Dr. Matthias Nyfeler

Work at ZHAW

Position

• Programme Director MSc specialisation in Applied Computational MSc specialisation in Applied Computational Life Sciences
• Head of Research Group Advance Signal Analytics
• Head ICLS statistical consulting
• Lecturer for Physics and Statistics

Focus

• Deep Learning Classification of Drone Radio Signals
• Statistics, Data Science
• Physical Computing
• Bioacoustics

Teaching

Statistics, Physics, Mathematical Modelling

Experience

• Lecturer
  ZHAW Wädenswil
  02 / 2018 - today
• Lecturer in Physics and Mathematics
  School of Engineering, Jönköping University, Schweden
  08 / 2016 - 01 / 2018
• High School Teacher in Physics and Mathematics
  Collège St. Michel Fribourg, Kantonsschule Olten, Kantonsschule Alpenquai Luzern
  08 / 2010 - 07 / 2016
• Scientific Assistant
  Albert Einstein Center for Fundamental Physics, University of Bern
  2005 - 2010

Education and Continuing education

Education

• University Didactics
  Jönköping University, Sweden
  2016 - 2017
• Teaching Diploma for Physics und Mathematics / Studies in Secondary and Higher Education
  PHBern
  2010 - 2011
• Ph.D. / Theoretical Physics
  Albert Einstein Center for Fundamental Physics, University of Bern
  2006 - 2009
• Master of Science / Physics
  Master of Science in Physics, Albert Einstein Center for Fundamental Physics, University of Bern
  2001 - 2006

Continuing Education

CAS Digital Life Sciences: Applied Reinforcement Learning, Machine Learning Fundamentals in Python, Natural Language Processing Fundamentals
ICLS ZHAW Wädenswil
2023

Network

ORCID digital identifier

ORCID ID: 0000-0001-7929-7625

Social media

Linkedin

Projects

• Radio Signal Object Detection / Project leader / ongoing
• Unknown Radio Signal Clustering / Project leader / ongoing
• ChirpScan: Monitoring grasshopper biodiversity with an AI-driven mobile app / Deputy project leader / ongoing
• ChirpNet: AI Grasshopper Biodiversity Monitoring / Project leader / completed
• Radio Signal Unsupervised and Transfer Learning / Project leader / completed
• TinyML Grasshopper Classifier / Deputy project leader / completed
• ZHAW Summer School for HealthTech Innovators / Team member / completed
• Drone Signal Dataset / Project leader / completed
• Drone Alarm / Project leader / completed
• Transformer Networks for Drone Signals / Project leader / completed
• Classification of drone signals / Project leader / completed
• Detection of drone signals / Project leader / 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
• Pedan, V., Popp, M., Rohn, S., Nyfeler, M., & Bongartz, A. (2019). Characterization of phenolic compounds and their contribution to sensory properties of olive oil. Molecules, 24(11), 2041. https://doi.org/10.3390/molecules24112041

Written conference contributions, peer-reviewed

• 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
• Horn, C., Nyfeler, M., Müller, G., & Schüpbach, C. (2022). Drone radio signal detection with multi-timescale deep neural networks [Conference paper]. In S. Y. Yurish (Ed.), Proceedings of the 4th International Conference on Advances in Signal Processing and Artificial Intelligence (pp. 140–143). IFSA Publishing. https://doi.org/10.21256/zhaw-27185

Publications before appointment at the ZHAW

• Nested Cluster Algorithm for Frustrated Quantum Antiferromagnets, M. Nyfeler, F.-J. Jiang, F. Kampfer, U.-J. Wiese, Phys. Rev. Lett. 100, 247206 (2008)
• Loop-Cluster Simulation of the t-J Model on the Honeycomb Lattice, F.-J. Jiang, F. Kämpfer, M. Nyfeler, U.-J. Wiese, Phys. Rev. B 78, 214406 (2008)
• Constraint Effective Potential of the Staggered Magnetization in an Antiferromagnet, U. Gerber, C. P. Hofmann, F.-J. Jiang, M. Nyfeler, U.-J. Wiese, J. Stat. Mech. (2009) P03021
• From an Antiferromagnet to a Valence Bond Solid: Evidence for a First Order Phase Transition, F.-J. Jiang, M. Nyfeler, S. Chandrasekharan, U.-J. Wiese, J. Stat. Mech. (2008) P02009
• Monte Carlo Determination of the Low-Energy Constants of a Spin 1/2 Heisenberg Model with Spatial Anisotropy, F. J. Jiang, F. Kämpfer, M. Nyfeler, Phys. Rev. B 80, 033104 (2009)
• A new efficient cluster algorithm for the Ising model, M. Nyfeler, M. Pepe and U. J. Wiese, PoS LAT2005 (2006) 112

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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