Jonas is working on a project that aims at developing open-source hardware and software for low-cost low-energy wildlife tracking. For this, he develops signal processing and optimisation algorithms based on probabilistic models. They can robustly localise receivers using just a few milliseconds of data recorded in environments that are known to be challenging for GPS.
Jonas is also looking into the tight integration of GPS into navigation systems of mobile platforms, e.g., robots. Specifically, he focusses on probabilistic models that can be used in incremental factor graph optimisation.
Jonas is an electrical engineer who was educated with a strong focus on automation and control. He has knowledge in areas such as signal processing, sensor fusion, classic and modern control concepts, modelling, simulation, optimisation, and artificial intelligence. He had the chance to apply his skills in the biomedical industry as well as the aerospace industry while working with The Boeing Company on machine learning for production optimisation. His Master's thesis was concerned with efficient communication in sensor networks. Later, he joined the University of Oxford to pursue a Doctor of Philosophy in the field of autonomous intelligent machines and systems. Currently, he is trying to improve GPS positioning for low-cost low-energy receivers using probabilistic signal processing. He is also interested in the tight integration of GPS into navigation systems of robots.
Hierarchical Event−Triggered Learning for Cyclically Excited Systems With Application to Wireless Sensor Networks
J. Beuchert‚ F. Solowjow‚ J. Raisch‚ S. Trimpe and T. Seel
In IEEE Control Systems Letters. Vol. 4. No. 1. Pages 103−108. 2020.
Overcoming Bandwidth Limitations in Wireless Sensor Networks by Exploitation of Cyclic Signal Patterns: An Event−triggered Learning Approach
J. Beuchert‚ F. Solowjow‚ S. Trimpe and T. Seel
In Sensors. Vol. 20. No. 1. Pages 260. 2020.
Fabrication Optimization for Composite Parts
M. Y.−C. Wang‚ J. L. Miller‚ J. Beuchert and R. E. H. Chen
US Patent App. 16/380‚215