<p>This paper presents techniques for speeding up commonly used algorithms forbounding volume (BV) computation, such as the AABB, sphere and k-DOP. Byexploiting the possibilities of parallelismin modern processors, the result exceedsthe expected theoretical result. The methods focus on data-level-parallelism(DLP) using Intel’s SSE instructions, for operations on 4 parallel independentsingle precision floating point values, with a theoretical speed-up factor of 4 ondata throughput. Still, a speed-up between 7–9 are shown in the computation ofAABBs and k-DOPs. For the computation of tight fitting spheres the speed-upfactor halts at approximately 4 due to a limiting data dependency. In addition,further parallelization by multithreading algorithms on multi-core CPUs showsspeed-up factors of 14 on 2 cores and reaching 25 on 4 cores, compared to nonparallel algorithms.</p>

This paper presents techniques for speeding up commonly used algorithms forbounding volume (BV) computation, such as the AABB, sphere and k-DOP. Byexploiting the possibilities of parallelismin modern processors, the result exceedsthe expected theoretical result. The methods focus on data-level-parallelism(DLP) using Intel’s SSE instructions, for operations on 4 parallel independentsingle precision floating point values, with a theoretical speed-up factor of 4 ondata throughput. Still, a speed-up between 7–9 are shown in the computation ofAABBs and k-DOPs. For the computation of tight fitting spheres the speed-upfactor halts at approximately 4 due to a limiting data dependency. In addition,further parallelization by multithreading algorithms on multi-core CPUs showsspeed-up factors of 14 on 2 cores and reaching 25 on 4 cores, compared to nonparallel algorithms.

The topic of this master´s thesis is development of system for configuration of fire truck. The design of the fire truck is based on legislative requirements, future incidents and on a character of landscape. The first part contains these requirements. How to process current incidents and how to predict future incidents is described in the next chapter. For prediction of future incidents are used neural networks are detailed in the third part of the thesis. Implementation of the three selected neural network from previous part for Windows platform is in the fourth part of the thesis. Last part of the thesis is testing several configurations of neural network and the one with the best result is implemented in application which is the main outcome of the thesis.