Outdated

Narrative visualization, i.e. the combination of storytelling techniques with interactive graphics is a powerful option to visually communicate complex data and research findings, such as medical data. A compelling narrative visualization connects data elements or events, often time-related, into a clear and memorable story. Moreover, a story can be told in different ways, e.g., engaging or emotional, depending on the author’s intent and audience. Bach et al. [1] described eighteen narrative design patterns that can be used to tell data stories, see Figure 1. However, little research has been done on which of these patterns are suitable for communicating medical data and issues, and whether specific medical design patterns can be derived.

Goal: The goal should be a comprehensive analysis of medical blogs/YouTube videos and interactive health-related stories in newspapers with respect to narrative patterns detected. Based on this analysis, research questions should be answered, such as whether there is a need for new patterns for medical visualization and whether existing patterns can be made more concrete to better fit medical data.

Type: Bachelor/ or Master Thesis (Team project (2 FIN students) would also be possible)

Requirements: Good skills in scientific reading and summarizing scientific papers; critical and creative thinking.

References
[1] Bach, B., et al. “Narrative design patterns for data-driven storytelling.” Data-driven storytelling. AK Peters/CRC Press, 2018. 107-133.: chrome-extension://oemmndcbldboiebfnladdacbdfmadadm/https://datavis2020.github.io/pdfs/Narrative_Design_Patterns__for_Data_Driven_Storytelling.pdf
[2] Hullman, J., et al., “Visualization rhetoric: Framing effects in narrative visualization,” IEEE Trans Vis Comput Graph, vol. 17, pp. 2231-40, 2011.: https://ieeexplore.ieee.org/abstract/document/6064988
[3] Meuschke, M., et al. “Towards Narrative Medical Visualization.” arXiv preprint arXiv:2108.05462 (2021).: https://arxiv.org/abs/2108.05462

End-of-line testing is an essential step in the production process to validate the functionality of units near the end of the production line. Defective products or those not matching manufacturing tolerances must be rejected before the products are shipped. To shorten production cycles, automatic testing increasingly replaces manual inspection performed by human operators. A unit under test is exposed to a stimulus and its response is recorded by different sensors. The resulting multivariate time signals are analyzed for defect identification and classification.
The goal is to bridge the domain gap between simulated and measured unit responses, such that a classification algorithm learned on simulation data can be applied to new (unlabeled) real-world data from sensor measurements. The units under test are electric motors, whose current dt. Strom signals are to be used for a defect classification. The transfer learning problem is characterized by a shift in the input domain (i.e. simulated vs. measured signals of the same motor) while the analysis task remains the same (i.e. classify the defect). The data are multivariate time series that are simulated/measured across various operating conditions. For defect-free motors, both simulated and corresponding measured signals are available. For defective motors, however, only simulated time series with corresponding defect labels are available. The lack of real-world training data for defective motors raises the need to learn from the simulated data a well-performing classifier that can be applied to real-word measurements.

Description: The increasing amount of space debris in earth orbit poses a growing threat to space travel. Therefore, it is very important to know where space debris is located in orbit and whether there is a possibility of collisions with satellites or space crafts in operation.
The goal is to develop a VR software that visualizes orbital objects. The prototype should facilitate the evaluation of possible collisions of objects and support the decision whether, for example, a course correction of a satellite is necessary. However, the determination of the position of objects in orbit is associated with uncertainties. Various influences on objects in Earth orbit, such as the interaction with the atmosphere or variations in the gravitational field, lead to a deviation between the actual position and the position calculated from the observation data.

Tasks:

• Research on existing debris visualizations and satellite propagation
• Implementation of a real-time visualization of debris positions and collision probabilities of objects in orbit
• Development of methods for the targeted, user-guided interactive analysis of space situations

Requirements:

• Study of computer science or comparable fields of study
• Knowledge of computer graphics
• Experience with software development in Unity/C#

Die immersive Wirkung der HTC Vive bietet ein großes Potenzial bei der Exploration medizinischer Objekte. Dieses Potential soll in dieser Masterarbeit genutzt werden, um Medizinstudierende zu unterstützen. Anstatt Namen und die Funktionsweise verschiedener Strukturen auf traditionelle Weise zu lernen, soll eine interaktive, virtuelle Lernumgebung geschaffen werden.

Aufgaben

• Erarbeitung eines medizinischen Lernszenarios
• Einbettung dieses Szenarios in eine interaktive, virtuelle Welt
• Geeignete Evaluierung des implementierten Prototyps

Anforderungen: Erfahrung in C# und Unity