Our Optical Networking and Sensing department is leading world-class research into the next generation of optical networks and sensing systems that will power ICT-based social solutions for years. From forward-looking theoretical studies to cutting-edge experiments to world- and industry-first technology field trials, we deliver globally recognized innovation that looks into the future and translates it into present reality. Read our optical networking and sensing news and publications from our team of researchers.
We explore two fiber sensing methods which enables coexistence with data transmission on DWDM fiber networks. Vibration detection and localization can be achieved by extracting optical phase from modified coherent transponders. Frequency-diverse chirped-pulse DAS with all-Raman amplification can improve SNR and achieves multi-span monitoring.
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.
Among the power transmission infrastructures, the low-voltage overhead power lines are specifically critical, due to the complicated roadside environments and the significant number of connections to the end utility users. Maintaining of such a large size grid with mostly wood poles is a challenging task and knowing the operating status and its structural integrity drastically speeds up the routine inspection. Applying a data-driven approach using accelerometer data to analyze the power line-induced vibration to classify different poles within different operational conditions is proposed.Feature creation is the important aspect to improve an accuracy of data-driven algorithms. For this purpose, a time-frequency domain classifier is developed, based on the data collected from two tri-axial accelerometers installed on the wood poles before and after streetlights are on. Data are explored both in time and frequency domain using techniques such as data augmentation and segmentation, averaging, filtering, and principal component analysis. Results of the machine learning classifier clearly shows distinct characteristics among the data collected from different work conditions and different poles. Further exploration of the applied algorithm will be pursued to construct more sophisticated features based on supervised learning to enhance the identification accuracy.
This paper presents a deep learning-based framework to detect and localize the pedestrians’ anomaly behaviors in videos captured at the grade crossing. A skeleton detection and tracking algorithm are employed to capture the key point trajectories of body movements of the pedestrians. A deep recurrent neural network is applied to learn the normal patterns of pedestrians’ movements using dynamics skeleton trajectories features. An anomaly behaviors detection and localization algorithm are developed by analyzing each pedestrian’s reconstructed trajectories. In the experiments, a video dataset involving normal pedestrian behaviors is established by collecting data at multiple grade crossing spots with different camera angles. Then the proposed framework is trained on the dataset to learn the regularity patterns of normal pedestrians and localize the anomaly behaviors during the testing phase. To the best of our knowledge, it is the first attempt to analyze pedestrians’ behavior at a grade crossing. The experimental results show that the proposed framework can detect and localize the anomaly behaviors, such as squatting down, lingering, and other behaviors that may cause safety issues at the grade crossing. Our study also points out the direction for further improvement of the present development to meet the need for real-world applications.
For the first time, we demonstrate detection and classification of rain intensity using Distributed Acoustic Sensing (DAS). An artificial neural network was applied for rain intensity classification and high precision of over 96% was achieved.
We review multiple use cases over deployed networks including co-existing sensing/data transmission, cable cut prevention and perimeter intrusion detection to realize telecom infrastructure can be sensing backbones instead of the sole function of data transmission.
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.
We review recent advances aimed at increasing the reach of distributed fiber optic sensing with simultaneous data transmission. We review two methods based on measurement of accumulated phase on telecom signals, and chirp-pulsed DAS with inline amplification and frequency diversity.
We propose a new method to detect acoustic signals by matching distributed acoustic sensing data with simulation. In the simulation of the dynamic strain on an optical fiber, the optical fiber layouts and the gauge length are properly incorporated. We apply the proposed method to the acoustic-source localization and demonstrate the method localizes the source accurately even under the layouts which include the straight optical fiber for the sensing points with the large gauge-length settings.
Recently, the distributed fiber optic sensing (DFOS) techniques have advanced rapidly. There emerges various types of DFOS sensors that can monitor physical parameters such as temperature, strain, and vibration. With these DFOS sensors deployed, the telecom networks are capable of offering additional services beyond communications, such as monitoring road traffic condition, monitoring utility pole health, monitoring city noise and accident, thus evolving to a new paradigm of Infrastructure-as-a-Sensor (IaaSr) or Network-as-a-Sensor (NaaSr). When telecom network carriers upgrade their infrastructures with DFOS sensors to provide such IaaSr/NaaSr services, there will arise a series of critical challenges: (1) where to place the DFOS sensors, and (2) how to provision the DFOS sensing fiber routes to cover the whole network infrastructures with the minimum number of DFOS sensors? We name this as the DFOS placement problem. In this paper, we prove that the DFOS placement problem is an NP-hard problem, and we analyze the upper bound of the number of DFOS sensors used. To facilitate the optimal solution, we formulate the DFOS placement problem with an Integer Linear Programming model that aims at minimizing the number of DFOS sensors used. Furthermore, we propose a cost-efficient heuristic solution, called Explore-and-Pick (EnP), which can achieve a close-to-optimal performance in a fast manner. We analyze the approximation ratio and the computational complexity of the proposed EnP algorithm. In addition, we conduct comprehensive simulations to evaluate the performance of the proposed solutions. Simulation results show that the EnP algorithm can outperform the baseline algorithm by 16% in average and 26% at best, and it achieves a performance that is close to the optimal result obtained by ILP.
