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. et al. (2024) ‘Robust low-cost drone detection and classification using convolutional neural networks in low SNR environments’, IEEE Journal of Radio Frequency Identification, 8, pp. 821–830. doi: 10.1109/JRFID.2024.3487303.
• Körner, P. et al. (2024) ‘Critical insights into data curation and label noise for accurate prediction of aerobic biodegradability of organic chemicals’, Environmental Science Processes & Impacts. doi: 10.1039/d4em00431k.
• Glüge, S. et al. (2023) ‘Evaluation of deep learning training strategies for the classification of bone marrow cell images’, Computer Methods and Programs in Biomedicine, (243), p. 107924. doi: 10.1016/j.cmpb.2023.107924.
• Epper, P. et al. (2023) ‘Increase of body temperature immediately after ovulation in mares’, Journal of Equine Veterinary Science, 127(104565). doi: 10.1016/j.jevs.2023.104565.
• Müller, A. et al. (2022) ‘Increase of skin temperature prior to parturition in mares’, Theriogenology, 190, pp. 46–51. doi: 10.1016/j.theriogenology.2022.07.007.
• Wehrli, S. et al. (2021) ‘Bias, awareness, and ignorance in deep-learning-based face recognition’, AI and Ethics, 2(3), pp. 509–522. doi: 10.1007/s43681-021-00108-6.
• Juchler, N. et al. (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), pp. 538–546. doi: 10.1080/21681163.2020.1728579.
• Glüge, S. et al. (2014) ‘Learning long-term dependencies in segmented-memory recurrent neural networks with backpropagation of error’, Neurocomputing, 141, pp. 54–64. doi: 10.1016/j.neucom.2013.11.043.

Book chapters, peer-reviewed

Ott, T. et al. (2019) ‘Economic measures of forecast accuracy for demand planning : a case-based discussion’, in Braschler, M., Stadelmann, T., and Stockinger, K. (eds) Applied data science : lessons learned for the data-driven business. Cham: Springer, pp. 371–386. doi: 10.1007/978-3-030-11821-1_20.

Written conference contributions, peer-reviewed

• Azzalini, L. et al. (2024) ‘Event-based hand detection on neuromorphic hardware using a sigma delta neural network’, in Wand, M. et al. (eds) Artificial Neural Networks and Machine Learning – ICANN 2024. Cham: Springer, pp. 353–364. doi: 10.1007/978-3-031-72359-9_26.
• Glüge, S. et al. (2023) ‘Robust drone detection and classification from radio frequency signals using convolutional neural networks’, in van Stein, N. et al. (eds) Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA. Setubal: SciTePress, pp. 496–504. doi: 10.5220/0012176800003595.
• Vidondo, B. et al. (2022) ‘Animal detection and species classification on Swiss camera trap images using AI’, in Bern Data Science Day (BDSD), Bern, 6 May 2022. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-24927.
• Frick, T. et al. (2021) ‘Explainable deep learning for medical time series data’, in Wireless Mobile Communication and Healthcare. Cham: Springer, pp. 244–256. doi: 10.1007/978-3-030-70569-5_15.
• Glüge, S. et al. (2020) ‘How (not) to measure bias in face recognition networks’, in Schilling, F.-P. and Stadelmann, T. (eds) Artificial Neural Networks in Pattern Recognition. Cham: Springer. doi: 10.1007/978-3-030-58309-5_10.
• Glüge, S., Böck, R. and Ott, T. (2017) ‘Emotion recognition from speech using representation learning in extreme learning machines’, in Sabourin, C. et al. (eds) Proceedings of the 9th International Joint Conference on Computational Intelligence. SciTePress, pp. 179–185. doi: 10.5220/0006485401790185.
• Glüge, S. et al. (2014) ‘The challenge of clustering flow cytometry data from phytoplankton in Lakes’, in Mladenov, V. M. and Ivanov, P. C. (eds) Nonlinear Dynamics of Electronic Systems 22nd International Conference, NDES 2014, Albena, Bulgaria, July 4-6, 2014. Proceedings. Cham: Springer, pp. 379–386. doi: 10.1007/978-3-319-08672-9_45.
• Nef, A. et al. (2014) ‘Causality detection in complex time dependent systems examplified in financial time series’, in Proceedings of the 2014 International Symposium on Nonlinear Theory and its Applications (NOLTA2014). IECE, pp. 176–179. Available at: http://www.ieice.org/nolta/symposium/archive/2014/nolta14fullvol.pdf.

Other publications

Böck, R. et al. (2013) ‘Annotation and classification of changes of involvement in group conversation’, in Proceedings : 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction - ACII 2013. IEEE Institute of Electrical and Electronics Engineers, pp. 803–808. doi: 10.1109/ACII.2013.150.

Oral conference contributions and abstracts

Ott, T. et al. (2018) ‘A dynamic network approach for the analysis of pathogen transmission chains’, in 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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