Exploring Methods and Technologies in our Living Lab Environment

Type of thesis: Bachelorarbeit / location: Leipzig / Status of thesis: Open theses

We offer a unique opportunity for undergraduates to engage in cutting-edge research in our Living Lab environment. As a center dedicated to innovation and advancement in technology, we invite motivated students to join us in exploring various methods and technologies to address real-world challenges.

Project Scope
We offer a wide variety of projects, each focused on the exploration and application of advanced technologies within specific domains. Examples include, but are not limited to.

Sensor evaluation in real-world scenarios

Investigate the utility and effectiveness of sensors such as XDK Bosch, depth cam, Lidar sensors, and others in real-world application scenarios. Analyze data collected from these sensors to gain insight into their performance and potential applications.

Enhance the handwriting recognition application

Continue to develop and enhance an existing handwriting recognition application. Implement improvements in accuracy to make the application more robust and efficient.

Sports analytics in the Handball Bundesliga

Explore the predictive capabilities of data analytics by examining the expected goal rate (xG) using game data from matches. Use statistical methods and machine learning algorithms to analyze patterns and factors influencing xG.

Implement a Local Positioning System (LPS)

Design and implement a local positioning system to enable accurate indoor positioning and navigation.
Investigate appropriate technologies and algorithms for accurate localization.

Implementation of Computer Vision in a Parkour Scenario

Explore the use of computer vision techniques to analyze and optimize movement in a parkour environment. Develop algorithms to track and analyze the motion of a jet bot navigating a parkour course.

Pose Detection using a Fisheye Camera

Explore the challenges and opportunities of pose detection utilizing a fisheye camera.
Develop algorithms to accurately detect and track human poses captured by fisheye cameras, considering the unique distortions and perspectives.

Counterpart

Dr. Thomas Burghardt

Leipzig University

Service and Transfer Center, Living Lab

TU
Universität
Max
Leibnitz-Institut
Helmholtz
Hemholtz
Institut
Fraunhofer-Institut
Fraunhofer-Institut
Max-Planck-Institut
Institute
Max-Plank-Institut