Nowadays, the market of Unmanned Aerial Systems (UAS) develops progressively with a lot of applications such as: infrastructure monitoring, law enforcement, environment research, goods transportation, etc. However, due to the lack of human observation, communication capacities, and protection, a UAS is an appropriate target for a criminal or a terrorist cyber attack. Therefore, risks related to the cyber-security need to be taken into account during the development of a UAS. This document presents a risk management methodology related to cyber-security. The expected result of the application of this methodology is a list of cyber-security requirements which guides the development of cyber-security countermeasures.

The objective of this work is to create a framework for the optimization of embedded software. We present algorithms and a tool flow to reduce the computational effort of programs, using value profiling and partial evaluation. Such a reduction translates into both energy savings and average-case performance improvement, while preserving a tolerable increase of worst case performance and code size. Our tool reduces the computational effort by specializing frequently executed procedures for the most common values of their parameters. The most effective specializations are automatically searched and identified, and the code is transformed through partial evaluation. Experimental results show that their technique improves both energy consumption and performance of the source code up to more than a factor of two, in average about 35% over the original program. Also, their automatic search engine greatly reduces code optimization time with respect to exhaustive search.

The paper presents a methodology for scheduling real time tasks in embedded systems where the task layout is known at design time and does not change at execution time (static systems) and where the cache miss costs are significant when compared to the normal execution time of the tasks. The scheduling model assumes a time driven dispatching of the application tasks which are ordered in a pre defined sequence. Building such a sequence in a way that is not only efficient but accounts for optimal cache sequencing is the aim of our method. The refinement of the schedule towards an optimal solution is done by simulated annealing techniques. The evaluation of the schedules is done by considering the effects of instruction caching when evaluating the computation time of the tasks.