Yuheng Chen is a Researcher in the Optical Sensing & Solutions Department at NEC Laboratories America in Princeton, NJ. He earned his Ph.D. in Aerospace Engineering from the Technion – Israel Institute of Technology, where his thesis focused on laser-induced aerosol dynamics and spectroscopy. He also holds an M.S. in Automatic Control Engineering and a B.S. in Automatic Control Engineering from Huazhong University of Science and Technology, China. Before joining NEC, Dr. Chen conducted postdoctoral research at Technion, Princeton University in the Department of Geosciences, and the Biodesign Institute at Arizona State University, where he expanded his expertise in spectroscopy, optical sensing, and chemical detection.
Dr. Chen’s research focuses on advancing distributed optical fiber sensing and developing innovative signal reconstruction methods that enhance both sensitivity and scalability. By leveraging coherent reflectometry, acoustic backscatter, and advanced inference techniques, he designs sensing architectures capable of detecting subtle variations in vibration, strain, and temperature across large-scale infrastructures. His work addresses key challenges in noise reduction, resolution enhancement, and low-power implementation, enabling distributed acoustic sensing (DAS) systems that are both cost-effective and robust under real-world conditions. His publications in venues such as Journal of Lightwave Technology and IEEE Transactions on Intelligent Transportation Systems highlight his impact on both theoretical advances and applied engineering solutions.
At NEC, Dr. Chen integrates physics-based modeling with AI-enhanced signal processing to develop the next generation of intelligent fiber-optic sensing systems. His contributions directly support NEC’s leadership in smart infrastructure monitoring, digital twins, and real-time diagnostics for transportation, energy, and communications networks. From detecting roadway anomalies and monitoring bridges and tunnels to enabling predictive maintenance in industrial systems, his work transforms complex optical signals into actionable insights. By combining optics, signal processing, and applied AI, he is helping create scalable sensing platforms that improve safety, reliability, and long-term resilience in critical infrastructure worldwide.
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 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.
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.
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.
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.
We report the distributed-fiber-sensing field trial results over a 5G-transport-network. A standard communication fiber is used with real-time AI processing for cable self-protection, cable-cut threat assessment and road traffic monitoring in a long-term continuous test.
We demonstrate a new application of fiber-optic-sensing and machine learning techniques for vehicle run-off-road events detection to enhance roadway safety and efficiency. The proposed approach achieves high accuracy in a testbed under various experimental conditions.
We report the field trial results of monitoring abnormal activities near deployed cable with fiber-optic-sensing technology for cable protection. Detection and position determination of abnormal events and evaluating the threat to the cable is realized.
We demonstrate fiber optic sensing systems in a distributed fiber sensor network built on existing telecom infrastructure to detect temperature, acoustic effects, vehicle traffic, etc. Measurements are also demonstrated with different network topologies and simultaneously sensing four fiber routes with one system.
To the best of our knowledge, we present the first underground fiber cable position detection methods using distributed fiber optic sensing (DFOS) technology. Meter level localization accuracy is achieved in the results.