Yuheng Chen NEC Labs America

Yuheng Chen is a Researcher in the Optical Networking and Sensing 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.

Posts

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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Field Trial of Cable Safety Protection and Road Traffic Monitoring over Operational 5G Transport Network with Fiber Sensing and On-Premise AI Technologies

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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.

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Vehicle Run-Off-Road Event Automatic Detection by Fiber Sensing Technology

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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.

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Field Trial of Abnormal Activity Detection and Threat Level Assessment with Fiber Optic Sensing for Telecom Infrastructure Protection

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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.

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Field Trial of Distributed Fiber Sensor Network Using Operational Telecom Fiber Cables as Sensing Media

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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.

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New Methods for Non-Destructive Underground Fiber Localization using Distributed Fiber Optic Sensing Technology

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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.

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Chemical profiling of red wines using surface-1 enhanced Raman spectroscopy (SERS)

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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 a reproducible SERS substrate, the AgNP mirror, previously developed by our group. However, as previously reported for red wine analysis, the signals obtained through these approaches were either due to interference of the fluorescence exhibited by pigments or mainly attributed to a DNA fraction, adenine. Therefore, an innovative approach was developed using solvent extraction to provide more characteristic information that is beneficial for wine chemical profiling and discrimination. Signature peaks in the wine extract spectra were found to match those of condensed tannins, resveratrol, anthocyanins, gallic acid, and catechin, which indicated that SERS combined with extraction is an innovative method for profiling wine chemicals and overcoming well-known challenges in red wine analysis. Based on this approach, we have successfully differentiated three red wines and demonstrated the possible relation between the overall intensity of wine spectra and the ratings. Since the wine chemical profile is closely related to the grape species, wine quality, and wine authentication, the SERS approach to obtain rich spectral information from red wine could advance wine chemical analysis.

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First Proof That Geographic Location on Deployed Fiber Cable Can Be Determined by Using OTDR Distance Based on Distributed Fiber Optical Sensing Technology

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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.

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First Field Trial of Distributed Fiber Optical Sensing and High-Speed Communication Over an Operational Telecom Network

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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) (48-Gbaud channels, 50-GHz channel spacing) was achieved. Employing DFOS technology, road traffic, i.e., vehicle speed and vehicle density, were sensed with 98.5% and 94.5% accuracies, respectively, as compared to video analytics. Additionally, road conditions, i.e., roughness level was sensed with >85% accuracy via a machine learning based classifier.

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