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Open Student Topics

  • Potential students need to send the following information when applying for a project: (1) a motivation letter stating their skills and background with respect to the project and information about work occupation and physical presence in Linz, and (2) transcripts and recommendation letters (optional).

Understanding Mission 2030 Measures

Topics:             Climate Change, Information Visualization, Interactive Articles, Explorable Examples
Supervision:   Christina Humer, opens an external URL in a new window, Marc Streit
Contact:           vds-lab(at)jku.at
Type:                BSc Practicum

JKU aims to become a climate-neutral university by 2030. To reach this goal, JKU employs a range of measures that help with reducing energy consumption. Some of the measures are centrally administered for the entire university, like reducing room heating in winter and room cooling in summer or reducing the building lights. Other measures aim to raise awareness among people and help to reduce energy consumption on an individual level. The latter is, for example, done with posters that show how to optimize people’s individual energy consumption.

In this project, we would like to enhance people’s understanding of the measures described on these posters with an interactive article. The interactive article should help people understand, how much energy can be saved when following these recommendations, and why the measure is meaningful (e.g., why is it useful to disconnect fully charged devices). The article should contain overall information about the topic addressed by a poster and include visualizations that aid the understanding of the provided information. Readers should also be able to interactively change parameters to see how energy consumption changes (e.g., the energy consumption of taking the elevator alone vs. with several people).

JKU Mission 2030
https://www.jku.at/en/campus/the-jku-campus/sustainability/ 
https://www.jku.at/en/campus/the-jku-campus/sustainability/the-energy-saving-campaign/ 

Examples of interactive articles
https://k-means-explorable.vercel.app/, opens an external URL in a new window 
https://visxai-dimensionality-reduction-1dbad0a67a092b007c526a45.vercel.app/, opens an external URL in a new window 

Explorable Examples
http://worrydream.com/ExplorableExplanations/, opens an external URL in a new window 

Mission 2030 Posters © Image adapted from: https://www.jku.at/en/campus/the-jku-campus/sustainability/the-energy-saving-campaign/



Changes over time when transitioning to commercial dashboarding systems

Topics: visualization, dashboarding systems, qualitative study
Supervision: Conny Walchshofer, Marc Streit
Contact:  vds-lab@jku.at
Type:  BSc Practical Work, and possibly a subsequent BSc Thesis


Many long-established, traditional manufacturing businesses are becoming more digital and data-driven to improve their production. These companies are embracing visual analytics in these transitions through their adoption of commercial dashboarding systems such as Tableau and MS Power BI.

However, transitioning work practices to a new software environment, is often confronted with technical but also socio-technical challenges [Walchshofer et al. 2023, opens an external URL in a new window]. Walchshofer et al. conducted an interview study with 17 workers from an industrial manufacturing company and reported on observations such as hidden/underappreciated labor or a visualization knowledge gap leading to discomfort with interaction in dashboards. As this study represents just a snapshot of time during a lengthy transition process, this student topic focuses on a follow-up study to understand how these challenges change over time.

The goal of this project is to contribute to the design and execution of a qualitative study. No programming knowledge is required. In collaboration with Linköping University (Sweden), we will develop questions for an interview series and conduct the interviews. Audio recordings of these interviews then need to be transcribed and analyzed. The findings need to be compared with the study's results by Walchshofer et al.

Changes of observations when transitioning to a commercial dashboarding system



Lost my Way

Topics: game development, educational games, web programming
Supervision: Günter Wallner
Contact: guenter.wallner(at)jku.at
Type: MSc Thesis, MSc Practicum

Description

Games have shown to be engaging and valuable tools for education. As such many educational games have been developed to date for a variety of educational topic ranging from language learning to mathematics. At the same time educational games are challenging to design as they need to effectively communicate educational content while being entertaining to play. The goal of this work is to a) convert a previously in Adobe Flash developed game for geometry learning to HTML 5 and deploy it and b) conduct a user study to ascertain its value.

Tasks

Work will include converting the game from Adobe Flash (source code and assets will be provided) into HTML5. The game needs to include logging facilities to be able to reconstruct the solutions to the levels. The converted game needs to be deployed on a web server (space will be provided) and subsequently evaluated with players (e.g., via an online study).

Requirements
● Good programming skills
● Knowledge of HTML 5 and web development
● Knowledge of Adobe Flash is of advantage
● Interest in game development and design

An image of the game lost my way, showing a grid, a road, and sheeps



Bottom-Up Synthetic Aperture Imaging

Topics: image processing, machine learning, forest ecology, climate change
Supervision: Oliver BimberMohamed Youssef
Contact: oliver.bimber(at)jku.at
Type: BSc Practicum, MSc Practicum, BSc Thesis, MSc Thesis

Airborne Optical Sectioning (AOS) is a wide synthetic-aperture imaging technique that employs manned or unmanned aircraft, to sample images within large (synthetic aperture) areas from above occluded volumes, such as forests. Based on the poses of the aircraft during capturing, these images are computationally combined to integral images by light-field technology. These integral images suppress strong occlusion and reveal targets that remain hidden in single recordings. It is being used for search and rescue, wildfire detection, wildlife observation, archology, and forest ecology.

In this project we want to explore the reverse principle. Instead of capturing and processing top-down recorded aerial images of forest with a drone, we capture bottom-up recordings of forest from the ground. The goal is to reconstruct forest structure in lower layers despite and determine vegetation indices that provide information about vegetation health. We want to use wearable (helmet) and fixed positioned multispectral cameras in forest. This project requires physical presence in Linz, as field-experiments with special equipment are involved. 

Details on AOS:
https://github.com/JKU-ICG/AOS/, opens an external URL in a new window

Bottom-Up Synthetic Aperture Imaging



Single-Shot Learning for Drone Swarms

Topics: classification, machine learning, autonomous drone swarms
Supervision: Oliver BimberRakes Nathan
Contact: oliver.bimber(at)jku.at
Type: BSc Practicum, MSc Practicum, BSc Thesis, MSc Thesis

Airborne Optical Sectioning (AOS) is a wide synthetic-aperture imaging technique that employs manned or unmanned aircraft, to sample images within large (synthetic aperture) areas from above occluded volumes, such as forests. Based on the poses of the aircraft during capturing, these images are computationally combined to integral images by light-field technology. These integral images suppress strong occlusion and reveal targets that remain hidden in single recordings. It is being used for search and rescue, wildfire detection, wildlife observation, archology, and forest ecology.

We have developed our own autonomous drone swarm. It is capable of detecting and tracking anomalies on the ground, despite heavy occlusion cause by vegetation, such as forest. First field-experiments in cooperation with the German Aerospace Center have been carried out. In this project, we want to explore the single-shot learning of classification (being used in addition to anomaly detection).   

Details on AOS:
https://github.com/JKU-ICG/AOS/, opens an external URL in a new window

Details on the JKU Drone Swarm:
https://www.youtube.com/playlist?list=PLgGsWgs4hgaMXzo7QhSwNRctz9JTvh1JM, opens an external URL in a new window
https://github.com/JKU-ICG/AOS/tree/stable_release/AOS%20for%20Drone%20Swarms, opens an external URL in a new window

Single-Shot Learning for Drone Swarms



Drone-Based Wildfire Detection

Topics: drones, image processing, classification, machine learning
Supervision: Oliver BimberMohamed Youssef
Contact: oliver.bimber(at)jku.at
Type: BSc Practicum, MSc Practicum, BSc Thesis, MSc Thesis

Airborne Optical Sectioning (AOS) is a wide synthetic-aperture imaging technique that employs manned or unmanned aircraft, to sample images within large (synthetic aperture) areas from above occluded volumes, such as forests. Based on the poses of the aircraft during capturing, these images are computationally combined to integral images by light-field technology. These integral images suppress strong occlusion and reveal targets that remain hidden in single recordings.

In this project we want to extend our AOS simulator to simulate wildfire in an as realistic as possible way. The simulation data can then be used for training classifiers that detect wildfire early and under densely occluded condition.

Details on AOS:
https://github.com/JKU-ICG/AOS/, opens an external URL in a new window

wildfire ©Image Source: https://www.thomasnet.com/insights/3-ways-technology-can-fight-the-australian-wildfires/



Visualization and Explanation of ML-Based Indiciation Expansion in Knowledge Graphs

Topics: visualization, explainable AI, knowledge graphs, healthcare
Supervision: Christian Steinparz, Marc Streit
Contact:  vds-lab@jku.at
Type:  MSc Practical Work, MSc Thesis

Indication expansion involves identifying new potential uses or "indications" for existing drugs. One way to find new indications is to analyze the connections and relationships within pharmaceutical data, including experimental data and literature. Our collaboration partner employs a machine learning model to predict new links in their knowledge graphs, intending to discover relationships between current drugs and diseases for which they have not been previously used. The project focuses on effectively visualizing and explaining these newly predicted links, enabling domain experts to determine the potential of further research into each drug's new possible uses.

Relevant Paper: Knowledge Graphs for Indication Expansion: An Explainable Target-Disease Prediction Method, Gurbuz et al. 2022, opens an external URL in a new window

 

 

 

Indication Expansion Image adapted from the presentation of Knowledge Graphs for Indication Expansion: An Explainable Target-Disease Prediction Method (Gurbuz et al. 2022): https://www.youtube.com/watch?v=ADszHqJhr2Y&ab_channel=BiorelateLtd.