Development of a life cycle management system for remote products.

The research described in this thesis has been developed as a part of the Reliability and Field Data Management for Multi-Component Products (REFIDAM) Project. This project was funded under the Applied Research Grants Scheme administered by Enterprise Ireland. The project was a partnership between Galway-Mayo Institute of Technology and an industrial company, Thermo King Europe. The project aimed to develop a system to manage the information required for maintenance costing, cost of ownership, reliability assessment and improvement of multi-component products, by establishing information flows between the customer network and across the Thermo King organisation.

Implementation of a customer relationship management system in an SME

The research conducted for this thesis has been carried out over a two year period as part of the Mobile Tools and Technology for customer care (MOTTO) project. The project was funded under the Applied Grant scheme administered by Enterprise Ireland and Nortel Networks Ltd. It was a partnership project between Galway-Mayo Institute of Technology, University of Limerick, National University of Ireland Galway, and a global Internet and communications company, Nortel Networks. The project aimed to investigate the enabling mobile communications technologies in e-Business and mobile communications in the area of Business-to-Business (B2B) customer care. The development of the application discussed in this thesis was developed in conjunction with the Galway-Mayo Institute of Technology, University of Limerick and AMT Ireland. The decision to develop the application in the Electronics Company of AMT in Limerick came about as a result of the contact established by Mark Southern from the University of Limerick. Mark was involved in overseeing the development and assisted in establishing the user requirements.

Development of a semi-automatic self learning pattern recognition system for the control of electronic boards

This project was funded under the Applied Research Grants Scheme administered by Enterprise Ireland. The project was a partnership between Galway - Mayo Institute of Technology and an industrial company, Tyco/Mallinckrodt Galway. The project aimed to develop a semi - automatic, self - learning pattern recognition system capable of detecting defects on the printed circuits boards such as component vacancy, component misalignment, component orientation, component error, and component weld. The research was conducted in three directions: image acquisition, image filtering/recognition and software development. Image acquisition studied the process of forming and digitizing images and some fundamental aspects regarding the human visual perception. The importance of choosing the right camera and illumination system for a certain type of problem has been highlighted. Probably the most important step towards image recognition is image filtering, The filters are used to correct and enhance images in order to prepare them for recognition. Convolution, histogram equalisation, filters based on Boolean mathematics, noise reduction, edge detection, geometrical filters, cross-correlation filters and image compression are some examples of the filters that have been studied and successfully implemented in the software application. The software application developed during the research is customized in order to meet the requirements of the industrial partner. The application is able to analyze pictures, perform the filtering, build libraries, process images and generate log files. It incorporates most of the filters studied and together with the illumination system and the camera it provides a fully integrated framework able to analyze defects on printed circuit boards.

Development of a rehabilitation system for the red mud waste generated at the Aughinish Alumina Bayer Plant

Aughinish Alumina Limited (AAL) have an obligation by terms of their Integrated Pollution Control Licence (IPCL) and Planning Permission to establish vegetation on the red mud stack at their plant at Aughinish, Co. Limerick. High pH and high exchangeable sodium percentage are the main known factors limiting the establishment of vegetation on red mud. Gypsum addition has been known to assist in alleviating these problems in other countries. However, there is no experience or published information on red mud rehabilitation under Irish conditions. Red mud with organic and inorganic waste-derived ameliorants as well as selected grassland species were examined under laboratory controlled environment conditions as well as in field plot trials. Also, in order that it would be economically achievable, the research utilised locally available waste products as the organic amendments. Screening trials found that physical constraints severely limit plant germination and growth in red mud. Gypsum addition effectively lowers pH, exchangeable sodium percentage and the availability of A1 and Fe in the mud. A strong relationship between pH, ESP and A1 levels was also found. Gypsum addition increased germination percentages and plant growth for all species investigated. Greenhouse trials demonstrated that organic wastes alone did not greatly improve conditions for plant growth but when used in conjunction with gypsum plant performances for all species investigated was significantly increased. There was a high mortality rate for grasses in non-gypsum treatments. An emerging trend of preferential iron uptake and calcium deficiency in non-gypsum treatments was found at pot screening stage. Species also displayed manganese and magnesium deficiencies.

The system of animate nature; the Gifford lectures delivered in the University of St. Andrews in the years 1915 and 1916, by J. Arthur Thomson.

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The system of animate nature; the Gifford lectures delivered in the University of St. Andrews in the years 1915 and 1916, by J. Arthur Thomson.

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Data-driven system identification via evolutionary retrieval of Takagi-Sugeno fuzzy models

Systemidentification, evolutionary automatic, data-driven model, fuzzy Takagi-Sugeno grammar, genotype interpretability, toxicity-prediction