Yaowen Li is a Senior Researcher in the Optical Sensing & Solutions Department at NEC Laboratories America. He obtained both B.S. and M.S. degrees in mechanical engineering in China. After spending seven years working on mechanical structural testing and analysis and precision optical measurement techniques, he went on to obtain his Ph.D. degree in mechanical engineering from the University of Maryland, College Park.
His thesis focused on fiber optic sensors and their demodulation schemes for dynamic strain measurements. Since his graduation, his work has been mainly on fiber Bragg gratings for telecom and industrial applications, fiber optic colorless tunable dispersion compensation devices for telecom, high-power fiber lasers, and fiber optic LiDAR systems.
His work in NEC Labs has focused on developing distributed fiber optic sensing systems for real-world applications. These systems include distributed acoustic sensors (DAS), distributed vibration sensors (DVS), distributed temperature sensors (DTS), Brillouin Optical Time Domain Reflectometry (BOTDR) based sensors, and other Rayleigh scattering-based distributed strain and temperature sensors. His current research also involves developing fiber optic microphones and hydrophone sensors for outdoor and underwater applications.
We demonstrate passive borehole velocity profiling using coherent DAS on a 3 km geothermal well. Deconvolution interferometry with phase-weighted stacking recovers interval velocities from ambient and ground micro-vibrations without active sources, achieving <80 m/s mean absolute error at 0.5–1 km depth against a calibration shot ground truth.
We demonstrate underwater acoustic transmission of image-derived semantic messages using an over-80-km distributed fiber-optic acoustic sensing link with chirp-coded pulse interrogation,Raman amplification, and fiber-optic hydrophones. At 50 bps with Raman amplification, 100 scene-graph messages (21,193 bits) are delivered without observed errors (BER upper bound < 4.72 × 10?5).
A fiber optic hydrophone array comprising four passive fiber mandrels, interrogated by a distributed acoustic sensing system was deployed in a lake for field testing. Several stationary and moving underwater acoustic sources were localized, continuously tracked, and their acoustic signals were enhanced by beamforming techniques.
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 field trial on urban monitoring over telecom networks using Rayleigh-based distributed temperature/ strain sensor (DTSS), showing the capability of detecting subtle infrastructure signatures including manhole locations, road traffic, sprinklers and underground leakage events.
We demonstrate a low-cost wavelength selectable distributed vibration sensor to monitor the whole routes in a 1×32 PON architecture with a unified sensitivity. The results obtained from the lab and a field testbed are presented.
NEC Laboratories America is proud to participate in OECC 2026, the 31st Opto-Electronics and Communications Conference, taking place in Busan, South Korea. We look forward to connecting with the international photonics and communications community and sharing the work we’re doing to shape the next generation of optical networks.
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 ambient environmental signals, such as vibrational patterns from traffic and day-night temperature fluctuations, and machine learning techniques for automated detection. By combining DAS waterfall traces with temperature measurements from DTS, we achieve improved classification accuracy. Experimental results from three real-world testbeds in Texas and New Jersey show a significant improvement in classification accuracyfrom 78.9% and 89.5% using DAS and DTS alone, respectively, to 94.7% via cross-referenced analysis. We propose a structured prediction formulation for manhole localization based on a U-Net architecture with a gated attention mechanism, where the label of each fiber location in the waterfall image is predicted using both its neighboring context and within-patch discriminative features. The method also supports cross-route generalization for manhole localization and enables condition diagnostics, identifying issues such as cable exposure and water ingress. These results highlight the potential for scalable deployment of fiber sensing solutions for real-time, continuous monitoring of telecom infrastructure.
We report a record long 200.6 km distributed acoustic sensing (DAS) link without inline ampli-fication, 28.6% improvement of sensing range has been achieved by using three segments of enhanced-scattering fibre (ESF) with progressively higher scattering enhancements.
While distributed acoustic sensing based on Rayleigh backscattering has been widely studied and deployed across various use cases, its adaptation in existing telecom infrastructure is fundamentally limited by inline optical amplifiers which block the backscattering light. Recently, vibration sensing based on forward transmission has emerged as a promising alternative, offering wider frequency response, higher signal-to-noise ratio, longer sensing distance, and compatibility with inline amplifiers. However, forward-transmission approach faces the challenge in signal identification and reconstruction when multiple vibration events and interference occur along the fiber simultaneously. To address this issue, we propose an equivalent dual-sensor phased array model to the bidirectional forward-transmission fiber sensing, which reformulates the problems in fiber sensing into the tasks in array processing. We introduce two beamforming techniques, including generalized sidelobe canceller (GSC) and null steering beamformer (NSBF) to reconstruct the signal of interest while suppressing noise and interference. We demonstrate the effectiveness of dual-sensor beamforming over 100km fiber experiment without amplifiers, achieving over 20 dB suppression of stationary interference using GSC and 18 dB jamming rejection using NSBF. To our knowledge, this is the first demonstration of high-fidelity signal recovery in forward-transmission sensing under strong noise and interference. This work bridges the gap between fiber sensing and adaptive array processing, opening new directions for blind source separation and integrated communication-sensing systems.