Daniel Auge, M.Sc.
Technische Universität München
Postadresse
Postal:
Boltzmannstr. 3
85748 Garching b. München
Curriculum Vitae
- (2018-now) Ph.D. candidate, Technical University of Munich
- (2018) Software Developer, Ibeo Automotive Systems GmbH
- (2015-2017) Master of Science in Electrical Engineering, Hamburg University of Technology
- (2011-2015) Bachelor of Science in General Engineering Science, Hamburg University of Technology
Research Interests
Development of spiking neural networks for the monitoring of analog radar sensor front ends.
Thesis Supervision
Ongoing:
Finished:
- Master thesis: Winner-take-all Circuits in Spiking Neural Networks for Object Tracking in Event-based Video Data
- Master thesis: Development of a Network Architecture for Speech Recognition Using Spiking Networks
- Master thesis: Embedded AI - Development of a Toolchain for the Adaption of Neural Networks to the Requirements of Embedded Systems
- Bachelor thesis: Developing an Annotation Tool for Machine Learning
- Bachelor thesis: Radar Gesture Recognition with Hierarchical Temporal Memory
Publications
2021
- A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks. Neural Processing Letters, 2021 mehr… BibTeX Volltext ( DOI )
- Hand Gesture Recognition in Range-Doppler Images Using Binary Activated Spiking Neural Networks. IEEE International Conference on Automatic Face and Gesture Recognition 2021, 2021accepted mehr… BibTeX
- End-to-end Spiking Neural Network for Speech Recognition Using Resonating Input Neurons. 30th International Conference on Artificial Neural Networks (ICANN), 2021accepted mehr… BibTeX
- Minimizing Inference Time: Optimization Methods for Converted Deep Spiking Neural Networks. International Joint Conference on Neural Networks (IJCNN), 2021accepted mehr… BibTeX
- An Evaluation of “Crash Prediction Networks” (CPN) for Autonomous Driving Scenarios in CARLA Simulator. SafeAI 2021 - The AAAI's Workshop on Artificial Intelligence Safety, 2021, mehr… BibTeX
2020
- Hand Gesture Recognition using Hierarchical Temporal Memory on Radar Sequence Data. Bernstein Conference 2020, 2020 mehr… BibTeX Volltext ( DOI )
- Resonate-and-Fire Neurons as Frequency Selective Input Encoders for Spiking Neural Networks. 2020, mehr… BibTeX Volltext (mediaTUM)
- Faster Conversion of Analog to Spiking Neural Networks by Error Centering. Bernstein Conference 2020, 2020 mehr… BibTeX Volltext ( DOI )