Infrared thermal imaging of the skin has been used for several decades to monitor the temperature distribution of human skin. Abnormalities such as malignancies, inflammation, and infection cause localized increases in temperature which show as hot spots or as asymmetrical patterns in an infrared thermogram. Even though it is nonspecific, infrared thermology is a powerful detector of problems that affect a patient's physiology. While the use of infrared imaging is increasing in many industrial and security applications, it has declined in medicine probably because of the continued reliance on first generation cameras. The transfer of military technology for medical use has prompted this reappraisal of infrared thermology in medicine. Digital infrared cameras have much improved spatial and thermal resolutions, and libraries of image processing routines are available to analyze images captured both statically and dynamically. If thermographs are captured under controlled conditions, they may be interpreted readily to diagnose certain conditions and to monitor the reaction of a patient's physiology to thermal and other stresses. Some of the major areas where infrared thermography is being used successfully are neurology, vascular disorders, rheumatic diseases, tissue viability, oncology (especially breast cancer), dermatological disorders, neonatal, ophthalmology, and surgery.
Metaheuristic techniques such as genetic algorithms, simulated annealing and tabu search have found wide application in most areas of engineering. These techniques have also been applied in business, financial and economic modelling. Metaheuristics have been applied to three areas of software engineering: test data generation, module clustering and cost/effort prediction, yet there remain many software engineering problems which have yet to be tackled using metaheuristics. It is surprising that metaheuristics have not been more widely applied to software engineering; many problems in software engineering are characterised by precisely the features which make metaheuristics search applicable. In the paper it is argued that the features which make metaheuristics applicable for engineering and business applications outside software engineering also suggest that there is great potential for the exploitation of metaheuristics within software engineering. The paper briefly reviews the principal metaheuristic search techniques and surveys existing work on the application of metaheuristics to the three software engineering areas of test data generation, module clustering and cost/effort prediction. It also shows how metaheuristic search techniques can be applied to three additional areas of software engineering: maintenance/evolution system integration and requirements scheduling. The software engineering problem areas considered thus span the range of the software development process, from initial planning, cost estimation and requirements analysis through to integration, maintenance and evolution of legacy systems. The aim is to justify the claim that many problems in software engineering can be reformulated as search problems, to which metaheuristic techniques can be applied. The goal of the paper is to stimulate greater interest in metaheuristic search as a tool of optimisation of software engineering problems and to encourage the investigation and exploitation of these technologies in finding near optimal solutions to the complex constraint-based scenarios which arise so frequently in software engineering
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