OPTICAL NETWORKING & SENSING
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Optical Networking & Sensing
Yue Tian is a Senior Researcher in the Optical Networking and Sensing Department at NEC Laboratories America in Princeton, NJ. He received his Ph.D. in Electrical Engineering from Princeton University, and his M.S. and B.S. in Electrical Engineering from Shanghai Jiao Tong University.
At NEC, Dr. Tian’s research spans distributed fiber-optic sensing, optical gas sensing, fiber communications, and photonic neural networks. His work focuses on optical signal and data processing for applications including ultra-long-haul transmission, intelligent sensing for smart cities, ICT and power infrastructure monitoring, and undersea sensing. He has contributed to the development of next-generation multimodal sensing networks that integrate large-scale infrastructure monitoring with advanced data analytics. By combining physical-layer innovations with intelligent data interpretation techniques—such as machine learning and large language models—he advances NEC’s leadership in intelligent sensing technologies that enhance reliability, safety, and situational awareness across critical infrastructures and complex environments.
Dr. Tian’s research has resulted in more than 50 publications and over 30 issued or pending patents, and has garnered support from the U.S. Department of Energy (DOE) and the National Institutes of Health (NIH).
The 2021 emergence of Brood X cicadas was monitored in situ in our testbed using a DAS system connected to an outdoor telecom fiber over a 16-day period. The spectral and energy characteristics of the cicada calling signal has been measured and analyzed.
For the first time, we present an end-to-end AI framework for data analysis in distributed fiber optic sensing. The proposed model eliminates the need for optical phase computation and outperforms traditional data processing pipelines, achieving over 96% recognition accuracy on a diverse acoustic dataset.
NEC Laboratories America (NECLA) was proud to join the European Conference on Optical Communication (ECOC 2025) in Copenhagen, Denmark, from September 28 to October 2. Our researchers presented cutting-edge work in distributed acoustic sensing, AI-driven fiber optics, and optical networking. From generative
Subsea cables are critical components of offshore wind turbines and are subjected to scour. Monitoring the scour conditions of subsea cables plays significant roles in improving safety and operation efficiency and reducing the levelized cost of electricity. This paper presents a feasibility study on
Fault localization in power distribution networks is essential for rapid recovery and enhancing system resilience. While Phasor Measurement Units (PMUs or ?PMUs) providehigh-resolution measurements for precise fault localization, their widespread deployment is cost-prohibitive. Distributed Fiber Optic
Distributed fiber optics sensing is applied for traffic management in the intersection. The high-definition fiber sensing data streaming is applied as source and YOLO computer vision model isemployed for event detection classification and localization.
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.
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.
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
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.
The NEC Labs America team Yaowen Li, Andrea D’Amico, Yue-Kai Huang, Philip Ji, Giacomo Borraccini, Ming-Fang Huang, Ezra Ip, Ting Wang & Yue Tian (Not pictured: Fatih Yaman) has arrived in San Diego, CA for OFC24! Our team will be speaking and presenting throughout the event. Read more for an overview
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
Our Sarper Ozharar, Yue Tian and Yangmin Ding and Jessica L. Ware from the American Museum of Natural History have discovered that fiber optic cables equipped with distributed acoustic sensing (DAS) can pick up the sounds of Brood X cicadas. DAS technology, typically used to monitor seismic activity,
Brood X is the largest of the 15 broods of periodical cicadas, and individuals from this brood emerged across the Eastern United States in spring 2021. Using distributed acoustic sensing (DAS) technology, the activity of Brood X cicadas was monitored in their natural environment in Princeton, NJ. Critical
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
Distributed Fiber Optic Sensing (DFOS) systems rely on measuring and analyzing different properties of the backscattered light of an optical pulse propagating along a fiber cable. DFOS systems can measure temperature, strain, vibrations, or acoustic excitations on the fiber cable and to their unique
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.
For the first time to our knowledge, a stationary weight hanging on an operational aerial telecommunication field fiber was detected and localized using only ambient data collected by a φ-DAS system. Although stationary weights do not create temporally varying signals, and hence cannot be observed directly
In optical communication systems, fibre nonlinearity is the major obstacle in increasing the transmission capacity. Typically, digital signal processing techniques and hardware are used to deal with optical communication signals, but increasing speed and computational complexity create challenges for
We demonstrated for the first time to our knowledge, the detection and localization of a static weight on an aerial cable by using frequency domain decomposition analysis of ambient vibrations detected by a φ-DAS system.
In distributed acoustic sensing (DAS) on aerial fiber-optic cables, utility pole localization is a prerequisite for any subsequent event detection. Currently, localizing the utility poles on DAS traces relies on human experts who manually label the poles’ locations by examining DAS signal patterns
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
We demonstrate an anti-spoofing face recognition system that is able to differentiate real human face with 3D printed materials. Face images captured in infrared structure light are analyzed for surface materials and spatial structure.
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
We demonstrate the experimental implementation of photonic neural network for fiber nonlinearity compensation over a 10,080 km trans-pacific transmission link. Q-factor improvement of 0.51 dB is achieved with only 0.06 dB lower than numerical simulations.
We proposed and demonstrated a new machine learning algorithm for wavelength modulation spectroscopy to enhance the accuracy of fire detection. The result shows more than 8% of accuracy improvement by analyzing CO/CO 2 2f signals.
