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.