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| URL | https://doi.org/10.18429/JACoW-ICALEPCS2025-WEMG006 |
|---|---|
| Title | An FPGA-based autoencoder model for real-time RF signal denoising for industrial accelerators |
| Authors |
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| Abstract | A challenge that industrial particle accelerators face is the high amounts of noise in sensor readings. This noise obscures essential beam diagnostic and operational data, limiting the amount of information that is relayed to machine operators and beam instrumentation engineers. Machine learning-based techniques have shown great promise in isolating noise patterns while preserving high-fidelity signals, enabling more accurate diagnostics and performance tuning. Our work focuses on the implementation of a real-time FPGA-based noise reduction autoencoder, tested on a Xilinx ZCU104 evaluation kit with the intention of being deployed on industrial particle accelerators in the near future. |
| Paper | download: WEMG006.pdf |
| Cite | BibTeX, LaTeX, Text/Word, RIS, EndNote |
| Conference | 20th Int. Conf. Accel. Large Exp. Phys. Control Syst. (ICALEPCS'25) |
| Series | |
| Location | Chicago, IL, USA |
| Date | 20-26 Sep 2025 |
| Publisher | JACoW Publishing, Geneva, Switzerland |
| Editorial Board | Kent Wootton (ANL), Jeffery Biss (FNAL), Gustavo Bruno (ANL), Jeff Kirchman (ANL), Kelly Jaje (ANL), Denise Skiadopoulos (ANL), SuYin Grass Wang (ANL) |
| Online ISBN | 978-3-95450-255-4 |
| Online ISSN | 2226-0358 |
| Received | 05 September 2025 |
| Revised | 18 September 2025 |
| Accepted | 21 October 2025 |
| Issued | 25 November 2025 |
| DOI | 10.18429/JACoW-ICALEPCS2025-WEMG006 |
| Pages | 928-932 |
| Copyright | Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |