Yuheng Chen NEC Labs America

Yuheng Chen

Researcher

Optical Networking & Sensing

Posts

Explore Benefits of Distributed Fiber Optic Sensing for Optical Network Service Providers

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We review various applications of distributed fiber optic sensing (DFOS) and machine learning (ML) technologies that particularly benefit telecom operators’ fiber networks and businesses. By leveraging relative phase shift of the reflectance of coherent Rayleigh, Brillouin and Raman scattering of light wave, the ambient environmental vibration, acoustic effects, temperature and fiber/cable strain can be detected. Fiber optic sensing technology allows optical fiber to support sensing features in addition to its conventional role to transmit data in telecommunications. DFOS has recently helped telecom operators by adding multiple sensing features and proved feasibility of co-existence of sensing and communication systems on same fiber. We review the architecture of DFOS technique and show examples where optical fiber sensing helps enhance network operation efficiency and create new services for customers on deployed fiber infrastructures, such as determination of cable locations, cable cut prevention, perimeter intrusion detection and networked sensing applications. In addition, edge AI platform allows data processing to be conducted on-the-fly with low latency. Based on discriminative spatial-temporal signatures of different events of interest, real-time processing of the sensing data from the DFOS system provides results of the detection, classification and localization immediately.

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Ambient Noise based Weakly Supervised Manhole Localization Methods over Deployed Fiber Networks

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We present a manhole localization method based on distributed fiber optic sensing and weakly supervised machine learning techniques. For the first time to our knowledge, ambient environment data is used for underground cable mapping with the promise of enhancing operational efficiency and reducing field work. To effectively accommodate the weak informativeness of ambient data, a selective data sampling scheme and an attention-based deep multiple instance classification model are adopted, which only requires weakly annotated data. The proposed approach is validated on field data collected by a fiber sensing system over multiple existing fiber networks.

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Distributed Optical Fiber Sensing Using Specialty Optical Fibers

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Distributed fiber optic sensing systems use long section of optical fiber as the sensing media. Therefore, the fiber characteristics determines the sensing capability and performance. In this presentation, various types of specialty optical fibers and their sensing applications will be introduced and discussed.

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Field Trials of Vibration Detection, Localization and Classification over Deployed Telecom Fiber Cables

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We review sensing fusion results of integrating fiber sensing with video for machine-learning-based localization and classification of impulsive acoustic event detection. Classification accuracy >97% was achieved on aerial coils, and >99% using fiber-based signal enhancers.

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Finite Element Modeling of Pavement and State Awareness Using Fiber Optic Sensing

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A variety of efforts have been put into sensing and modeling of pavements. Such capability is commonly validated with experimental data and used as reference for damage detection and other structural changes. Finite element models (FEM) often provides a high fidelity physics-base benchmark to evaluate the pavement integrity. On the monitoring of roads and pavements in general, FEM combining with in-situ data largely extends the awareness of the pavement condition, and enhances the durability and sustainability for the transportation infrastructures. Although many studies were performed in order to simulate static stress and strain in the pavement, FEM also show potential for dynamic analysis, allowing to extract both frequency response and wave propagation at any location, including the behavior of the soil on the surroundings. Fiber optical sensing is adopted in this research, which outperforms the traditional sensing techniques, such as accelerometers or strain gauges, given its nature of providing continuous measurement in a relatively less intrinsic fashion. Moreover, the data is adopted to validate and calibrate the FEM with complex material properties, such as damping and viscoelasticity of the pavement as well as other nonlinear behavior of the surrounded soil. The results demonstrate a successful FEM with good accuracy of the waveform prediction.

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Field Tests of Impulsive Acoustic Event Detection, Localization, and Classification Over Telecom Fiber Networks

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We report distributed-fiber-optic-sensing results on impulsive acoustic events localization/classification over telecom networks. A deep-learning-based model was trained to classify starter-gun and fireworks signatures with high accuracy of > 99% using fiber-based-signal-enhancer and >97% using aerial coils.

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Perimeter Intrusion Detection with Rayleigh Enhanced Fiber Using Telecom Cables as Sensing Backhaul

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We report field test results of facility perimeter intrusion detection with distributed-fiber-sensing technology and backscattering-enhanced-fiber by using deployed telecom fiber cables as sensing backhaul. Various intrusive activities, such as walking/jumping at >100ft distance, are detected.

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Detection of Road Anomaly Using Distributed Fiber Optic Sensing

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Road surface condition can significantly impact the interaction between vehicles and pavement structure, which may even cause high fuel consumption and safety issues of drivers and vehicles. Distributed fiber optic sensing (DFOS) technology is a useful tool to perform continuous and real-time monitoring of traffic and road surface condition. However, it is challenging to process the data for the purpose of road anomaly detection. The study proposed two approaches to detect the road anomaly using DFOS. In the first method, local binary pattern (LBP) histograms were used to extract the features of the images with and without road anomaly, and support vector machine (SVM) combined with principal component analysis (PCA) was adopted as the classifier. The convolutional neural network (CNN) was applied on the binary classification data to analyze the images in the second method. The accuracy and benefits of two methodologies were compared. The vehicle speed was estimated by detecting lines using Hough transform. The feasibility of road anomaly detection using DFOS is proved.

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AI-Driven Applications over Telecom Networks by Distributed Fiber Optic Sensing Technologies

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By employing distributed fiber optic sensing (DFOS) technologies, field deployed fiber cables can be utilized as not only communication media for data transmissions but also sensing media for continuously monitoring of the physical phenomenon along the entire route. The fiber can be used to monitor ambient environment along the route covering a wide geographic area. With help of artificial intelligence and machine learning (AI/ML) technologies on information processing, many applications can be developed over telecom networks. We review the recent field results and demonstrate how DFOS can work with existing communication channels and provide holistic view of road traffic monitoring included vehicle counts and average vehicle speeds. A long-term wide-area road traffic monitoring system is an efficient way of gathering seasonal vehicle activities which can be applied in future smart city applications. Additionally, DFOS also offers cable cut prevention functions such as cable self-protection and cable cut threat assessment. Detection and localization of abnormal events and evaluating the threat to the cable are realized to protect telecom facilities.

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First Field Trial of Monitoring Vehicle Traffic on Multiple Routes by Using Photonic Switch and Distributed Fiber Optics Sensing System on Standard Telecom Networks

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We demonstrated for the first time that motor vehicle traffic and road capacity on multiple fiber routes can be monitored by using a distributed-fiber-optics-sensing system with a photonic switch on in-service telecom fiber cables.

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