Optical Fiber Sensors: Shedding More Light with Machine Learning

Dear Colleagues,

Optical fiber sensing technologies are at the cutting edge of modern sensing systems, heralding a new era in precision measurement and real-time data acquisition. These technologies capitalize on the unique properties of optical fibers, transforming them into dynamic platforms capable of detecting, monitoring, and analyzing a wide range of physical parameters. At the core of optical fiber sensing is the sophisticated use of optical fibers to transmit, receive, and modulate light signals. This fundamental principle enables the development of sensors with unparalleled sensitivity to variations in temperature, strain, pressure, and other environmental factors. Unlike conventional sensing methods, optical fiber sensors offer immunity to electromagnetic interference, making them exceptionally suited for deployment in demanding and high-performance environments. This intrinsic advantage ensures accurate and reliable data collection, even in the most challenging conditions, positioning optical fiber sensing technologies as indispensable tools in the realm of advanced measurement and monitoring. However, the demodulation of optical fiber sensors signals and their interpretation are still challenging tasks in some cases. Therefore, the application of machine learning techniques to optical fiber sensors signal processing leads to a positive synergetic effect, leading to more efficient solutions in healthcare, structure health monitoring, industrial inspection, and many other technologies.

This Special Issue seeks to publish high-quality papers that explore the integration of machine learning with various fiber-based sensor technologies. We welcome research that delves into a diverse array of topics, including, but not limited to, the following: the application of machine learning techniques for processing of complex responses of optical fiber sensors, including distributed sensors; solving signal demodulation tasks in optical fiber sensors using machine learning techniques, including speckle pattern processing, multimode interference signal demodulation, fading reduction, and response linearization in distributed optical fiber sensors; application of machine learning techniques to advancing design process and optimization of optical fiber sensors; and simulation of optical fiber sensors signals using machine learning techniques. Considered machine learning techniques cover both classical machine learning as well as deep artificial neural networks.

Dr. Koustav Dey
Prof. Dr. Nikolai Ushakov
Prof. Sourabh Roy
Dr. Elena De Vita
Guest Editors

Manuscript Submission Information

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• machine learning
• deep learning
• fiber gratings
• sensor signal processing
• fiber optic interferometers
• multimode interference (MMI)
• physical, chemical and bio sensors
• distributed fiber optic sensors
• fiber optic specklegram sensors
• fiber vortex sensors