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Speedrunning in general means to go fast in a video game. This simple concept has an immense impact on the ways players engage with games. The inherent aspect of optimization within this esports niche makes it a good application field of optimization methods. This work gives an overview of speedrunning as an esports discipline. Prior works on this subject are discussed and assessed and relevant nomenclature is introduced. Using this information, routing --- the procedure of planning a speedrun --- is picked up as a graph optimization problem.
Nintendo's iconic game The Legend of Zelda: Ocarina of Time from 1998 is used as a working example to assess previous works and to explore more of this mainly uncharted field of research.To do so, the process of speedrun modeling is conducted as exhaustive as possible within the limits of a work like this. All relevant steps to obtain a faithful model are lined out. This procedure yields a partial game graph with 6764 nodes and approx. 321,022 edges --- some uncertainty included. Current pathfinding techniques are discussed and assessed regarding their applicability to the presented speedrun routing problem. The resulting graph model and algorithmic approaches present a good reference to identify the most promising points of improvement. Challenges, flaws and possible solutions to still standing problems are discussed and assumptions from prior works are assessed. Finally, further tracks of scholarly and community work in this area are suggested and possible extensions to the approach are lined out.
It is shown that speedrun routing is not a trivial shortest path problem that can be solved with conventional methods. Furthermore, it is made clear that different games can have vastly differing routing circumstances. Assumptions of other works have been assessed using a working example and many interesting challenges and fields of further study and research in the field of speedrunning have been identified. Some probabilistic pathfinding methods and current works on AI agents for games pose interesting approaches which can be extended on by utilizing the findings of the presented work. Specialized modeling and optimization techniques have to be employed in order to have a positive effect on the speedrunning community.
This project was done in collaboration with CERN and is part of the detector control system of the ATLAS experiment. The primary goal foresaw the development and testing of the FPGA card for the MOPS-HUB crate with the focus on radiation tolerance. This was accomplished with the approach of designing two different PCBs. The first PCB was created as a fast prototype with the use of a commercial SOM-board. This was also beneficial for confirming that the chosen FPGA is suitable for the MOPS-HUB application. After the successful assembly and test, a second, more complex and foremost radiation tolerant PCB was designed. This was achieved by solely using components of the CERN radiation database.
The second part of this thesis focuses on increasing the distance of TMR registers with a Python script. A method was created for extracting and later parsing a design’s placement
information from Vivado. Furthermore, were system designed and implemented to recognize TMR cells, to find and validate free cells and to finally create a new placement for import into Vivado. These algorithms were tested with a multitude of configurations and the quality, based on the maximum possible frequency of a design, determined.
The amount of data produced and stored in multiple types of distributed data sources is growing steadily. A crucial factor that determines whether data can be analyzed efficiently is the use of adequate visualizations. Almost simultaneously with the ongoing availability of data numerous types of visualization techniques have emerged. Since ordinary business intelligence users typically lack expert visualization knowledge, the selection and creation of visualizations can be a very time- and knowledge-consuming task. To encounter these problems an architecture that aims at supporting ordinary BI users in the selection of adequate visualizations is developed in this thesis. The basic idea is to automatically provide visualization recommendations based on the concrete BI scenario and formalized visualization knowledge. Ontologies that formalize all relevant knowledge play an important role in the developed architecture and are the key to make the knowledge machine-processable.
This study paper introduces different tools, i.e. analytical methods and visualizations, in business intelligence environments. It especially emphasizes the use of OLAP-based technologies as a tradtional kind of data analysis in contrast to as graph analysis and formal concept analysis as rather new approaches in the area of visual analytics.