OPTICAL SENSING & SOLUTIONS
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Optical Sensing & Solutions
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.
NEC Laboratories America has formed two new departments from its Optical Networking and Sensing group. Optical Communications and Computing, led by Yue-Kai Huang, and Optical Sensing and Solutions, led by Yue Tian, will carry the lab’s photonics research into new territory.
We present methods and field trial results demonstrating an integrated distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) system for manhole localization, condition diagnostics, and anomaly detection in pre-deployed telecommunication fiber networks. The proposed system leverages
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 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.
Field trials of ambient noise-based automated methods for manhole localization and condition diagnostics using a real-time DAS/DTS integrated system were conducted. Crossreferencingmultiple sensing data resulted in a 94.7% detection rate and enhanced anomaly identification.
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
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
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
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.
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
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
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
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
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
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.
In this study, we explored surface-enhanced Raman spectroscopy (SERS) for analyzing red wine through several facile sample preparations. These approaches involved the direct analysis of red wine with Raman spectroscopy and the direct incubation of red wine with silver nanoparticles (i.e., AgNPs) and
We demonstrated for the first time that geographic locations on deployed fiber cables can be determined accurately by using OTDR distances. The method involves vibration stimulation near deployed cables and distributed fiber optical sensing technology.
To the best of our knowledge, we present the first field trial of distributed fiber optical sensing (DFOS) and high-speed communication, comprising a coexisting system, over an operation telecom network. Using probabilistic-shaped (PS) DP-144QAM, a 36.8 Tb/s with an 8.28-b/s/Hz spectral efficiency (SE)
For the first time, we demonstrate detection of vehicle speed, density, and road conditions using deployed fiber carrying high-speed data transmission, and prove carriers’ large-scale fiber infrastructures can also be used as ubiquitous sensing networks.