Thesis (M. Phil.)--University of Hong Kong, 1999. / Includes bibliographical references (leaves 123-127).

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2002. / Thesis advisor(s): Tri T. Ha, Jovan Lebaric. Includes bibliographical references (p. 63). Also available online.

Thesis (Ph. D.)--University of Chicago, Graduate School of Business. / Includes bibliographical references. Also available on the Internet.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 80-82). Also available via the Internet.

Magister Scientiae - MSc / Huge amounts of data are being collected as a result of the increased use of mobile telecommunications. Insight into information and knowledge derived from these databases can give operators a competitive edge in terms of customer care and retention, marketing and fraud detection. One of the strategies for fraud detection checks for signs of questionable changes in user behavior. Although the intentions of the mobile phone users cannot be observed, their intentions are reflected in the call data which define usage patterns. Over a period of time, an individual phone generates a large pattern of use. While call data are recorded for subscribers for billing purposes, we are making no prior assumptions about the data indicative of fraudulent call patterns, i.e. the calls made for billing purpose are unlabeled. Further analysis is thus, required to be able to isolate fraudulent usage. An unsupervised learning algorithm can analyse and cluster call patterns for each subscriber in order to facilitate the fraud detection process. This research investigates the unsupervised learning potentials of two neural networks for the profiling of calls made by users over a period of time in a mobile telecommunication network. Our study provides a comparative analysis and application of Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM) recurrent neural networks algorithms to user call data records in order to conduct a descriptive data mining on users call patterns. Our investigation shows the learning ability of both techniques to discriminate user call patterns; the LSTM recurrent neural network algorithm providing a better discrimination than the SOM algorithm in terms of long time series modelling. LSTM discriminates different types of temporal sequences and groups them according to a variety of features. The ordered features can later be interpreted and labeled according to specific requirements of the mobile service provider. Thus, suspicious call behaviours are isolated within the mobile telecommunication network and can be used to to identify fraudulent call patterns. We give results using masked call data from a real mobile telecommunication network. / South Africa

Thesis (M.B.A.)--University of Hong Kong, 1998. / Includes bibliographical references (leaf 148-152).

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2007. / The work presented in this Thesis relates to the increased capabilities of the mobile Internet and the possible use of cellphones as an enhancement to video surveillance systems. The focus of the Thesis is on the delivery of live video content to Java enabled cellphones. The various characteristics, capabilities and limitations of the mobile networks- and phones are investigated. Various options for streaming video content to cellphones are also explored. The design and implementation of a digital surveillance system with the ability to stream live video to a cellphone is presented. Two versions of the streaming protocol are developed and implemented in cellphone applications, with which the video stream can be viewed. An evaluation and real-life testing of the applications are presented. Recommendations regarding further enhancements are provided.

D.Phil. / A modern day N-P complete problem is the assigning of frequencies to transmitters in a cellular network in such a manner that, ideally, no two transmitters in the same cell or neighbouring cells use the same frequency. Considering that an average cellular network provider has over 29 000 transmitters and only 55 frequencies, choosing these frequencies in an optimal way is a very difficult computational problem. Swarm intelligence allows the acceptable minimization and optimization of the frequency assignment problem (FAP). Swarm intelligence is a concept modelling the processes in natural systems such as ant colonies, beehives, human immune systems and the human brain. These systems are selforganizational and display high efficiency in the execution of their tasks. A number of simple automated agents interacting with each other and the environment form a collective. Specifically, there is no "central agent" directing the others. A collective can display surprising intelligence which emerges out of the interaction of the individual agents. This collective intelligence, referred to as swarm intelligence, is displayed in ant colonies when ants build elaborate nests, regulate nest temperature and efficiently search for food in very complex environments. In this thesis a proposal is made to utilize swarm intelligence to build a swarm automatic frequency planner (swarm AFP). The swarm AFP produces frequency plans that are better, or on par with existing frequency planning tools, and in a fraction of the time. A swarm AFP is presented through an in-depth investigation into complex adaptive systems, agent architectures and emergence. Based on an understanding of these concepts, a swarm intelligence model called ACEUS is constructed. ACEUS forms the platform of the swarm AFP. It is a contribution to multi-agent technology as it is a new multi-agent framework that exhibits swarm intelligence and complex distributed computation. What differentiates ACEUS from other multi-agent technologies is that ACEUS works on the basis that the tasks or constructions that have been created by the agents actually guide the agents in their endeavours. There is no centralised agent controlling or guiding the process. The agents in ACEUS receive information and stimulation from their tasks or constructions in the environment. As these constructions or tasks alter the environment, the agents receive stimulus from the changing environment and then react to the changing environment. The changing environment acts as an emergent guiding force to the agents. This is the important contribution that stigmergy contributes to ACEUS. Utilizing this concept, ACEUS is used to create a swarm AFP. The swarm AFP is benchmarked against the COST 259 Siemens benchmarks. In all the COST 259 Siemens scenarios the swarm AFP produced the best results in the shortest time. The swarm AFP was also tested in a real cellular network and the resulting statistics before and after the swarm AFP implementation are presented.

The purpose of this research is to determine the linearity and monotonicity of the THS5651IDW digital to analog converter (DAC), a prototype of the future Texas Instruments TLV5651, 10-bit, 125 MHz communication DAC. Testing was conducted at the Texas Instruments facility on Forest Lane, Dallas, Texas. Texas Instruments provided test equipment, software and laboratory space to obtain test data. Analysis of the data found the DAC to be monotonic since the magnitude of the differential nonlinearity (DNL) was less than ± 1 least significant bit (LSB) and the integral nonlinearity (INL) was less than ± 0.5 LSB. The study also showed that the DAC has primarily negative DNL although the DNL is well within the desired specification.

Knowledge in organizations today is considered as one of the most important assets the organization possesses. A considerable part of this knowledge is the knowledge possessed by the individuals employed by the organization. In order for intelligent systems to perform some of the tasks their human counter parts perform in an organization the intelligent systems need to acquire the knowledge their human counter parts possess for the specific task. To develop an intelligent system that can perform a specific task in an organization, the knowledge needed to perform the task will have to be extracted from the individuals in the organization via knowledge acquisition. This knowledge will then be presented so that the intelligent system can understand it and perform the task. In order to develop an intelligent system an ontology representing the domain under consideration as well as the rules that constitute the reasoning behind the intelligent system needs to be developed. In this dissertation a development environment for developing intelligent systems called the Collaborative Ontology Builder for Reasoning and Analysis (COBRA) was developed. COBRA provides a development environment for developing the ontology and rules for an intelligent system. COBRA was used in this study to develop a Cellular telecommunications Network Consistency Checking Intelligent System (CNCCIS), which was implemented in a cellular telecommunications network. / Prof. E.M. Ehlers