Skogsbränder är svåra att upptäcka i ett tidigt stadie, vilket leder till förödande konsekvenser. Hela 30 % av koldioxiden som atmosfären tar emot kommer från skogsbränder. Flera tusentals människor och djur mister livet eller tvingas lämna sina hem. Det finns idag flera tekniker som med varierande framgång kan upptäcka skogsbränder. I detta arbete skall en alternativ metod för rökdetektering utvecklas och testas. Metoden ska vara möjlig att appliceras på UAV (Unmanned Aerial Vehicle) teknik. Arbetet fokuserar på att skilja på brandrök och dimma med högupplöst data. Två algoritmer prövas, SDA (Statistisk distributions algoritm) och KBA (Kunskapsbaserad igenkännings algoritm). Den första testar statistiska distributioner för att hitta unika identifierare för rök. Den andra algoritmen är baserad på kunskapen om rök vad gäller spektrala och morfologiska egenskaper. Röken identifieras med hjälp av form, area och kanter. Algoritmen visade en precision med 90 % i bilder innehållande rök och en feldetektering med 20 % för bilder innehållande dimma. / It is very difficult to discover forest fires in an early stage which can lead to devastat-ing consequences. Today, 30% of the total carbon dioxide that is released in the at-mosphere comes from forest fires. Thousands of human beings and animals are killed or forced to leave their homes every year. There are a variety of techniques today that is being used for discovering forest fires but whom lack in accuracy or has problems with a large amount of false alarms. This paper is an experimental study to try to solve this issue. The proposed method in this paper could be applied on UAV (Unmanned Arial Vehicles). This study will focus on identifying smoke regions from forest fires and removing fog objects which has similar characteristics as smoke. Two algorithms are tested, SDA (Statistical distributions algorithm) and KBA (Knowledge-based identification algorithm). The SDA uses statistic distribution al-gorithm where smoke and fogs characteristics are identified. The second algorithm, KBA, is a knowledge-based algorithm, where the shape, area and edges of the smoke’s characteristics are applied. The algorithm showed a 90 % accuracy for find-ing smoke in images with a false alarm rate of 20 % in images of fog.

Functionality for efficient computation of properties of system lifetimes was developed, based on the Mathematica framework. The model of these systems consists of a system structure and the components independent lifetime distributions. The components are assumed to be non-repairable. In this work a very general implementation was created, allowing a large number of lifetime distributions from Mathematica for all the component distributions. All system structures with a monotone increasing structure function can be used. Special effort has been made to compute fast results when using the exponential distribution for component distributions. Standby systems have also been modeled in similar generality. Both warm and cold standby components are supported. During development, a large collection of examples were also used to test functionality and efficiency. A number of these examples are presented. The implementation was evaluated on large real world system examples, and was found to be efficient. New results are presented for standby systems, especially for the case of mixed warm and cold standby components.