbose. We have expressed web services security standards as patterns to make it easy for designers and users to understand their key points. We have written two patterns for two web services standards; WS-Secure Conversation, and WS-Federation. This completed an earlier work we have done on web services standards. We showed relationships between web services security standards and used them to solve major Cloud security issues, such as, authorization and access control, trust, and identity management. Close to web services, we investigated Business Process Execution Language (BPEL), and we addressed security considerations in BPEL and how to enforce them. To see how Cloud vendors look at web services standards, we took Amazon Web Services (AWS) as a case-study. By reviewing AWS documentations, web services security standards are barely mentioned. We highlighted some areas where web services security standards could solve some AWS limitations, and improve AWS security process. Finally, we studied the security guidance of two major Cloud-developing organizations, CSA and NIST. Both missed the quality of attributes offered by web services security standards. We expanded their work and added benefits of adopting web services security standards in securing the Cloud. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2013.

For an 8-bit grayscale image patch of size n x n, the number of distinguishable signals is 256(n2). Natural images (e.g.,photographs of a natural scene) comprise a very small subset of these possible signals. Traditional image and video processing relies on band-limited or low-pass signal models. In contrast, we will explore the observation that most signals of interest are sparse, i.e. in a particular basis most of the expansion coefficients will be zero. Recent developments in sparse modeling and L1 optimization have allowed for extraordinary applications such as the single pixel camera, as well as computer vision systems that can exceed human performance. Here we present a novel neural network architecture combining a sparse filter model and locally competitive algorithms (LCAs), and demonstrate the networks ability to classify human actions from video. Sparse filtering is an unsupervised feature learning algorithm designed to optimize the sparsity of the feature distribution directly without having the need to model the data distribution. LCAs are defined by a system of di↵erential equations where the initial conditions define an optimization problem and the dynamics converge to a sparse decomposition of the input vector. We applied this architecture to train a classifier on categories of motion in human action videos. Inputs to the network were small 3D patches taken from frame di↵erences in the videos. Dictionaries were derived for each action class and then activation levels for each dictionary were assessed during reconstruction of a novel test patch. We discuss how this sparse modeling approach provides a natural framework for multi-sensory and multimodal data processing including RGB video, RGBD video, hyper-spectral video, and stereo audio/video streams. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

by Chiu Kwok-yuan, Edward & Man Kin-wah, Andy. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1988. / Bibliography: leaves 107-108.

by Teresa W.M. Ng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1989. / Bibliography: leaves 97-100.

by Tam Chi-Fai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 87-88). / abstract --- p.I / table of content --- p.II / list of figures --- p.V / acknowledgments --- p.VII / Chapter chapter 1 --- introduction --- p.1 / Chapter 1.1 --- Two Stage Operations Planning --- p.1 / Chapter 1.2 --- Iterative Activities in the Two Stage Planning Approach --- p.3 / Chapter 1.3 --- Expert Decision Support System for Two Stage Planning --- p.4 / Chapter 1.4 --- Scope of the Study --- p.5 / Chapter 1.5 --- Organization of the Thesis --- p.6 / Chapter chapter 2 --- literature review --- p.7 / Chapter 2.1 --- Network Design for Air Express Service --- p.7 / Chapter 2.2 --- Integrative Use of Optimization and Simulation Model --- p.8 / Chapter 2.3 --- Expert System & Decision Support System --- p.11 / Chapter 2.3.1 --- Expert System --- p.11 / Chapter 2.3.2 --- Decision Support System --- p.13 / Chapter 2.3.3 --- ES / DSS Integration --- p.14 / Chapter chapter 3 --- research methodology --- p.19 / Chapter 3.1 --- Review on DSS / ES Integration --- p.19 / Chapter 3.2 --- System Design --- p.20 / Chapter 3.3 --- Prototyping --- p.22 / Chapter 3.4 --- Analysis and Evaluation --- p.23 / Chapter chapter 4 --- system architecture and knowledge modeling --- p.24 / Chapter 4.1 --- Architecture Overview --- p.24 / Chapter 4.1.1 --- System Architecture and Interactions --- p.26 / Chapter 4.1.2 --- Decision Support System --- p.27 / Chapter 4.1.3 --- Expert System --- p.32 / Chapter 4.2 --- System Operations --- p.35 / Chapter 4.2.1 --- Operations Flow --- p.35 / Chapter chapter 5 --- case study and prototyping --- p.38 / Chapter 5.1 --- Case Background --- p.38 / Chapter 5.1.1 --- The Service Network --- p.38 / Chapter 5.1.2 --- Objectives of the Project --- p.40 / Chapter 5.1.3 --- Network Design Methodology --- p.41 / Chapter 5.2 --- Iterative Network Planning --- p.49 / Chapter 5.2.1 --- Multi-period Network Planning Feedback --- p.50 / Chapter 5.2.2 --- Feedback in Validation and Evaluation --- p.51 / Chapter 5.3 --- The System Prototype --- p.57 / Chapter 5.3.1 --- Data Management and Model Manipulation --- p.57 / Chapter 5.3.2 --- Intelligent Guidance for the Iterations --- p.65 / Chapter chapter 6 --- evaluation and analysis --- p.75 / Chapter 6.1 --- Test Scenario for Network Planning --- p.75 / Chapter 6.1.1 --- Consultation Process --- p.75 / Chapter 6.1.2 --- Consultation Results --- p.78 / Chapter 6.2 --- Effectiveness of EDSS in Network Planning --- p.81 / Chapter 6.3 --- Generalized Advancement and Limitation --- p.82 / Chapter chapter 7 --- conclusion --- p.85 / bibliography --- p.87 / appendices --- p.89

Yau Po Chung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 91-92). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background --- p.3 / Chapter 2.1 --- Temporal Theories --- p.3 / Chapter 2.2 --- Related Works --- p.4 / Chapter 2.2.1 --- Consistency and Satisfiability of Timing Specifications --- p.4 / Chapter 2.2.2 --- Symbolic Constraint Satisfaction --- p.5 / Chapter 3 --- Previous Developed Work --- p.7 / Chapter 3.1 --- Previous Problem Domain --- p.7 / Chapter 3.1.1 --- Basics of MC68000 Read Cycle --- p.7 / Chapter 3.2 --- Knowledge-based System Structure --- p.9 / Chapter 3.3 --- Diagnostic Reasoning Mechanisms --- p.10 / Chapter 3.4 --- Time Range Approach --- p.11 / Chapter 3.4.1 --- Time Range Representation --- p.11 / Chapter 3.4.2 --- Constraint Satisfaction of Time Ranges --- p.12 / Chapter 3.4.3 --- Constraint Propagation of Time Ranges --- p.13 / Chapter 3.5 --- Fuzzy Time Point Approach --- p.14 / Chapter 3.5.1 --- Fuzzy Time Point Models --- p.14 / Chapter 3.5.2 --- Definition of Fuzzy Time Points --- p.15 / Chapter 3.5.3 --- Constraint Propagation of Fuzzy Time Points --- p.17 / Chapter 3.5.4 --- Constraint Satisfaction of Fuzzy Time Points --- p.18 / Chapter 4 --- The Proposed Segmented Time Range Approach --- p.20 / Chapter 4.1 --- Introduction --- p.20 / Chapter 4.2 --- The Insufficiency of The Existing Time Range Approach --- p.22 / Chapter 4.3 --- Segmented Time Range Approach --- p.23 / Chapter 4.3.1 --- The Representation --- p.23 / Chapter 4.3.2 --- Constraint Propagation and Satisfaction --- p.25 / Chapter 4.3.3 --- Contributions --- p.25 / Chapter 4.3.4 --- Limitations --- p.29 / Chapter 4.4 --- Conclusion --- p.30 / Chapter 5 --- New Problem Domain and Our New System --- p.31 / Chapter 5.1 --- Introduction --- p.31 / Chapter 5.2 --- Pentium-SRAM Interfacing Problem --- p.31 / Chapter 5.2.1 --- Asynchronous SRAM Solution --- p.32 / Chapter 5.2.2 --- Synchronous SRAM Solution --- p.33 / Chapter 5.3 --- The Knowledge Base --- p.35 / Chapter 5.4 --- Characteristics of Our New System --- p.35 / Chapter 6 --- Burst Read Cycle --- p.37 / Chapter 6.1 --- Introduction --- p.37 / Chapter 6.2 --- Asynchronous SRAM Solution --- p.37 / Chapter 6.2.1 --- Implementation --- p.39 / Chapter 6.2.2 --- Implementation Results --- p.45 / Chapter 6.3 --- Synchronous SRAM Solution --- p.48 / Chapter 6.3.1 --- Implementation --- p.49 / Chapter 6.3.2 --- Implementation Results --- p.56 / Chapter 6.4 --- Conclusion --- p.58 / Chapter 7 --- Burst Write Cycle --- p.60 / Chapter 7.1 --- Introduction --- p.60 / Chapter 7.2 --- Asynchronous SRAM Solution --- p.60 / Chapter 7.2.1 --- Implementation --- p.61 / Chapter 7.2.2 --- Implementation Results --- p.67 / Chapter 7.3 --- Synchronous SRAM Solution --- p.71 / Chapter 7.3.1 --- Implementation --- p.71 / Chapter 7.3.2 --- Implementation Results --- p.79 / Chapter 7.4 --- Conclusion --- p.82 / Chapter 8 --- Conclusion --- p.83 / Chapter 8.1 --- Summary of Achievements --- p.83 / Chapter 8.2 --- Future Development --- p.86 / Appendix Some Characteristics of Our New System --- p.89 / Bibliography --- p.91

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Preventing bad things from happening to engineered systems, demands improvements to how we model their operation with regard to safety. Safety-critical and fiscally-critical systems both demand automated and exhaustive verification, which is only possible if the models of these systems, along with the number of scenarios spawned from these models, are tractably finite. To this end, this dissertation ad dresses problems of a model's tractability and usefulness. It addresses the state space minimization problem by initially considering tradeoffs between state space size and level of detail or fidelity. It then considers the problem of human interpretation in model capture from system artifacts, by seeking to automate model capture. It introduces human control over level of detail and hence state space size during model capture. Rendering that model in a manner that can guide human decision making is also addressed, as is an automated assessment of system timeliness. Finally, it addresses state compression and abstraction using logical fault models like fault trees, which enable exhaustive verification of larger systems by subsequent use of transition fault models like Petri nets, timed automata, and process algebraic expressions. To illustrate these ideas, this dissertation considers two very different applications - web service compositions and submerged ocean machinery. / by John C. Sloan. / Thesis (Ph.D.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.