No description available.

This thesis originates from first hand early experiences of the researcher regarding current processes and practices in operation in BAE SYSTEMS Ltd (now referred to hereafter as `the Company'), and recognises the potential for improvement within the realm of knowledge management. The huge volume of internal and external information overwhelms the majority of organisations and knowledge management provides solutions to enable organisations to be effective, efficient, and competitive. The software agent approach and information retrieval technique indicates great potential for effectively managing information. This research seeks to answer the questions of whether software agents can provide the Company with solutions to the knowledge management issues identified in this inquiry and whether they can also be used elsewhere within the organisation to improve other aspects of the business. The research analysis shows that software agents offer a wide applicability across the Company; can be created with relative ease and can provide benefits by improving the effectiveness and efficiency of processes. Findings also provided valuable insight into human-computer-interface design and usability aspects of software agent applications. The research deals with these questions using action research in order to develop a collaborative change mechanism within the Company and a practical applicability of the research findings in situ. Using a pluralistic methodology the findings provide a combination of the subjective and objective views intermittently within the research cycles thereby giving the researchera more holistic view of this research. Little attention has been paid to integrating software agent technologies into the knowledge management processes.This research proposes a software agent application that incorporates: (1) Co-ordination of software agents for information retrieval to manage information gathering, filtering, and dissemination; (2) To promote effective interpretation of information and more efficient processes;(3) Building accurate search profiles weighted on pre-defined criteria; (4) Integrating and organising a Company resource management knowledge-base; (5) Ensuring that the right information gets to the right personnel at the right time; and (6) So the Company can effectively assign the right experts to the right roles within the Company.

No description available.

A new hybrid neural network model capable of texture analysis in a digital image processing environment is presented in this thesis. This model is constructed from two different types of neural network, self-organisation and back-propagation. Along with a brief resume of digital image processing concepts, an introduction to neural networks is provided. This contains appropriate documentation of the neural networks and test evidence is also presented to highlight the relative strengths and weaknesses of both neural networks. The hybrid neural network is proposed from this evidence along with methods of training and operation. This is supported by practical examples of the system's operation with digital images. Through this process two modes of operation are explored, classification and segmentation of texture content within images. Some common methods of texture analysis are also documented, with spatial grey level dependence matrices being chosen to act as a feature generator for classification by a back-propagation neural network, this provides a benchmark to assess the performance of the hybrid neural network. This takes the form of descriptive comparison, pictorial results, and mathematical analysis when using aerial survey images. Other novel approaches using the hybrid neural network are presented with concluding comments outlining the findings presented within this thesis.

by Avinash K. Dixit and Robert S. Pindyck. / Includes bibliographical references (p. 27-28). / Research supported by the National Science Foundation and by the MIT Center for Energy and Environmental Policy Research.

Nalin Kulatilaka. / Includes bibliographical references.

Michael C. Lynch. / "This is a revised version of a paper presented to the 1986 Midwest Political Science Association Meeting, April 10, 1986."--p. [1]. / Includes bibliographical references.

No description available.

No description available.

Advances in digital technology in the last decade have motivated the development of very efficient and high quality speech compression algorithms. While in the early low bit rate coding systems, the main target was the production of intelligible speech at low bit rates, expansion of new applications such as mobile satellite systems increased the demand for reducing the transmission bandwidth and achieving higher speech quality. This resulted in the development of efficient parametric models for speech production system. These models were the basis of powerful speech compression algorithms such as CELP, MBE, MELP and WI. The performance of a speech coder not only depends on the speech production model employed but also on the accurate estimation of speech parameters. Periodicity, also known as pitch, is one of the speech parameters that greatly affect the synthesised speech quality. Thus, the subject of pitch determination has attracted much research in the area of low bit rate coding. In these studies it is assumed that for a short segment of speech, called frame, the pitch is fixed or smoothly evolving. The pitch estimation algorithms generally fail to determine irregular variations, which can occur at onset and offset speech segments. In order to overcome this problem, a novel preprocessing method, which detects irregular pitch variations and modifies the speech signal such as to improve the accuracy of the pitch estimation, is proposed. This method results in more regular speech while maintaining perceptual speech quality. The perceptual quality of the synthesised speech may also be improved using postfiltering techniques. Conventional postfiltering methods generally consider the enhancement of the whole speech spectrum. This may result in the broadening of the first formant, which leads to the increase of quantisation noise for this formant. A new postfiltering technique, which is based on factorising the linear prediction synthesis filter, is proposed. This provides more control over the formant bandwidth and attenuation of spectral speech valleys. Key words: Pitch smoothing, speech pre-processor, postfiltering.