SIRIO : Integrated Forest Firesmonitoring, detection and decision supportsystem with low cost commercial sensorssuited for complex orography

Forest Fires in our society cause a lot of damage, in particular regarding the economic and environmental landscape. In order to monitor a large portion of territory automatically, with a good cost/performances trade-off, it is necessary to develop new early warning systems. We propose a ground-based system with modular architecture, equipped with low cost commercial sensor. The idea is to develop the software able to manage the forest fires monitoring. The technique is based on Static and Dynamic analysis of chromatic changes between images, tailored for our case of study in a large scale monitoring of vegetation and using different sensors to reduce or eliminate the false alarm rate. Concerning the image geo-referencing tool, the present work describes an innovative projective geo-referencing algorithm able to geo-reference complex orography regions using fixed ground station images. Besides, it does not need the collection of Ground Control Points, which is a very hard task in complex orography environments. In order to make a user oriented product and to help the operator during extinguishing activities, a decision support tool has been developed as well. This work presents the results of one year monitoring campaign conducted in cooperation with the Civil Protection Offices in Sanremo (IM), Ital

An estimate of necessary effort in the development of software projects

International Workshop on Intelligent Technologies for Software Engineering (WITSE'04). 19th IEEE International Conference on Automated Software Engineering (Linz, Austria, September 20th - 25th, 2004)The estimated of the effort in the development of software projects has already been studied in the field of software engineering. For this purpose different ways of measurement such as Unes of code and function points, generally addressed to relate software size with project cost (effort) have been used. In this work we are presenting a research project that deals with this field, us'mg machine learning techniques to predict the software project cost. Several public set of data are used. The analysed sets of data only relate the effort invested in the development of software projects and the size of the resultant code. For this reason, we can say that the data used are poor. Despite that, the results obtained are good, because they improve the ones obtained in previous analyses. In order to get results closer to reality we should find data sets of a bigger size that take into account more variables, thus offering more possibilities to obtain solutions in a more efficient way.Publicad