Applying human-like intelligence to future generation network to improve communication efficiency

 

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dc.contributor.advisor Chan, H Anthony en_ZA
dc.contributor.author Li, Yang en_ZA
dc.date.accessioned 2014-07-31T10:55:09Z
dc.date.available 2014-07-31T10:55:09Z
dc.date.issued 2007 en_ZA
dc.identifier.citation Li, Y. 2007. Applying human-like intelligence to future generation network to improve communication efficiency. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/5183
dc.description Includes abstract.
dc.description Includes bibliographical references (leaves 251-257).
dc.description.abstract In recent decades, communications network has evolved at drastic speed to provide advanced and intelligent services. This strengthening service provision owes to the successful establishment of various intelligent networks and the use of artificial intelligence, pervasive computing, and social networking in communications. It has consequently endowed network users with abundant choices of communication services. While these communications services are bringing convenience to human lives, people in turn are performing more tasks. The current network with its large number of available communications services is then often burdening network users with the complexity and inflexibility in using these services. In particular, the network lacks the initiative and the ability to investigate a user’s most recent communication needs and subsequently adjust the manner of service provision according to these needs and user connecting possibilities. The network needs to be more intelligent to handle these problems. We therefore propose importing human-like intelligence into the network to facilitate communication-session processing according to user needs. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Electrical Engineering en_ZA
dc.title Applying human-like intelligence to future generation network to improve communication efficiency en_ZA
dc.type Thesis / Dissertation en_ZA
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Engineering & the Built Environment en_ZA
dc.publisher.department Department of Electrical Engineering en_ZA
dc.type.qualificationlevel Doctoral en_ZA
dc.type.qualificationname PhD en_ZA
uct.type.filetype Text
uct.type.filetype Image


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