Advances in Radio Science
Advances in Radio Science
ARS
Articles | Volume 21
Articles | Volume 21
https://doi.org/10.5194/ars-21-25-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ars-21-25-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 21
01 Dec 2023
 | 01 Dec 2023

Mathematical optimization and machine learning to support PCB topology identification

Ilda Cahani and Marcus Stiemer

Viewed

Total article views: 664 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
500 141 23 664 28 25
  • HTML: 500
  • PDF: 141
  • XML: 23
  • Total: 664
  • BibTeX: 28
  • EndNote: 25
Views and downloads (calculated since 01 Dec 2023)
Cumulative views and downloads (calculated since 01 Dec 2023)

Viewed (geographical distribution)

Total article views: 672 (including HTML, PDF, and XML) Thereof 672 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 04 Jun 2025
Download
Short summary
A framework to assist to the modeling, classification, and identification of PCB designs involving different topological approaches is proposed based on the analysis of scattering parameters. The underlying idea is to break down the relevant information hidden in the network topology to a flattened latent space via an encoder-decoder structure. Various machine learning and optimization methods are proposed using the trained encoder or decoder as upstream or downstream component.
Read more
Share