For the first time, we present an end-to-end AI framework for data analysis in distributed fiber optic sensing. The proposed model eliminates the need for optical phase computation and outperforms traditional data processing pipelines, achieving over 96% recognition accuracy on a diverse acoustic dataset.
Distributed fiber-optic sensing combined with machine learning enables continuous monitoring of telecom infrastructure. We employ generative modeling for event classification, supporting semi supervised learning, uncertainty calibration, and noise resilience. Our approach offers a scalable, data-efficient solution for real-world deployment in complex environments.
We experimentally investigated variable spectral loading in an OMS, identifying performance under best and worst transmission conditions. Metrics and data visualization allowed correlation between channel configurations and OSNR variations, enabling the derivation of a simple spectrum allocation rule.
The 2021 emergence of Brood X cicadas was monitored in situ in our testbed using a DAS system connected to an outdoor telecom fiber over a 16-day period. The spectral and energy characteristics of the cicada calling signal has been measured and analyzed.
We report a record long 200.6 km distributed acoustic sensing (DAS) link without inline ampli-fication, 28.6% improvement of sensing range has been achieved by using three segments of enhanced-scattering fibre (ESF) with progressively higher scattering enhancements.
Fault localization in power distribution networks is essential for rapid recovery and enhancing system resilience. While Phasor Measurement Units (PMUs or ?PMUs) providehigh-resolution measurements for precise fault localization, their widespread deployment is cost-prohibitive. Distributed Fiber Optic Sensing (DFOS) offers a promising alternative for event detection along power lines using collocated optical fiber; however, it cannot independently differentiate between events and pinpoint exact fault locations. This paper introduces an innovative framework that combines DFOS with sparsely deployed PMUs for accurate fault localization. The proposed approach first utilizes a Graph Attention Network (GAT) model to capture spatial and temporal correlations from synchronized PMU and DFOS measurements, effectively identifying fault zones. High-spatial- resolution DFOS measurements further refine the fault locationwithin the identified zone. Singular Value Decomposition (SVD) is applied to extract feature vectors from DFOS measurements, enhancing the convergence speed of the GAT model. Thisintegrated solution significantly improves localization accuracy while minimizing reliance on extensive deployment of PMUs.
We propose to apply optical circuit switching to enable dynamic AllReduce reconfiguration for accelerating distributed machine learning. With simulated annealing-based optimization, theproposed AllReduce reconfiguration approach achieves 31% less average training time than existing solutions.
We demonstrate real-time fiber risk assessment and dynamic network routing in live metro networks using deployed DASs, satellite imagery, and large-scale AI, achieving the first significantreduction in fiber failures in four years
Distributed fiber optics sensing is applied for traffic management in the intersection. The high-definition fiber sensing data streaming is applied as source and YOLO computer vision model isemployed for event detection classification and localization.
This paper outlines QoT-driven optimization strategies in coherent fiber-optic WDM networks, addressing distinct transmission scenarios, QoT metrics, control-plane methodologies, and emerging trends to enhance network reliability, flexibility and capacity.
