Dr. Stefan Glüge

Projects

• Radio Signal Object Detection / Project leader / ongoing
• Unknown Radio Signal Clustering / Deputy project leader / ongoing
• Radio Signal Unsupervised and Transfer Learning / Deputy project leader / completed
• Emerging AI and computing technologies / Team member / completed
• Drone Signal Dataset / Deputy project leader / completed
• Predictive Analytics for Hospital Supply Chain Management / Team member / completed
• PiaBreed: Machine Learning for automated ovulation and birth monitoring in horses / Deputy project leader / completed
• Using data analysis techniques for the interpretation of stable isotope labelling incorporation from real-time PTR-ToF-MS data / Team member / completed
• Neuronal Growth Modelling (BioDynaMo Initial Project) / Deputy project leader / completed
• A cloud-based IoT approach for food safety and quality prediction / Team member / completed
• Infectiology++ – Germ Tracking / Deputy project leader / completed
• Libra: A One-Tool Solution for MLD4 Compliance / Team member / completed
• Comprehensive Sales Forecasting for Supply Chain Optimization in Food Industry / Team member / completed
• Multi-Asset Investment Process using Bayes Ensembles of Trading Models / Team member / completed
• Clustering of high-throughput flow-cytometry data / Team member / completed

Publications

Articles in scientific journal, peer-reviewed

• Glüge, S., Nyfeler, M., Aghaebrahimian, A., Ramagnano, N., & Schüpbach, C. (2024). Robust low-cost drone detection and classification using convolutional neural networks in low SNR environments. IEEE Journal of Radio Frequency Identification, 8, 821–830. https://doi.org/10.1109/JRFID.2024.3487303
• Körner, P., Glüge, J., Glüge, S., & Scheringer, M. (2024). Critical insights into data curation and label noise for accurate prediction of aerobic biodegradability of organic chemicals. Environmental Science Processes & Impacts. https://doi.org/10.1039/d4em00431k
• Glüge, S., Balabanov, S., Koelzer, V. H., & Ott, T. (2023). Evaluation of deep learning training strategies for the classification of bone marrow cell images. Computer Methods and Programs in Biomedicine, 243, 107924. https://doi.org/10.1016/j.cmpb.2023.107924
• Epper, P., Glüge, S., Vidondo, B., Wróbel, A., Ott, T., Sieme, H., Kaeser, R., & Burger, D. (2023). Increase of body temperature immediately after ovulation in mares. Journal of Equine Veterinary Science, 127(104565). https://doi.org/10.1016/j.jevs.2023.104565
• Müller, A., Glüge, S., Vidondo, B., Wróbel, A., Ott, T., Sieme, H., & Burger, D. (2022). Increase of skin temperature prior to parturition in mares. Theriogenology, 190, 46–51. https://doi.org/10.1016/j.theriogenology.2022.07.007
• Wehrli, S., Hertweck, C., Amirian, M., Glüge, S., & Stadelmann, T. (2021). Bias, awareness, and ignorance in deep-learning-based face recognition. AI and Ethics, 2(3), 509–522. https://doi.org/10.1007/s43681-021-00108-6
• Juchler, N., Schilling, S., Glüge, S., Bijlenga, P., Rüfenacht, D., Kurtcuoglu, V., & Hirsch, S. (2020). Radiomics approach to quantify shape irregularity from crowd-based qualitative assessment of intracranial aneurysms. Computer Methods in Biomechanics and Biomedical Engineering : Imaging & Visualization, 8(5), 538–546. https://doi.org/10.1080/21681163.2020.1728579
• Glüge, S., Böck, R., Palm, G., & Wendemuth, A. (2014). Learning long-term dependencies in segmented-memory recurrent neural networks with backpropagation of error. Neurocomputing, 141, 54–64. https://doi.org/10.1016/j.neucom.2013.11.043

Book chapters, peer-reviewed

Ott, T., Glüge, S., Bödi, R., & Kauf, P. (2019). Economic measures of forecast accuracy for demand planning : a case-based discussion. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 371–386). Springer. https://doi.org/10.1007/978-3-030-11821-1_20

Written conference contributions, peer-reviewed

• Azzalini, L., Glüge, S., Struckmeier, J., & Sandamirskaya, Y. (2024). Event-based hand detection on neuromorphic hardware using a sigma delta neural network [Conference paper]. In M. Wand, K. Malinovská, J. Schmidhuber, & I. V. Tetko (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2024 (pp. 353–364). Springer. https://doi.org/10.1007/978-3-031-72359-9_26
• Glüge, S., Nyfeler, M., Ramagnano, N., Horn, C., & Schüpbach, C. (2023). Robust drone detection and classification from radio frequency signals using convolutional neural networks [Conference paper]. In N. van Stein, F. Marcelloni, H. K. Lam, & J. Filipe (Eds.), Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA (pp. 496–504). SciTePress. https://doi.org/10.5220/0012176800003595
• Vidondo, B., Glüge, S., Hubert, L., Fischer, C., & Le Grand, L. (2022, May 6). Animal detection and species classification on Swiss camera trap images using AI. Bern Data Science Day (BDSD), Bern, 6 May 2022. https://doi.org/10.21256/zhaw-24927
• Frick, T., Glüge, S., Rahimi, A., Benini, L., & Brunschwiler, T. (2021). Explainable deep learning for medical time series data [Conference paper]. Wireless Mobile Communication and Healthcare, 244–256. https://doi.org/10.1007/978-3-030-70569-5_15
• Glüge, S., Amirian, M., Flumini, D., & Stadelmann, T. (2020). How (not) to measure bias in face recognition networks [Conference paper]. In F.-P. Schilling & T. Stadelmann (Eds.), Artificial Neural Networks in Pattern Recognition. Springer. https://doi.org/10.1007/978-3-030-58309-5_10
• Glüge, S., Böck, R., & Ott, T. (2017). Emotion recognition from speech using representation learning in extreme learning machines [Conference paper]. In C. Sabourin, J. Julian Merelo, U.-M. O’Reilly, K. Madani, & K. Warwick (Eds.), Proceedings of the 9th International Joint Conference on Computational Intelligence (pp. 179–185). SciTePress. https://doi.org/10.5220/0006485401790185
• Nef, A., Glüge, S., Ott, T., & Kauf, P. (2014). Causality detection in complex time dependent systems examplified in financial time series [Conference paper]. Proceedings of the 2014 International Symposium on Nonlinear Theory and Its Applications (NOLTA2014), 176–179. http://www.ieice.org/nolta/symposium/archive/2014/nolta14fullvol.pdf
• Glüge, S., Pomati, F., Albert, C., Kauf, P., & Ott, T. (2014). The challenge of clustering flow cytometry data from phytoplankton in Lakes [Conference paper]. In V. M. Mladenov & P. C. Ivanov (eds.), Nonlinear Dynamics of Electronic Systems 22nd International Conference, NDES 2014, Albena, Bulgaria, July 4-6, 2014. Proceedings (pp. 379–386). Springer. https://doi.org/10.1007/978-3-319-08672-9_45

Other publications

Böck, R., Glüge, S., Siegert, I., & Wendemuth, A. (2013). Annotation and classification of changes of involvement in group conversation [Conference paper]. Proceedings : 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction - ACII 2013, 803–808. https://doi.org/10.1109/ACII.2013.150

Oral conference contributions and abstracts

Ott, T., Glüge, S., Schüle, M., & Hill, C. (2018, June). A dynamic network approach for the analysis of pathogen transmission chains. The 26th Nonlinear Dynamics of Electronic Systems Conference, (NDES 2018), Acireale, 11-13 June 2018.

Research data

Glüge, Stefan; Nyfeler, Matthias; Ramagnano, Nicola; Horn, Claus; Schüpbach, Christoph, . Noisy drone RF signal classification. Kaggle. Available from: https://www.kaggle.com/datasets/sgluege/noisy-drone-rf-signal-classification

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