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.

Search for very high energy Gamma radiation from the Starburst Galaxy IC 342

This thesis describes a search for very high energy (VHE) gamma-ray emission from the starburst galaxy IC 342. The analysis was based on data from the 2003 — 2004 observing season recorded using the Whipple 10-metre imaging atmospheric Cherenkov telescope located on Mount Hopkins in southern Arizona. IC 342 may be classed as a non-blazar type galaxy and to date only a few such galaxies (M 87, Cen A, M 82 and NGC 253) have been detected as VHE gamma-ray sources. Analysis of approximately 24 hours of good quality IC 342 data, consisting entirely of ON/OFF observations, was carried out using a number of methods (standard Supercuts, optimised Supercuts, scaled optimised Supercuts and the multivariate kernel analysis technique). No evidence for TeV gamma-ray emission from IC 342 was found. The significance was 0.6 a with a nominal rate of 0.04 ± 0.06 gamma rays per minute. The flux upper limit above 600 GeV (at 99.9 % confidence) was determined to be 5.5 x 10-8 m-2 s-1, corresponding to 8 % of the Crab Nebula flux in the same energy range.