OPTICAL NETWORKING & SENSING
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Optical Networking & Sensing
Zhuocheng Jiang is a Research Assistant in the Optical Networking and Sensing Department at NEC Laboratories America. He received his PhD in Deep Learning and Data Compression from The University of Alabama in Huntsville, his MS in Electrical Engineering Technologies from Shenzhen University, and his BS in Electrical Engineering Technologies from Qinghai University for Nationalities.
Dr. Jiang’s research explores the intersection of artificial intelligence, LLM and fiber-optic sensing. He specializes in machine learning-based interpretation of large-scale sensing data, optical signal analysis and visualization. His work integrates LLM, machine learning, and advanced computational models to improve the accuracy and scalability of distributed acoustic sensing (DAS) platforms. By leveraging AI techniques, he enhances the ability of optical fibers to serve not only as communication channels but also as pervasive sensing tools.
At NEC, Dr. Jiang contributes to next-generation monitoring solutions designed for infrastructure, energy, and industrial systems. His recent efforts have focused on high-resolution DAS technologies that enable real-time detection of vibrations, anomalies, and environmental changes across vast networks. These innovations transform complex optical data streams into actionable engineering insights, supporting NEC’s mission to deliver intelligent sensing systems that improve safety, reliability, and operational efficiency worldwide.
A 100-meter-long fiber optic cable was installed at the bottom of a water tank at the Davidson Laboratory, together with a hydrophone for reference. The water tank is approximately 2.5 meters deep and 95 meters long; the tank also employs a 6-paddle wavemaker which can generate programmable surface waves.
We propose a novel Distributed Fiber Optic Sensing (DFOS) placement strategy tailored to the evolving needs of modern power grids, where fiber cables serve dual purposes: communication and real-time sensing. Our approach integrates a heuristic algorithm, PURE (Power Source-aware Route Exploration), with
We present a generative AI framework based on a conditional diffusion model for distributed acoustic sensing (DAS) data imputation. The proposed DiffOptics model generates high-quality DAS data of various acoustic events using telecom fiber cables.
The NEC Labs America Optical Networking and Sensing team is attending the 2025 Optical Fiber Communications Conference and Exhibition (OFC), the premier global event for optical networking and communications. Bringing together over 13,500 attendees from 83+ countries, more than 670 exhibitors, and hundreds
For the first time, we demonstrate remote sensing of pole-mounted fuse-cutout blowing in a power grid setup using telecom fiber cable. The proposed frequency-based AI model achieves over 98% detection accuracy using distributed fiber sensing data.
Our optical networking & sending team has arrived in Frankfurt for the 2024 European Conference on Optical Communication (ECOC) and is excited to present many papers and tutorials this week. Please follow this page and on our social media channels for updates.
The various sensing technologies such as cameras LiDAR radar and satellites with advanced machine learning models offers a comprehensive approach to environmental perception and understanding. This paper introduces an innovative Distributed Fiber Optic Sensing (DFOS) technology utilizing the existing
We introduce two pioneering applications leveraging Distributed Fiber Optic Sensing (DFOS) and Machine Learning (ML) technologies. These innovations offer substantial benefits forfortifying telecom infrastructures and public safety. By harnessing existing telecom cables, our solutions excel in perimeter
Our team will be attending CVPR 2024 (The IEEE /CVF Conference on Computer Vision & Pattern Recognition) from June 17-21! See you there at the NEC Labs America Booth 1716! Stay tuned for more information about our participation.
Falling trees or their limbs can cause power lines to break or sag, sometimes resulting in devastating wildfires. Conventional protections such as circuit breakers, overcurrent relays and automatic circuit reclosers may clear short circuits caused by tree contact, but they may not detect cases where
We present the field trial of an innovative neural network and DAS-based technique, employing a pre-trained CNN fine-tuning strategy for effective rain detection and classification within two practical scenarios.
Utility pole detection and localization is the most fundamental application in aerial-optic cables using distributed acoustic sensing (DAS). The existing pole localization method recognizes the hammer knock signal on DAS traces by learning from knocking vibration patterns. However, it requires many efforts
This paper presents a multi-sensor feature fusion (MSFF) neural network comprised of two inception layer-type multiple channel feature fusion (MCFF) networks for both inner-sensor and cross-sensor feature fusion in conjunction with a deep residual neural network (ResNet) for accurate grease life assessment
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.
This paper presents a deep learning-based framework to detect and localize the pedestrians’ anomaly behaviors in videos captured at the grade crossing. A skeleton detection and tracking algorithm are employed to capture the key point trajectories of body movements of the pedestrians. A deep recurrent
For the first time, we demonstrate detection and classification of rain intensity using Distributed Acoustic Sensing (DAS). An artificial neural network was applied for rain intensity classification and high precision of over 96% was achieved.
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
