Summary of Threat Classification on Deployed Optical Networks Using Mimo Digital Fiber Sensing, Wavelets, and Machine Learning, by Khouloud Abdelli et al.
Threat Classification on Deployed Optical Networks Using MIMO Digital Fiber Sensing, Wavelets, and Machine Learning
by Khouloud Abdelli, Henrique Pavani, Christian Dorize, Sterenn Guerrier, Haik Mardoyan, Patricia Layec, Jeremie Renaudier
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a machine learning framework that demonstrates mechanical threats classification in an optical network. The framework leverages the wavelet transform of output data from Multiple-Input-Multiple-Output (MIMO) Differential Frequency Shift (DFS) across a 57-km operational network link. The model incorporates transfer learning and achieves 93% classification accuracy using field data, which has potential benefits for optical network supervision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a machine learning system that can identify mechanical threats in an optical network. They used data from a 57-kilometer long network and applied a type of mathematical transformation called the wavelet transform to help their model learn patterns. The model was trained using data from previous similar projects, which helped it perform well with new, unseen data. This system has the potential to improve how networks are monitored for problems. |
Keywords
» Artificial intelligence » Classification » Machine learning » Transfer learning
