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 Unsupervised and Transfer Learning / Project leader / ongoing
• ChirpNet: AI Grasshopper Biodiversity Monitoring / 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

• 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

• Pedan, Vasilisa; Popp, Martin; Rohn, Sascha; Nyfeler, Matthias; Bongartz, Annette,

  2019.

  Characterization of phenolic compounds and their contribution to sensory properties of olive oil.

  Molecules.

  24(11), pp. 2041.

  Available from: https://doi.org/10.3390/molecules24112041

• 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

• Horn, Claus; Nyfeler, Matthias; Müller, Georg; Schüpbach, Christof,

  2022.

  Drone radio signal detection with multi-timescale deep neural networks[paper].

  In:

  Yurish, Sergey Y., ed.,

  Proceedings of the 4th International Conference on Advances in Signal Processing and Artificial Intelligence.

  4th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI), Corfu, Greece, 19-21 October 2022.

  IFSA Publishing.

  pp. 140-143.

  Available from: 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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