Dr. Stefan Glüge

Projects

• Radio Signal Unsupervised and Transfer Learning / Deputy project leader / ongoing
• 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

• Glüge, Stefan; Nyfeler, Matthias; Aghaebrahimian, Ahmad; Ramagnano, Nicola; Schüpbach, Christof,

  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.

  Available from: https://doi.org/10.1109/JRFID.2024.3487303

• Körner, Paulina; Glüge, Juliane; Glüge, Stefan; Scheringer, Martin,

  2024.

  Critical insights into data curation and label noise for accurate prediction of aerobic biodegradability of organic chemicals.

  Environmental Science Processes & Impacts.

  Available from: https://doi.org/10.1039/d4em00431k

• Epper, Pascale; Glüge, Stefan; Vidondo, Beatriz; Wróbel, Anna; Ott, Thomas; Sieme, Harald; Kaeser, Rebekka; Burger, Dominik,

  2023.

  Increase of body temperature immediately after ovulation in mares.

  Journal of Equine Veterinary Science.

  127(104565).

  Available from: https://doi.org/10.1016/j.jevs.2023.104565

• Glüge, Stefan; Balabanov, Stefan; Koelzer, Viktor Hendrik; Ott, Thomas,

  2023.

  Evaluation of deep learning training strategies for the classification of bone marrow cell images.

  Computer Methods and Programs in Biomedicine.

  (243), pp. 107924.

  Available from: https://doi.org/10.1016/j.cmpb.2023.107924

• Müller, Antonia; Glüge, Stefan; Vidondo, Beatriz; Wróbel, Anna; Ott, Thomas; Sieme, Harald; Burger, Dominik,

  2022.

  Increase of skin temperature prior to parturition in mares.

  Theriogenology.

  190, pp. 46-51.

  Available from: https://doi.org/10.1016/j.theriogenology.2022.07.007

• Wehrli, Samuel; Hertweck, Corinna; Amirian, Mohammadreza; Glüge, Stefan; Stadelmann, Thilo,

  2021.

  Bias, awareness, and ignorance in deep-learning-based face recognition.

  AI and Ethics.

  2(3), pp. 509-522.

  Available from: https://doi.org/10.1007/s43681-021-00108-6

• Juchler, Norman; Schilling, Sabine; Glüge, Stefan; Bijlenga, Philippe; Rüfenacht, Daniel; Kurtcuoglu, Vartan; Hirsch, Sven,

  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.

  Available from: https://doi.org/10.1080/21681163.2020.1728579

• Glüge, Stefan; Böck, Ronald; Palm, Günther; Wendemuth, Andreas,

  2014.

  Learning long-term dependencies in segmented-memory recurrent neural networks with backpropagation of error.

  Neurocomputing.

  141, pp. 54-64.

  Available from: https://doi.org/10.1016/j.neucom.2013.11.043

• Ott, Thomas; Glüge, Stefan; Bödi, Richard; Kauf, Peter,

  2019.

  Economic measures of forecast accuracy for demand planning : a case-based discussion.

  In:

  Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, eds.,

  Applied data science : lessons learned for the data-driven business.

  Cham:

  Springer.

  pp. 371-386.

  Available from: https://doi.org/10.1007/978-3-030-11821-1_20

• Azzalini, Loïc; Glüge, Stefan; Struckmeier, Jens; Sandamirskaya, Yulia,

  2024.

  Event-based hand detection on neuromorphic hardware using a sigma delta neural network[paper].

  In:

  Wand, Michael; Malinovská, Kristína; Schmidhuber, Jürgen; Tetko, Igor V., eds.,

  Artificial Neural Networks and Machine Learning – ICANN 2024.

  33rd International Conference on Artificial Neural Networks (ICANN), Lugano, Switzerland, 17–20 September 2024.

  Cham:

  Springer.

  pp. 353-364.

  Available from: https://doi.org/10.1007/978-3-031-72359-9_26

• Glüge, Stefan; Nyfeler, Matthias; Ramagnano, Nicola; Horn, Claus; Schüpbach, Christof,

  2023.

  Robust drone detection and classification from radio frequency signals using convolutional neural networks[paper].

  In:

  van Stein, Niki; Marcelloni, Francesco; Lam, H.K.; Filipe, Joaquim, eds.,

  Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA.

  15th International Joint Conference on Computational Intelligence (IJCCI), Rome, Italy, 13-15 November 2023.

  Setubal:

  SciTePress.

  pp. 496-504.

  Available from: https://doi.org/10.5220/0012176800003595

• Vidondo, Beatriz; Glüge, Stefan; Hubert, Laurtent; Fischer, Claude; Le Grand, Luc,

  2022.

  Animal detection and species classification on Swiss camera trap images using AI[poster].

  In:

  Bern Data Science Day (BDSD), Bern, 6 May 2022.

  ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

  Available from: https://doi.org/10.21256/zhaw-24927

• Frick, Thomas; Glüge, Stefan; Rahimi, Abbas; Benini, Luca; Brunschwiler, Thomas,

  2021.

  Explainable deep learning for medical time series data[paper].

  In:

  Wireless Mobile Communication and Healthcare.

  International Conference on Wireless Mobile Communication and Healthcare (MobiHealth), Online, 18 December 2020.

  Cham:

  Springer.

  pp. 244-256.

  Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ; 362.

  Available from: https://doi.org/10.1007/978-3-030-70569-5_15

• Glüge, Stefan; Amirian, Mohammadreza; Flumini, Dandolo; Stadelmann, Thilo,

  2020.

  How (not) to measure bias in face recognition networks[paper].

  In:

  Schilling, Frank-Peter; Stadelmann, Thilo, eds.,

  Artificial Neural Networks in Pattern Recognition.

  9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020.

  Cham:

  Springer.

  Lecture Notes in Computer Science ; 12294.

  Available from: https://doi.org/10.1007/978-3-030-58309-5_10

• Glüge, Stefan; Böck, Ronald; Ott, Thomas,

  2017.

  Emotion recognition from speech using representation learning in extreme learning machines[paper].

  In:

  Sabourin, Christophe; Julian Merelo, Juan; O'Reilly, Una-May; Madani, Kurosh; Warwick, Kevin, eds.,

  Proceedings of the 9th International Joint Conference on Computational Intelligence.

  9th International Joint Conference on Computational Intelligence, Funchal, Portugal, 1-3 November 2017.

  SciTePress.

  pp. 179-185.

  Available from: https://doi.org/10.5220/0006485401790185

• Nef, Annina; Glüge, Stefan; Ott, Thomas; Kauf, Peter,

  2014.

  Causality detection in complex time dependent systems examplified in financial time series[paper].

  In:

  Proceedings of the 2014 International Symposium on Nonlinear Theory and its Applications (NOLTA2014).

  Nonlinear Theory and Applications 2014 (NOLTA), Luzern, 14-18 September 2014.

  IECE.

  pp. 176-179.

  Available from: http://www.ieice.org/nolta/symposium/archive/2014/nolta14fullvol.pdf

• Glüge, Stefan; Pomati, Francesco; Albert, Carlo; Kauf, Peter; Ott, Thomas,

  2014.

  The challenge of clustering flow cytometry data from phytoplankton in Lakes[paper].

  In:

  Mladenov, V.M.; Ivanov, P.C., eds.,

  Nonlinear Dynamics of Electronic Systems 22nd International Conference, NDES 2014, Albena, Bulgaria, July 4-6, 2014. Proceedings.

  NDES 2014, 22nd Nonlinear Dynamics of Electronic Systems Conference, Albena, Bulgaria, 4-6 July 2014.

  Cham:

  Springer.

  pp. 379-386.

  Communications in Computer and Information Science ; 438.

  Available from: https://doi.org/10.1007/978-3-319-08672-9_45

• Böck, Ronald; Glüge, Stefan; Siegert, Ingo; Wendemuth, Andreas,

  2013.

  Annotation and classification of changes of involvement in group conversation[paper].

  In:

  Proceedings : 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction - ACII 2013.

  ACII 2013 Humaine Association Conference, Geneva, 2-5 September 2013.

  IEEE Institute of Electrical and Electronics Engineers.

  pp. 803-808.

  Available from: https://doi.org/10.1109/ACII.2013.150

• Ott, Thomas; Glüge, Stefan; Schüle, Martin; Hill, Christopher,

  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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