Thesis (MTech (Electrical Engineering))--Peninsula Technikon, 2002. / Word processed copy. Summary in English. Includes bibliographical references (leaves 170-179). Also available online.

Thesis (Ph. D.)--West Virginia University, 2003. / Title from document title page. Document formatted into pages; contains ix, 110 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 90-94).

The Objective of this thesis is to talk about the usage of Fuzzy Logic in pattern recognition. There are different fuzzy approaches to recognize the pattern and the structure in data. The fuzzy approach that we choose to process the data is completely depends on the type of data. Pattern reorganization as we know involves various mathematical transforms so as to render the pattern or structure with the desired properties such as the identification of a probabilistic model which provides the explaination of the process generating the data clarity seen and so on and so forth. With this basic school of thought we plunge into the world of Fuzzy Logic for the process of pattern recognition. Fuzzy Logic like any other mathematical field has its own set of principles, types, representations, usage so on and so forth. Hence our job primarily would focus to venture the ways in which Fuzzy Logic is applied to pattern recognition and knowledge of the results. That is what will be said in topics to follow. Pattern recognition is the collection of all approaches that understand, represent and process the data as segments and features by using fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved. In the broadest sense, pattern recognition is any form of information processing for which both the input and output are different kind of data, medical records, aerial photos, market trends, library catalogs, galactic positions, fingerprints, psychological profiles, cash flows, chemical constituents, demographic features, stock options, military decisions.. Most pattern recognition techniques involve treating the data as a variable and applying standard processing techniques to it.

published_or_final_version / abstract / toc / Earth Sciences / Master / Master of Philosophy

A lot of work has been done in developing energy efficient network and user devices to reduce the power consumption of nodes and devices in networks. This thesis proposes an innovative approach using fuzzy logic for power saving and extending the life time of network nodes and user devices. Using the concept of silent networking we will define an actionable silent period-this is the period during which the network node or the user device does not expect to originate, receive or relay any traffic. The decision of switching the network interface or user device in the silent mode depends on the history of the network activity. Secondly, if the actionable silent period is high enough, then we can switch the entire interface in power down mode leaving just the timer ON to wake up the interface at the end of silent period. Fuzzy logic is used in mapping the history of the network interface and based on the fuzzy rules that we define, the actionable silent period for interfaces is formulated. Experimental analysis using simulations has been done to view the power saving that can be achieved using this method. Furthermore, a methodology for extending the lifetime of the networked devices is formulated. Using this innovative approach we can save a considerable amount of energy and proportionally increase the lifetime of the networked devices.

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