---
_id: '34140'
abstract:
- lang: eng
  text: In this paper, machine learning techniques will be used to classify different
    PCB layouts given their electromagnetic frequency spectra. These spectra result
    from a simulated near-field measurement of electric field strengths at different
    locations. Measured values consist of real and imaginary parts (amplitude and
    phase) in X, Y and Z directions. Training data was obtained in the time domain
    by varying transmission line geometries (size, distance and signaling). It was
    then transformed into the frequency domain and used as deep neural network input.
    Principal component analysis was applied to reduce the sample dimension. The results
    show that classifying different designs is possible with high accuracy based on
    synthetic data. Future work comprises measurements of real, custom-made PCB with
    varying parameters to adapt the simulation model and also test the neural network.
    Finally, the trained model could be used to give hints about the error’s cause
    when overshooting EMC limits.
author:
- first_name: Jad
  full_name: Maalouly, Jad
  last_name: Maalouly
- first_name: Dennis
  full_name: Hemker, Dennis
  last_name: Hemker
- first_name: Christian
  full_name: Hedayat, Christian
  last_name: Hedayat
- first_name: Christian
  full_name: Rückert, Christian
  last_name: Rückert
- first_name: Ivan
  full_name: Kaufmann, Ivan
  last_name: Kaufmann
- first_name: Marcel
  full_name: Olbrich, Marcel
  last_name: Olbrich
- first_name: Sven
  full_name: Lange, Sven
  id: '38240'
  last_name: Lange
- first_name: Harald
  full_name: Mathis, Harald
  last_name: Mathis
citation:
  ama: 'Maalouly J, Hemker D, Hedayat C, et al. AI Assisted Interference Classification
    to Improve EMC Troubleshooting in Electronic System Development. In: <i>2022 Kleinheubach
    Conference</i>. IEEE; 2022.'
  apa: Maalouly, J., Hemker, D., Hedayat, C., Rückert, C., Kaufmann, I., Olbrich,
    M., Lange, S., &#38; Mathis, H. (2022). AI Assisted Interference Classification
    to Improve EMC Troubleshooting in Electronic System Development. <i>2022 Kleinheubach
    Conference</i>. 2022 Kleinheubach Conference, Miltenberg, Germany.
  bibtex: '@inproceedings{Maalouly_Hemker_Hedayat_Rückert_Kaufmann_Olbrich_Lange_Mathis_2022,
    place={Miltenberg, Germany}, title={AI Assisted Interference Classification to
    Improve EMC Troubleshooting in Electronic System Development}, booktitle={2022
    Kleinheubach Conference}, publisher={IEEE}, author={Maalouly, Jad and Hemker,
    Dennis and Hedayat, Christian and Rückert, Christian and Kaufmann, Ivan and Olbrich,
    Marcel and Lange, Sven and Mathis, Harald}, year={2022} }'
  chicago: 'Maalouly, Jad, Dennis Hemker, Christian Hedayat, Christian Rückert, Ivan
    Kaufmann, Marcel Olbrich, Sven Lange, and Harald Mathis. “AI Assisted Interference
    Classification to Improve EMC Troubleshooting in Electronic System Development.”
    In <i>2022 Kleinheubach Conference</i>. Miltenberg, Germany: IEEE, 2022.'
  ieee: J. Maalouly <i>et al.</i>, “AI Assisted Interference Classification to Improve
    EMC Troubleshooting in Electronic System Development,” presented at the 2022 Kleinheubach
    Conference, Miltenberg, Germany, 2022.
  mla: Maalouly, Jad, et al. “AI Assisted Interference Classification to Improve EMC
    Troubleshooting in Electronic System Development.” <i>2022 Kleinheubach Conference</i>,
    IEEE, 2022.
  short: 'J. Maalouly, D. Hemker, C. Hedayat, C. Rückert, I. Kaufmann, M. Olbrich,
    S. Lange, H. Mathis, in: 2022 Kleinheubach Conference, IEEE, Miltenberg, Germany,
    2022.'
conference:
  end_date: 2022-09-29
  location: Miltenberg, Germany
  name: 2022 Kleinheubach Conference
  start_date: 2022-09-27
date_created: 2022-11-24T14:21:17Z
date_updated: 2022-11-24T14:21:34Z
department:
- _id: '59'
- _id: '485'
keyword:
- emc
- pcb
- electronic system development
- machine learning
- neural network
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9954484
place: Miltenberg, Germany
project:
- _id: '52'
  name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: 2022 Kleinheubach Conference
publication_identifier:
  eisbn:
  - 978-3-948571-07-8
publication_status: published
publisher: IEEE
status: public
title: AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic
  System Development
type: conference
user_id: '38240'
year: '2022'
...