Jian Fang NEC Labs AmericaJian Fang is a Researcher in the Optical Networking and Sensing Department at NEC Laboratories America. He received his B.S. in Electrical, Electronics, and Communications Engineering and his M.S. in Information and Communication Engineering from Shanghai Jiao Tong University, and his Ph.D. in Electrical and Electronic Engineering from the University of Melbourne. His research interests include distributed optical fiber sensing, specialty fibers and hybrid sensing systems, optical signal processing, and photonic sensing for industrial and environmental applications. At NEC, he develops advanced sensing systems that leverage coherent backscatter to detect vibration and intrusion across long-distance fiber links. His work underpins NEC’s innovations in passive infrastructure monitoring for security, seismic awareness, and smart mobility. Dr. Fang’s integration of novel signal processing techniques with real-world constraints has made him a key contributor to NEC’s next-generation sensing portfolio.

Posts

Leveraging Deployed Telecom Cables for Distributed Fiber Sensing Topologies and Applications

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Distributed fiber optic sensing (DFOS) has emerged as a promising technology for wide-area monitoring by utilizing existing telecom cables as large-scale sensing media. This paper explores three sensing modalities, backscattering-based sensing, forward-transmission-based sensing, and hybrid sensing, and discusses their respective benefits, challenges, and application domains. Backscattering sensing demonstrates strong potential for applications such as road traffic monitoring, pavement condition assessment, intrusion detection, and cabledamage prevention but is constrained in amplified dense wavelength division multiplexing (DWDM) networks. Forward-transmission sensing enables sensing over operational telecom links with in-line amplification, extending sensing reach, although it involves trade-offs in spatial resolution and localization accuracy. To address these challenges, a hybrid sensing architecture that integrates backscattering and forward-transmission techniques is introduced, achieving enhanced sensing distance while maintaining high sensitivity and localization performance.In addition, this work incorporates artificial intelligence (AI) through a locally adaptive anomaly detection (LAAD) framework based on self-supervised representation learning. By leveraging location-based pretext tasks and unlabeled data, the proposed AI approach enables efficient adaptation across heterogeneous fiber routes and operational environments, significantly reducing reliance on labeled data while improving cross-domain generalization. Field trials over deployed telecom networks validate the feasibility and effectiveness of the proposedsensing and AI framework, demonstrating scalable, telecom-compatible DFOS for real-world infrastructure monitoring and intelligent network operations.

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Field study on phase and polarization dynamics of deployed anti-resonant hollow core fiber cable for vibration sensing

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We report the first field study of the phase and polarization dynamics of deployed antiresonant hollow core fiber cable in a data center interconnect for real-world vibration sensing,revealing enhanced phase sensitivity and significantly faster polarization angular rate compared with standard single mode fibers.

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NEC Labs America Attending OFC 2026 Los Angeles, March 15-19

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NEC Laboratories America’s Optical Networking & Sensing team will participate in OFC 2026 in Los Angeles, March 15–19, contributing to panels, workshops, and courses focused on optical sensing, multicore fibers, and next-generation high-capacity optical communication systems.

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Field Trial of High-Sensitivity Forward-Transmission Sensing for Real-World Event Detection Over Live Urban Fiber Networks

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Vibration sensing based on forward transmission is an emerging topic for network protection and environmental monitoring, especially in long-haul submarine cables and urban fiber networks. However, previous field trials of this approach have mainly focused on localizing strong events under controlled or relatively quiet conditions. In this work, we investigate the capability of forward-transmission vibration sensing to detect weak signals in noisy environments. We demonstrate a high-sensitivity vibration sensing system operating over an 80-km deployed live urban fiber loop without optical amplifiers. The system is enhanced by adaptive time-frequency masking and in-band laser phase noise suppression techniques to improve sensitivity and noise robustness. It has successfully identified and localized weak real-world vibration events with peak-to-peak amplitude lower than 20 rad, such as construction activity near a manhole and even footsteps on handhole lids. Field trial results confirm its robust performance under dynamic environments, including road traffic-induced ground vibrations and aerial cable disturbances. To the best of our knowledge, this is the first demonstration of weak vibration event detection using forward transmission in urban fiber networks. It remarks a significant step towards practical distributed vibration sensing in smart city applications.

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Advances in Fiber Sensing

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In this talk, we will present recent technological advances in fiber sensing applications with long monitoring distances orextending multiple fiber spans. In forward-transmission-based sensing, adaptive beamforming techniques weredemonstrated to achieve multi-event vibration sensing in environments with interference and jamming with significantimprovements in signal reconstruction, noise reduction, and interference rejection from other locations. For sensing oversubmarine cables with many fiber spans with repeaters, it is shown that distributed reflection from Rayleigh scattering canbe detected with sufficient SNR for fiber sensing using HLLB paths. In particular, longitudinal averaging of receivedRayleigh scattered signals can facilitate state-of-polarization-based, multi-span sensing using eigenvalue method.

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Optical Flow Processing for Chirp-Pulse Coherent OTDR

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We propose a novel optical flow processing technique for distributed temperature and strain sensing with the chirped-pulse coherent OTDR. Unlike conventional 1-dimensional cross-correlation methods, the technique treats the 2-dimensional waterfall data as sequential video frames, estimating local shifts through optical flow. The weighted least square approach with adaptive window size enables pixel-level optical flow calculation, providing accurate local shifts via accumulative tracks with enhanced spatial resolution. Preliminary experimental results over 20km fiber demonstrate its effectiveness for dynamic temperature and strain sensing, addressing limitations of traditional methods and improving sensing capabilities.

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Multiple Sensor-head Phase-sensitive Optical Time-domain Laser Vibrometer

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We propose a hybrid remote and distributed vibration sensing system based on phase-sensitive optical time-domain reflectometry with collimator-based sensor heads. We demonstrate dual-laser vibrometers that detects nm-scale displacements of remote targets.

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Text-guided Device-realistic Sound Generation for Fiber-based Sound Event Classification

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Recent advancements in unique acoustic sensing devices and large-scale audio recognition models have unlocked new possibilities for environmental sound monitoring and detection. However, applying pretrained models to non-conventional acoustic sensors results in performance degradation due to domain shifts, caused by differences in frequency response and noise characteristics from the original training data. In this study, we introduce a text-guided framework for generating new datasets to retrain models specifically for these non-conventional sensors efficiently. Our approach integrates text-conditional audio generative models with two additional steps: (1) selecting audio samples based on text input to match the desired sounds, and (2) applying domain transfer techniques using recorded impulse responses and background noise to simulate the characteristics of the sensors. We demonstrate this process by generating emulated signals for fiber-optic Distributed Acoustic Sensors (DAS), creating datasets similar to the recorded ESC-50 dataset. The generated signals are then used to train a classifier, which outperforms few-shot learning approaches in environmental sound classification.

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Underwater Acoustic OFDM Transmission over Optical Fiber with Distributed Acoustic Sensing

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We demonstrate fiber-optic acoustic data transmission using distributed acoustic sensing technology in an underwater environment. An acoustic orthogonal frequencydivisionmultiplexing (OFDM) signal transmitted through a fiber-optic cable deployed in a standard 40-meter-scale underwater testbed.

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Multi-Event Distributed Forwarding Sensing with Dual-Sensor Adaptive Beamforming

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We present adaptive beamforming techniques to forward-transmission multi-event vibration sensing in environments with interference and jamming. Experimental validation over 100km fiber demonstrates significant improvements on signal reconstruction, noise reduction, and interference rejection from other locations.

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