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M.Sc. Marten Franke

https://doi.org/10.1109/IROS47612.2022.9981156 Franke, M., Ristić-Durrant, D. & Michels, K.. (2022). Deep learning-based rail track detection, XX International Scientific-expert Conference on Railways – RAILCON [...] Franke, M., Gopinath, V., Ristić-Durrant, D., & Michels, K. (2022). Object-Level Data Augmentation for Deep Learning-Based Obstacle Detection in Railways. Applied Sciences, 12(20), 10625. https://doi.org/10 [...] 00192 Ristić-Durrant, D., Franke, M., Asghar, A., Ojdanić, D., Miltenovic, A. & Michels, K. (2021). Deep-learning-based image features for industrial applications of visual identification and inspection

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Nils Hülsmeier

62549 Fax: +49 421 218 98 62549 Mail: Huelsmeier(at)me.uni-bremen.de Research : - Machine learning and deep learning for digital signal processing - Efficient hardware architectures for machine learning algorithms [...] March 2022. Towards Dynamic Fault Tolerance for Hardware-Implemented Artificial Neural Networks: A Deep Learning Approach D. Gregorek, N. Hülsmeier and S. Paul, IEEE International Conference on Robotics

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Bauteil Charakterisierung

der Wafer-/Chip-Ebene Static High Voltage Characterisation Pressure vessel with nitrogen atmosphere Deep Level Transient Spectroscopy Energetic position of dopants and “dirt” in Si, SiC, GaN, etc.

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Design Automation

increasing in complexity as modern technology nodes are constantly decreasing the minimum feature size. Deep sub-micron technologies with nanometre channel length introduce new effects which force a paradigm

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Dr.-Ing. Danijela Ristic-Durrant

12700/APH.19.3.2022.3.15 Mohammadzadeh Babr M., Faghihabdolahi M., Ristić-Durrant D., Michels K., Deep Learning-Based Occlusion Handling of Overlapped Plants for Robotic Grasping . Applied Sciences. 2022; [...] 73655 Ristić-Durrant, D., Franke, M., Michels, K., Nikolić, V., Banić, M., Simonović, M., (2021). Deep learning-based obstacle detection and distance estimation using object bounding box , Facta Universitatis [...] 2019, Springer Nature Switzerland AG 2019. Haseeb, M.A., Ristić-Durrant, D., Gräser, A. (2019). A deep learning based autonomous distance estimation and tracking of multiple objects for improvement in

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Communication Networks for Space

Approach. 6th ed., Pearson Education, 2013 A. Förster: Wireless Sensor Networks. Wiley, 2016 J. Taylor: Deep Space Communications. Wiley, 2016

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HWK Workshop 2023

d stimulus encoding strategy by integrating state-of-the-art deep learning into a Bayesian optimization framework. We first trained a deep stimulus encoder (DSE) to optimize stimuli assuming perfect knowledge [...] etics with Deep Learning and Synthetic Reality Donders Center for Cognition, Radboud Universiteit In this talk, I will give an overview of our recent work combining neural coding with deep learning to [...] transformative power of deep reinforcement learning in enhancing neuroprosthetic vision in multifaceted settings. Together, our investigations pave the way for a future where deep learning and synthetic

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Topics for Projects

scope of the project includes IoT, sensors, design and testing of monitoring stations, machine and deep learning for parameter prediction, simulation of pollution behavior, and data analysis. References: [...] contribute most to the fault-detection outcomes. This project welcomes students with a background in IoT, deep learning, and software programming (Python, C++, Arduino) to contribute through Bachelor/Master projects [...] low-level debugging will be highly advantageous. These projects offer an excellent opportunity to deepen technical expertise, contribute to innovative research, and gain insights into interdisciplinary

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Thermischer Strömungssensor

gebräuchlichen Thermoelementen aus Polysilizium und Gold oder Aluminium. Die Membran wird mittels DRIE (Deep Reactive Ion Etching) freigelegt. Dies erlaubt senkrechte Seitenwände der Kavität und führt zu einer

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Magnus Symposium Berlin

Datencharakteristiken II 09:00 Dr. Sebastian Goldt, SISSA Triest, Stochastische Thermodynamik und Deep Learning (online) 09:20 Prof. Dr. Giovanna Morigi, Universität des Saarlandes, Quantum Information

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M.Sc. Marten Franke

Nils Hülsmeier

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Design Automation

Dr.-Ing. Danijela Ristic-Durrant

Communication Networks for Space

HWK Workshop 2023

Topics for Projects

Thermischer Strömungssensor

Magnus Symposium Berlin