Distributed Acoustic Sensing (DAS) is a technology used to monitor and analyze acoustic signals along a fiber-optic cable. It has a wide range of applications in various industries, including oil and gas, infrastructure monitoring, security, and environmental monitoring. DAS works by using the fiber-optic cable itself as a sensor to detect acoustic disturbances and vibrations.

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NEC Provides AI-Based Traffic Monitoring System with Fiber-Optic Sensing Technology for NEXCO CENTRAL

NEC Corporation has deployed an AI-based traffic monitoring system to Central Nippon Expressway Company Limited (NEXCO CENTRAL). The system uses fiber-optic sensing and AI technologies to visualize traffic conditions, such as the location, speed, and direction of travel, from vibrations produced by vehicle movement.

Distributed Acoustic Sensing for Datacenter Optical Interconnects using Self-Homodyne Coherent Detection

We demonstrate distributed acoustic sensing (DAS) over a bidirectional datacenter link which uses self-homodyne coherent detection for the data signal. Frequency multiplexing allows sharing the optoelectronic hardware, and enables DAS as an auxiliary function.

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.