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Prof. Dr.-Ing. Hendrik Wöhrle


Hendrik Wöhrle; Felix Schneider; Fabian Schlenke; Denis Lebold; Mariela De Lucas Alvarez; Frank Kirchner; Michael Karagounis

In: IEEE Transactions on Circuits and Systems I: Regular Papers, Vol. 70, No. 1, Pages 40-53, IEEE, 1/2023.

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Felix Wiebe; Shivesh Kumar; Daniel Harnack; Malte Langosz; Hendrik Wöhrle; Frank Kirchner

In: William Holderbaum; J. M. Selig (Hrsg.). 2nd IMA Conference on Mathematics of Robotics. IMA Conference on Mathematics of Robotics (IMA-2022), September 8-10, London, United Kingdom, Springer Proceedings in Advanced Robotics, ISBN 9783030913519, Springer International Publishing, 1/2022.

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Hendrik Wöhrle; Mariela De Lucas Alvarez; Fabian Schlenke; Alexander Walsemann; Michael Karagounis; Frank Kirchner (Hrsg.)

IEEE International Midwest Symposium on Circuits and Systems (MWSCAS-2021), August 9-11, USA, IEEE, 8/2021.

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Short biography

Prof. Dr.-Ing. Hendrik Wöhrle studied bioinformatics as well as electrical engineering and information technology at the Free University of Berlin and the Fernuniversität in Hagen. After working in industry as a software developer for signal processing and numerical algorithms in the field of biosignal analysis, he joined the Robotics Innovation Center of the German Research Center for Artificial Intelligence, where he worked on signal processing and machine learning for brain computer interfaces, data flow architectures for reconfigurable hardware, and rehabilitation robotics. In 2017, he received his PhD in computer science on application-specific computer architectures for artificial intelligence from the University of Bremen. Since 2019 he is professor for Intelligent Autonomous Sensor and Actuator Systems at Dortmund University of Applied Sciences. His current research areas are the development of efficient and low-power computer architectures for machine learning in miniaturized and embedded systems, robotics and biomedical engineering. Machine learning techniques are again used to design and optimize these systems. His research also addresses the reverse perspective, i.e., the development and application of machine learning techniques for the optimization of signal processing system, for Internet of Things applications, and for the design of specialized or domain- and application-specific hardware architectures for machine learning and robotics.

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