Utility pole localization by learning from ambient traces on distributed acoustic sensing 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 for data collection such as knocking every pole and manually labeling the poles’ locations, making this labor-intensive solution expensive, inefficient, and highly error prone. In this paper, we propose a pole localization solution by learning the ambient data collected from a DAS system, which are vibration patterns excited by random ambient events, such as wind and nearby traffic. In detail, we investigate a universal framework for learning representations of ambient data in the frequency domain by contrastive learning of the similarity of low and high-frequency series. A Gaussian-based data reweighting kernel is employed for eliminating the effect of the label noise. Experimental results demonstrate the proposed methods outperform the existing contrastive learning methods on the real-world DAS ambient dataset.
Optical Networking & Sensing
Distributed fiber optic sensing over readily available telecom fiber networks 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.
Rain Intensity Detection and Classification with Pre-existing Telecom Fiber Cables 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.
Detection and Localization of Stationary Weights Hanging on Aerial Telecommunication Fibers using Distributed Acoustic Sensing 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 from the DAS traces, the existence and the location of the additional weights were revealed by the operational modal analysis of the aerial fiber structure.
A Silicon Photonic-Electronic Neural Network for Fiber Nonlinearity Compensation 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 such approaches. Highly parallel, ultrafast neural networks using photonic devices have the potential to ease the requirements placed on digital signal processing circuits by processing the optical signals in the analogue domain. Here we report a silicon photonic–electronic neural network for solving fibre nonlinearity compensation in submarine optical-fibre transmission systems. Our approach uses a photonic neural network based on wavelength-division multiplexing built on a silicon photonic platform compatible with complementary metal–oxide–semiconductor technology. We show that the platform can be used to compensate for optical fibre nonlinearities and improve the quality factor of the signal in a 10,080 km submarine fibre communication system. The Q-factor improvement is comparable to that of a software-based neural network implemented on a workstation assisted with a 32-bit graphic processing unit.
Static Weight Detection and Localization on Aerial Fiber Cables using Distributed Acoustic Sensing 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.
Automatic Fine-Grained Localization of Utility Pole Landmarks on Distributed Acoustic Sensing Traces Based on Bilinear Resnets 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 generated in response to hammer knocks on the poles. This process is inefficient, error-prone and expensive, thus impractical and non-scalable for industrial applications. In this paper, we propose two machine learning approaches to automate this procedure for large-scale implementation. In particular, we investigate both unsupervised and supervised methods for fine-grained pole localization. Our methods are tested on two real-world datasets from field trials, and demonstrate successful estimation of pole locations at the same level of accuracy as human experts and strong robustness to label noises.
Field Trial of Distributed Fiber Sensor Network Using Operational Telecom Fiber Cables as Sensing Media 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 one system.
Anti-spoofing Face Recognition Using Infrared Structure Light 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.
Chemical profiling of red wines using surface-1 enhanced Raman spectroscopy (SERS) 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 a reproducible SERS substrate, the AgNP mirror, previously developed by our group. However, as previously reported for red wine analysis, the signals obtained through these approaches were either due to interference of the fluorescence exhibited by pigments or mainly attributed to a DNA fraction, adenine. Therefore, an innovative approach was developed using solvent extraction to provide more characteristic information that is beneficial for wine chemical profiling and discrimination. Signature peaks in the wine extract spectra were found to match those of condensed tannins, resveratrol, anthocyanins, gallic acid, and catechin, which indicated that SERS combined with extraction is an innovative method for profiling wine chemicals and overcoming well-known challenges in red wine analysis. Based on this approach, we have successfully differentiated three red wines and demonstrated the possible relation between the overall intensity of wine spectra and the ratings. Since the wine chemical profile is closely related to the grape species, wine quality, and wine authentication, the SERS approach to obtain rich spectral information from red wine could advance wine chemical analysis.
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