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Molecular dynamics (MD) as a field of molecular modelling has great potential to revolutionize our knowledge and understanding of complex macromolecular structures. Its field of application is huge, reaching from computational chemistry and biology over material sciences to computer-aided drug design. This thesis on one hand provides insights into the underlying physical concepts of molecular dynamics simulations and how they are applied in the MD algorithm, and also briefly illustrates different approaches, as for instance the molecular mechanics and molecular quantum mechanics approaches.
On the other hand an own all-atom MD algorithm is implemented utilizing and simplifying a version of the molecular mechanics based AMBER force field published by \big[\cite{cornell1995second}\big]. This simulation algorithm is then used to show by the example of oxytocin how individual energy terms of a force field function. As a result it has been observed, that applying the bond stretch forces alone caused the molecule to be compacted first in certain regions and then as a whole, and that with adding more energy terms the molecule got to move with increasing flexibility.
This thesis explores a 3D object detection and pose estimation approach based on the point pair features method presented by Drost et. al. [Dro+10]. While pose estimation methods have shown good improvements, they still remain a crucial problem on the computer vision field. In this work, we implemented a program that takes point cloud scenes as input and returns the detected object with their estimated pose. The program fully covers an object detection pipeline by processing 3D models during an offline phase, extracting their point pair features and creating a global descriptor out of them. During an online phase, the same features are extracted from a point cloud scene and are matched to the model features. After the voting scheme, potential poses of the object are retrieved. The poses end being clustered together and post-processed to finally deliver a result. The program was tested using simulated and real data. We evaluate these tests and present the final results, by discussing the achieved accuracy of the detections and the estimated poses.
This thesis deals with the question of which success factors apply to social media marketing. German start-up companies are examined for this purpose. The investigation is based on ten semi-structured expert interviews, which are evaluated using a qualitative content analysis. The results show that there are many success factors and that companies proceed differently in many areas when implementing social media marketing measures. Important success factors are a tailor-made, company-specific social media strategy with a clear formulation of goals, integration of social media marketing into the corporate strategy, openness to new social media trends, credibility and reputation, compliance with data protection, the correct handling of criticism, use of social media as a market research tool and a long-term social media strategy. However, the significance of the results should not be overestimated, since this is a qualitative study with a limited range and only examines individual cases. Further research on the success factors could be based on quantitative studies or group discussions in which experts or users or customers are interviewed. Another possibility is the qualitative examination of the user-generated content.
Künstliche Intelligenz (KI) hat sich in den letzten Jahren in der Gesellschaft stark ver-breitet und auch in Unternehmen einen zunehmenden Einsatz gefunden. Bekannt für den Einsatz sind meistens große internationale Konzerne, welche eine führende Position in den Märkten einnehmen. Um Deutschland als KI-Standort zu stärken, hat deshalb die Bundesregierung 2018 beschlossen, mit 5 Milliarden Euro deutsche Unternehmen in der Verwendung von KI zu fördern. Stand September 2021 wurden von diesen allerdings nur 346 Millionen abgerufen, wodurch sich die Frage stellt, wie besonders kleine und mittlere Unternehmen (KMUs) in Deutschland mit dem Thema KI umgehen.
Ziel dieser Arbeit ist es deshalb, die Perspektive der KMUs auf Akzeptanzkriterien, Hindernisse und Potenziale des Einsatzes von KI zu untersuchen. Um die Forschungs-fragen zu beantworten, wurde eine qualitative Interviewstudie mit fünf KMUs durchge-führt, welche im Anschluss mit dem Ansatz der qualitativen Inhaltsanalyse nach May-ring ausgewertet wurde.
Die Arbeit zeigt, dass Unternehmen ein großes Interesse am Thema KI haben und sich der Einsatz immer weiter verbreitet. KMUs sehen viel Potenzial durch den Einsatz der neuen Technologie, sehen aber noch einige Hindernisse, die es zu überwinden gilt.
Usability experts conduct user studies to identify existing usability problems. An established method is to record gaze behavior with an eye-tracker. These studies require a lot of effort to evaluate the results. Automated recognition of good and bad usability in recorded user data can support usability experts in eye tracking evaluation and reduce the effort. The objective of that bachelor thesis is to identify suitable eye-tracking metrics that correlate with the quality of usability. For this purpose, the central research question is answered: Which eye-tracking metrics correlate with the quality of a web form’s operation? To answer the research question, a quantitative A/B-user-study with eye-tracking was conducted and recorded the
gaze behavior of 30 subjects while filling out the web form. The web form was designed, that each web form page was available as a good and bad variant according to known usability guidelines. The results confirm a significant correlation between the eye-tracking-metric "number of visits to an
AOI" and the quality of the operation of a web form. The eye-tracking-metrics
"number of fixations within an AOI" and "duration of fixations within an AOI" also correlate with the quality of usability. No correlation could be confirmed for the "time of the first fixation within an AOI".