Optical Networking and Sensing

Our Optical Networking and Sensing department is leading world-class research into the next generation of optical networks and sensing systems that will power ICT-based social solutions for years. From forward-looking theoretical studies to cutting-edge experiments to world- and industry-first technology field trials, we deliver globally recognized innovation that looks into the future and translates it into present reality. Read our optical networking and sensing news and publications from our team of researchers.

Posts

Ambient Noise based Weakly Supervised Manhole Localization Methods over Deployed Fiber Networks

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We present a manhole localization method based on distributed fiber optic sensing and weakly supervised machine learning techniques. For the first time to our knowledge, ambient environment data is used for underground cable mapping with the promise of enhancing operational efficiency and reducing field work. To effectively accommodate the weak informativeness of ambient data, a selective data sampling scheme and an attention-based deep multiple instance classification model are adopted, which only requires weakly annotated data. The proposed approach is validated on field data collected by a fiber sensing system over multiple existing fiber networks.

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Drone Detection and Localization using Enhanced Fiber-Optic Acoustic Sensor and Distributed Acoustic Sensing Technology

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In recent years, the widespread use of drones has led to serious concerns about safety and privacy. Drone detection using microphone arrays has proven to be a promising method. However, it is challenging for microphones to serve large-scale applications due to the issues of synchronization, complexity, and data management. Moreover, distributed acoustic sensing (DAS) using optical fibers has demonstrated its advantages in monitoring vibrations over long distances but does not have the necessary sensitivity for weak airborne acoustics. In this work, we present, to the best of our knowledge, the first fiber-optic quasi-distributed acoustic sensing demonstration for drone surveillance. We develop enhanced fiber-optic acoustic sensors (FOASs) for DAS to detect drone sound. The FOAS shows an ultra-high measured sensitivity of −101.21 re. 1rad/µPa, as well as the capability for high-fidelity speech recovery. A single DAS can interrogate a series of FOASs over a long distance via optical fiber, enabling intrinsic synchronization and centralized signal processing.We demonstrate the field test of drone detection and localization by concatenating four FOASs as DAS. Both the waveforms and spectral features of the drone sound are recognized. With acoustic field mapping and data fusion, accurate drone localization is achieved with a root-mean-square error (RMSE) of 1.47 degrees. This approach holds great potential in large-scale sound detection applications, such as drone detection or city event monitoring.

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Distributed fiber optic sensing over readily available telecom fiber networks

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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 specifications, they have many applications and advantages over competing technologies. In this talk we will focus on the challenges and applications of DFOS systems using outdoor grade telecom fiber networks instead of standard indoor or some specialty fiber cables.

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Using Global Fiber Networks for Environmental Sensing

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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.

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Next-Generation Computing Finally Sees Light

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Moore’s law is dead, as we have squeezed all the innovation out of silicon. Fiber optics is the solution to meet the computing needs of tomorrow. Today, we can already use the light traveling inside fiber optic cables as sensors that measure vibrations, sound, temperature, light, and pressure changes. We’re now developing the means to take this to the next level with photonic computing at the speed of light to provide faster reaction time, reduce energy consumption and improve battery range

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Availability Analysis for Reliable Distributed Fiber Optic Sensors Placement

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We perform the availability analysis for various reliable distributed fiber optic sensor placement schemes in the circumstances of multiple failures. The study can help the network carriers to select the optimal protection scheme for their network sensing services, considering both service availability and hardware cost.

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Distributed Optical Fiber Sensing Using Specialty Optical Fibers

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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.

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A Multi-sensor Feature Fusion Network Model for Bearings Grease Life Assessment in Accelerated Experiments

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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 and bearings health monitoring. The single MCFF network is designed for low-level feature extraction and fusion of either vibration or acoustic emission signals at multi-scales. The concatenation of MCFF networks serves as a cross-sensor feature fusion layer to combine extracted features from both vibration and acoustic emission sources. A ResNet is developed for high-level feature extraction from the fused feature maps and prediction. Besides, to handle the large volume of collected data, original time-series data are transformed to the frequency domain with different sampling intervals and targeted ranges. The proposed MSFF network outperforms other models based on different fusion methods, fully connected network predictors and/or a single sensor source.

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Semi-supervised Identification and Mapping of Water Accumulation Extent using Street-level Monitoring Videos

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Urban flooding is becoming a common and devastating hazard, which causes life loss and economic damage. Monitoring and understanding urban flooding in a highly localized scale is a challenging task due to the complicated urban landscape, intricate hydraulic process, and the lack of high-quality and resolution data. The emerging smart city technology such as monitoring cameras provides an unprecedented opportunity to address the data issue. However, estimating water ponding extents on land surfaces based on monitoring footage is unreliable using the traditional segmentation technique because the boundary of the water ponding, under the influence of varying weather, background, and illumination, is usually too fuzzy to identify, and the oblique angle and image distortion in the video monitoring data prevents georeferencing and object-based measurements. This paper presents a novel semi-supervised segmentation scheme for surface water extent recognition from the footage of an oblique monitoring camera. The semi-supervised segmentation algorithm was found suitable to determine the water boundary and the monoplotting method was successfully applied to georeference the pixels of the monitoring video for the virtual quantification of the local drainage process. The correlation and mechanism-based analysis demonstrate the value of the proposed method in advancing the understanding of local drainage hydraulics. The workflow and created methods in this study have a great potential to study other street level and earth surface processes.

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

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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.

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