Distributed Fiber Optic Sensing (DFOS) is a technology that allows the monitoring of physical parameters along the length of an optical fiber in real-time. It transforms standard optical fibers into long and continuous sensors, enabling the measurement of various environmental and structural conditions. DFOS systems are widely used in industries such as oil and gas, civil engineering, transportation, and environmental monitoring.
We propose a method to compensate the phase offset between samples from different tributaries in time-interleaved phase OTDR using nested phase reference channels. We demonstrate our method for a four-span bidirectional link with high-loss loopback.
As AI data centers grow, the fiber-optic networks that connect massive computing clusters become critical infrastructure. This talk explores how Distributed Fiber Optic Sensing (DFOS) can turn communication cables into real-time sensors that detect physical threats and improve cyber-physical resilience.
NEC Laboratories America celebrates Women’s History Month and International Women’s Day by recognizing the women researchers, engineers, and interns whose work in artificial intelligence, optical networking, cybersecurity, and data science is helping shape the future of technology.
Distributed Fiber-Optic Sensing (DFOS) is a promising technique for large-scale acoustic monitoring. However, its wide variation in installation environments and sensor characteristics causes spatial heterogeneity. This heterogeneity makes it difficult to collect representative training data. It also degrades the generalization ability of learning-based models, such as fine-tuning methods, under a limited amount of training data. To address this, we formulate Sound Event Classification (SEC) as data assimilation in an embedding space. Instead of training models, we infer sound event classes by combining pretrained audio embeddings with simulated DFOS signals. Simulated DFOS signals are generated by applying various frequency responses and noise patterns to microphone data, which allows for diverse prior modeling of DFOS conditions. Our method achieves out-of-domain (OOD) robust classification without requiring model training. The proposed method achieved accuracy improvements of 6.42, 14.11, and 3.47 percentage points compared with conventional zero-shot and two types of fine-tune methods, respectively. By employing the simulator in the framework of data assimilation, the proposed method also enables precise estimation of physical parameters from observed DFOS signals.
Fiber sensing function was introduced in 2020 as one of the key technology features for the OpenAPN (all photonics network) developed by IOWN GF (Innovative Optical and Wireless NetworkGlobal Forum) in 2020.To our best knowledge, IOWN GF is the first global standard developmentorganization or technology forum that studied fiber sensing technology for telecommunication anddata communication networks, because it brings new feature and benefits to the networkoperators (such as making network operation more efficient, and bringing new values to theexisting network infrastructure), as shown in the examples above.
Passive-Optical-Networks (PON) have emerged as a pivotal technology for broadband access network and are now expanding to wireless communication, supporting 5G and development of future 6G frameworks. PON systems are expected to find many new applications, including in electrical power grids, modern industrial factories, and smart city infrastructure. With the growing capabilities and increasing complexity and extent of the optical distribution network, effective surveillance of fiber infrastructure has become increasingly important to ensure long-term viability and dependability. Simultaneously, there is increasing demand for effective distributed monitoring systems for the power-grid elements and machinery in automated factories operating within PON environments. This paper discusses the challenges and potential solutions for implementing distributed acoustic sensing (DAS) within PON architecture. We will present design and experimental demonstrations of a co-existing DAS and 10G PON (XGS-PON) system with a 23.5 km feeder fiber (FF) and a 1 × 16 splitter. A unique signature from each distributed fiber (DF) and optical network units (ONU) is detected by utilizing a coded Enhanced Scatter Fiber (ESF). Vibration events originating from up to three DF/ONUs are identified using a novel scheme using the coded ESFs in conjunction with fiber delay lines. We further investigated the sensing performance and potential crosstalk between XGS-PON and DAS signals within this co-existing DAS and XGS-PON system.
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.
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 Integer Linear Programming (ILP) to optimize DFOS placement while addressing power supply constraints. The strategy ensures resilient monitoring across diverse grid scenarios by prioritizing observability during outages and leveraging advancements in fiber infrastructure deployment. Case studies demonstrate the effectiveness of our methodology in maintaining power grid resilience while minimizing deployment costs.
We proposed the use of BOTDA as a monitoring tool to identify fiber types present in deployed hybrid-span fiber cables, to assist in network planning, setting optimal launch powers, and selecting correct modulation formats.
We develop a heuristic solution to effectively optimize the placement of multi-channel distributed fiber optic sensors in mesh optical fiber cable networks. The solution has beenimplemented in a field network to provide continuous monitoring.
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