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.

Management and evaluation of a range of forest species for use as cut foliage

This project explored the possibility of harvesting marketable foliage stems in addition to producing timber from plantations of Larix leptolepis, Cupressus macrocarpa ‘Goldcrest’ and Tsuga heterophylla. Data recorded from trial sites included both growth parameters, in the form of height and diameter increments, and production parameters in the form of foliage stem yields. Results varied with species and site type. In many cases, results achieved appear to have been influenced more by the biology of the trees than by the treatments alone. Trials were also established to investigate methods of managing old or over grown Abies procera Christmas tree plantations for forest foliage production. Shelf life testing and market research into the domestic trade of forest foliage were also conducted over the course of the project. Recommendations for managing forest plantations for foliage production as well as a general discussion on the industry are presented in this report.