Tjorben Lerg

Research Associate
Institute for Electrical Engineering in Medicine
Universität zu Lübeck
Moislinger Allee 53-55
23558 Lübeck
Gebäude 19
Email: | tjorben.lerg(at)imte.fraunhofer.de |
Phone: | +49 451 384448-288 |
Research
Research interests
- Real-time signal processing
- Modelling and estimation in biomedical context
Current Projects
- Real-time processing and estimation of respiratory parameters and signals
Curriculum Vitae
Tjorben Lerg received his B.Sc. and M.Sc. in electrical engineering and information processing from Kiel University in 2022 and 2024 respectively. His theses focused on real-time signal processing in context of magneto-electric measurement systems and audio signal processing for speech impaired patients. Since 2024 he is employed as a research associate at the Fraunhofer IMTE.
Publications
Tjorben Lerg,
Implementation of Auditory-Feedback-Modulation Systems in Real-Time, 2024.
Implementation of Auditory-Feedback-Modulation Systems in Real-Time, 2024.
Tjorben
Lerg,
Martha
Gerhardt,
Lukas
Zimoch,
Christian
Dorn,
Eric
Elzenheimer,
Christin
Bald,
Johannes
Hoffmann,
Sören
Kaps,
Michael
Höft,
Gerhard
Schmidt,
Stephan
Wulfinghoff, and
Rainer
Adelung,
Self-powered elementary hybrid magnetoelectric sensor, Nano Energy , 2023.
Self-powered elementary hybrid magnetoelectric sensor, Nano Energy , 2023.
DOI: | https://doi.org/10.1016/j.nanoen.2023.108720 |
Bibtex: | ![]() @article{GERHARDT2023108720, title = {Self-powered elementary hybrid magnetoelectric sensor}, journal = {Nano Energy}, volume = {115}, pages = {108720}, year = {2023}, issn = {2211-2855}, doi = {https://doi.org/10.1016/j.nanoen.2023.108720}, url = {https://www.sciencedirect.com/science/article/pii/S2211285523005578}, author = {Martha Gerhardt and Lukas Zimoch and Christian Dorn and Eric Elzenheimer and Christin Bald and Tjorben Lerg and Johannes Hoffmann and Sören Kaps and Michael Höft and Gerhard Schmidt and Stephan Wulfinghoff and Rainer Adelung}, keywords = {Self-powered, Energy harvester, Magnetoelectric Field sensor, Sensor evaluation, Sensor modeling}, abstract = {There are numerous magnetic field sensors available, but no simple, robust, sensitive sensor for biomedical applications that does not require cryogenic cooling or shielding has yet been developed. In this contribution, a new approach for building a magnetoelectric field sensor is presented, which has the potential to fill this gap. The sensor is based on a resonant cantilever with a piezoelectric readout layer and a pair of opposing permanent magnets. One is attached to the cantilever, and the other one is fixed to a sample holder below. This new concept can be deduced from the most basic composite-based sensor [1], where the magnets interact analog to two particles in a polymer matrix. The bias-free, empirical measurements show a limit-of-detection of 46 pT/√Hz with a sensitivity of 2170 V/T using the sensor's resonance frequency of 223.5 Hz under ambient conditions. The sensor fabrication is based on low resolution silicon technology, which promises high compatibility and the possibility to be integrated into MEMS devices. The design of this new sensor can be easily altered and adjusted according to the requirements of the specific sensor application. For example, tuning of the operating resonance frequency cannot solely be modified in the production of the cantilever but also by the arrangement of the permanent magnets. In addition, the concept can also be applied to energy harvesters. Beside possible mechanical excitation, the presence of a magnetic stray field alone allows the sensor to convert 20 µT into a power of 1.31 µW/cm³·Oe². The fact that the device does not require any DC bias field makes it very attractive for energy harvesting applications since this allows a purely passive operation. In this manuscript, the sensor assembly, measurements of directional sensitivity, noise level, limit-of-detection, evaluation for energy harvesting applications from magnetic fields and a quantitative sensor model are presented.} } |
Tjorben Lerg,
Design and Implementation of an Adaptive 3D-Characterization of Magnetic Sensors, 2022.
Design and Implementation of an Adaptive 3D-Characterization of Magnetic Sensors, 2022.

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