---
_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'
...
---
_id: '21542'
abstract:
- lang: eng
  text: Using near-field (NF) scan data to predict the far-field (FF) behaviour of
    radiating electronic systems represents a novel method to accompany the whole
    RF design process. This approach involves so-called Huygens' box as an efficient
    radiation model inside an electromagnetic (EM) simulation tool and then transforms
    the scanned NF measured data into the FF. For this, the basic idea of the Huygens'box
    principle and the NF-to-FF transformation are briefly presented. The NF is measured
    on the Huygens' box around a device under test using anNF scanner, recording the
    magnitude and phase of the site-related magnetic and electric components. A comparison
    between a fullwave simulation and the measurement results shows a good similarity
    in both the NF and the simulated and transformed FF.Thus, this method is applicable
    to predict the FF behaviour of any electronic system by measuring the NF. With
    this knowledge, the RF design can be improved due to allowing a significant reduction
    of EM compatibility failure at the end of the development flow. In addition, the
    very efficient FF radiation model can be used for detailed investigations in various
    environments and the impact of such an equivalent radiation source on other electronic
    systems can be assessed.
author:
- first_name: Dominik
  full_name: Schröder, Dominik
  last_name: Schröder
- first_name: Sven
  full_name: Lange, Sven
  id: '38240'
  last_name: Lange
- first_name: Christian
  full_name: Hangmann, Christian
  last_name: Hangmann
- first_name: Christian
  full_name: Hedayat, Christian
  last_name: Hedayat
citation:
  ama: 'Schröder D, Lange S, Hangmann C, Hedayat C. Far-field prediction combining
    simulations with near-field measurements for EMI assessment of PCBs. In: <i>Tensorial
    Analysis of Networks (TAN) Modelling for PCB Signal Integrity and EMC Analysis</i>.
    1st ed. Croyton, UK:  The Institution of Engineering and Technology (IET); 2020:315-346
    (32). doi:<a href="https://doi.org/10.1049/pbcs072e_ch14">10.1049/pbcs072e_ch14</a>'
  apa: 'Schröder, D., Lange, S., Hangmann, C., &#38; Hedayat, C. (2020). Far-field
    prediction combining simulations with near-field measurements for EMI assessment
    of PCBs. In <i>Tensorial Analysis of Networks (TAN) Modelling for PCB Signal Integrity
    and EMC Analysis</i> (1st ed., pp. 315-346 (32)). Croyton, UK:  The Institution
    of Engineering and Technology (IET). <a href="https://doi.org/10.1049/pbcs072e_ch14">https://doi.org/10.1049/pbcs072e_ch14</a>'
  bibtex: '@inbook{Schröder_Lange_Hangmann_Hedayat_2020, place={Croyton, UK}, edition={1},
    title={Far-field prediction combining simulations with near-field measurements
    for EMI assessment of PCBs}, DOI={<a href="https://doi.org/10.1049/pbcs072e_ch14">10.1049/pbcs072e_ch14</a>},
    booktitle={Tensorial Analysis of Networks (TAN) Modelling for PCB Signal Integrity
    and EMC Analysis}, publisher={ The Institution of Engineering and Technology (IET)},
    author={Schröder, Dominik and Lange, Sven and Hangmann, Christian and Hedayat,
    Christian}, year={2020}, pages={315-346 (32)} }'
  chicago: 'Schröder, Dominik, Sven Lange, Christian Hangmann, and Christian Hedayat.
    “Far-Field Prediction Combining Simulations with near-Field Measurements for EMI
    Assessment of PCBs.” In <i>Tensorial Analysis of Networks (TAN) Modelling for
    PCB Signal Integrity and EMC Analysis</i>, 1st ed., 315-346 (32). Croyton, UK:  The
    Institution of Engineering and Technology (IET), 2020. <a href="https://doi.org/10.1049/pbcs072e_ch14">https://doi.org/10.1049/pbcs072e_ch14</a>.'
  ieee: 'D. Schröder, S. Lange, C. Hangmann, and C. Hedayat, “Far-field prediction
    combining simulations with near-field measurements for EMI assessment of PCBs,”
    in <i>Tensorial Analysis of Networks (TAN) Modelling for PCB Signal Integrity
    and EMC Analysis</i>, 1st ed., Croyton, UK:  The Institution of Engineering and
    Technology (IET), 2020, pp. 315-346 (32).'
  mla: Schröder, Dominik, et al. “Far-Field Prediction Combining Simulations with
    near-Field Measurements for EMI Assessment of PCBs.” <i>Tensorial Analysis of
    Networks (TAN) Modelling for PCB Signal Integrity and EMC Analysis</i>, 1st ed.,  The
    Institution of Engineering and Technology (IET), 2020, pp. 315-346 (32), doi:<a
    href="https://doi.org/10.1049/pbcs072e_ch14">10.1049/pbcs072e_ch14</a>.
  short: 'D. Schröder, S. Lange, C. Hangmann, C. Hedayat, in: Tensorial Analysis
    of Networks (TAN) Modelling for PCB Signal Integrity and EMC Analysis, 1st ed.,  The
    Institution of Engineering and Technology (IET), Croyton, UK, 2020, pp. 315-346
    (32).'
date_created: 2021-03-18T13:49:49Z
date_updated: 2022-01-06T06:55:03Z
department:
- _id: '485'
doi: 10.1049/pbcs072e_ch14
edition: '1'
keyword:
- Huygens' box
- NF-to-FF transformation
- efficient FF radiation model
- FF behaviour
- EMI assessment
- PCB
- near-field measurements
- efficient radiation model
- far-field behaviour
- RF design process
- far-field prediction
- Huygens'box principle
- fullwave simulation
- electronic system radiation
- equivalent radiation source
- electromagnetic simulation tool
- near-field scan data
- EM compatibility failure reduction
language:
- iso: eng
main_file_link:
- url: https://digital-library.theiet.org/content/books/10.1049/pbcs072e_ch14
page: 315-346 (32)
place: Croyton, UK
project:
- _id: '52'
  name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Tensorial Analysis of Networks (TAN) Modelling for PCB Signal Integrity
  and EMC Analysis
publication_identifier:
  isbn:
  - '9781839530494'
  - '9781839530500'
publication_status: published
publisher: ' The Institution of Engineering and Technology (IET)'
related_material:
  record:
  - id: '21542'
    relation: other
    status: public
status: public
title: Far-field prediction combining simulations with near-field measurements for
  EMI assessment of PCBs
type: book_chapter
user_id: '38240'
year: '2020'
...