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

Using Global Fiber Networks for Environmental Sensing

We review recent advances in distributed fiber optic sensing (DFOS) and their applications. The scattering mechanisms in glass, which are exploited for reflectometry-based DFOS, are Rayleigh, Brillouin, and Raman scatterings. These are sensitive to either strain and/or temperature, allowing optical fiber cables to monitor their ambient environment in addition to their conventional role as a medium for telecommunications. Recently, DFOS leveraged technologies developed for telecommunications, such as coherent detection, digital signal processing, coding, and spatial/frequency diversity, to achieve improved performance in terms of measurand resolution, reach, spatial resolution, and bandwidth. We review the theory and architecture of commonly used DFOS methods. We provide recent experimental and field trial results where DFOS was used in wide-ranging applications, such as geohazard monitoring, seismic monitoring, traffic monitoring, and infrastructure health monitoring. Events of interest often have unique signatures either in the spatial, temporal, frequency, or wavenumber domains. Based on the temperature and strain raw data obtained from DFOS, downstream postprocessing allows the detection, classification, and localization of events. Combining DFOS with machine learning methods, it is possible to realize complete sensor systems that are compact, low cost, and can operate in harsh environments and difficult-to-access locations, facilitating increased public safety and smarter cities.

Distributed Optical Fiber Sensing Using Specialty Optical Fibers

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 discussed.

Field Trials of Vibration Detection, Localization and Classification over Deployed Telecom Fiber Cables

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.

Template Matching Method with Distributed Acoustic Sensing Data and Simulation Data

We propose a new method to detect acoustic signals by matching distributed acoustic sensing data with simulation. In the simulation of the dynamic strain on an optical fiber, the optical fiber layouts and the gauge length are properly incorporated. We apply the proposed method to the acoustic-source localization and demonstrate the method localizes the source accurately even under the layouts which include the straight optical fiber for the sensing points with the large gauge-length settings.

Field Tests of Impulsive Acoustic Event Detection, Localization, and Classification Over Telecom Fiber Networks

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.

Evolution of Fiber Infrastructure – From Data Transmission to Network Sensing

We review multiple use cases over deployed networks including co-existing sensing/data transmission, cable cut prevention and perimeter intrusion detection to realize telecom infrastructure can be sensing backbones instead of the sole function of data transmission.

Remote Drone Detection and Localization with Optical Fiber Microphones and Distributed Acoustic Sensing

We demonstrate the first fiber-optic drone detection method with ultra-highly sensitive optical microphones and distributed acoustic sensor. Accurate drone localization has been achieved through acoustic field mapping and data fusion.

Perimeter Intrusion Detection with Rayleigh Enhanced Fiber Using Telecom Cables as Sensing Backhaul

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.

Distributed Fiber Sensor Network Using Telecom Cables as Sensing Media: Technology Advancements and Applications

Distributed fiber optic sensing (DFOS) is a rapidly evolving field that allows the existing optical fiber infrastructure for telecommunications to be reused for wide-area sensing. Using the backscattering mechanisms of glass—which includes Rayleigh, Brillouin, and Raman backscatter—it is possible to realize distributed vibration and temperature sensors with good sensitivity at every fiber position, and spatial resolution is determined by the bandwidth of the interrogation signal. In this paper, we will review the main technologies in currently deployed DFOS. We review the digital signal processing operations that are performed to extract the sensing parameters of interest. We report recent distributed vibration sensing, distributed acoustic sensing, and distributed temperature sensing field trial results over an existing network with reconfigurable add/drop multiplexers carrying live telecom traffic, showing that the network is capable of simultaneous traffic and temperature monitoring. We report Brillouin optical time-domain reflectometry experimental results for monitoring static strain on aerial fiber cables suspended on utility poles. Finally, we demonstrate an example of network modification to make passive optical networks compatible with DFOS by adding reflective semiconductor optical amplifiers at optical network units.

Bipolar Cyclic Linear Coding for Brillouin Optical Time Domain Analysis

We demonstrate, for the first time, that cyclic linear pulse coding can be bipolar for BOTDA sensors, breaking the unipolar limitation of linear coding techniques and elevating the coding gain for a given code length.