CLEO US 2025

SoFiN Project results from the work on algorithms developed for machine learning-enhanced fiber sensing to extract meaningful information from sensing measurement data, enhance signal quality under various noise conditions and further model the Rayleigh backscattering process in #OTDR systems, were presented last week at the world’s leading event on lasers, photonics, and electro-optics; CLEO Conference US 2025.

The conference paper describing part of the ML work developed within SoFiN and titled “Method for Conversion of Optical Phase to Temperature for Coherent φ-OTDR” stems from a joined work from partners DTU – Technical University of Denmark and Adtran with many thanks to all colleagues starting with Ermakov, R., and including Azendorf, F., Wang, H., Sandmann, A., Da Ros, F., & Zibar, D. From DTU team, colleague Francesco Da Ros presented the progress on the algorithms developed to convert the recovered optical phase to temperature change in Phase-sensitive Optical Time Domain Reflectometry (φ-OTDR).

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