Machine Learning-Enhanced Fiber Optic Sensing: From Materials to Applications
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".
Deadline for manuscript submissions: 25 August 2026 | Viewed by 4
Special Issue Editors
Interests: optical sensors; fiber optics sensors; optical diagnostics and sensing; spectroscopy; Industry 4.0 plant; localization; RFID; WiFi; WLAN; positioning; asset tracking; traffic monitoring
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; distributed optical fiber sensor; deep learning; optical fiber sensor; sensors; temperature sensor; humidity sensor; fiber Bragg gratings; fiber loop mirror sensor; fiber laser; spectroscopy; FTIR
Special Issue Information
Dear Colleagues,
Fiber optic sensing technologies have become key tools in a wide range of applications, from structural health monitoring and materials testing to biomedical diagnostics and environmental monitoring. With the rapid development of machine learning (ML) and artificial intelligence (AI), there are now new opportunities to enhance the performance, sensitivity, and versatility of fiber optic sensors.
This Special Issue will cover advances in machine learning-enhanced fiber optic sensing, including novel sensor materials and designs, innovative uses of existing sensors, and the application of ML-based signal processing to improve measurement accuracy and enable real-time data interpretation. We are seeking contributions that focus on topics of interest in this area, which include, but are not limited to, new fiber-based sensor concepts, integration of ML algorithms with distributed sensing techniques, data-driven noise reduction, predictive maintenance, and evaluation of ML-enhanced sensing systems in real-world environments. Both original research and review papers are welcome.
Dr. Boguslaw Szczupak
Dr. Mateusz Mądry
Guest Editors
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Keywords
- machine learning for fiber optic sensing
- artificial intelligence in photonic sensors
- fiber Bragg grating (FBG) signal processing
- optical frequency domain reflectometry (OFDR) with ML
- distributed fiber sensing and data-driven methods
- noise reduction and feature extraction in optical signals
- predictive maintenance using fiber sensor networks
- structural health monitoring with ML-enhanced fiber sensors
- smart materials and intelligent sensing platforms
- IoT integration of fiber optic sensors
- deep learning for optical signal interpretation
- adaptive algorithms for real-time fiber sensing
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