Abstract: An AI-driven cable mapping system that employs distributed fiber optic sensing (DFOS) fiber sensing and machine learning that provides autonomous determination of fiber optic cable location and mapping of same. Designed Al algorithms operating within our inventive systems and methods provide an easy solution for cable mapping in a GIS system; automatically maps using landmarks and manhole locations; and employs a supervised learning algorithm. A vehicle-assist operation is employed wherein a vehicle carries a Global Positioning System (GPS) device and drives along a roadway thereby following the fiber optic cable route; data paring that provides further significant locational information wherein time synchronizes between the DFOS system and vehicle GPS device from which we automatically pair the data of fiber length from traffic trajectories and GPS coordinates by time series.

Abstract: A device may receive, from a fiber sensor device, sensing data associated with a fiber optic cable, the sensing data being produced by an activity that poses a threat of damage to the fiber optic cable, and the sensing data identifying: amplitudes of vibration signals, frequencies of the vibration signals, patterns of the vibration signals, times associated with the vibration signals, and locations along the fiber optic cable associated with the vibration signals. The device may process, with a machine learning model, the sensing data to determine a threat level of the activity to the fiber optic cable, the machine learning model having been trained based on historical information regarding detected vibrations, historical information regarding sources of the detected vibrations, and historical information regarding threat levels to the fiber optic cable. The device may perform one or more actions based on the threat level to the fiber optic cable.

Abstract: Systems and methods for manhole localization along deployed fiber optic cables that employs cross-correlation methodologies and ambient road traffic operating proximate to the manholes including fiber optic telecommunications cables to detect the manhole locations using distributed fiber optic sensing (DFOS). Advantageously the manhole locations are determined without employing labor intensive field surveys.

Abstract: Method for source localization for cable cut prevention using distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) is described that is robust/immune to underground propagation speed uncertainty. The method estimates the location of a vibration source while considering any uncertainty of vibration propagation speed and formulates the localization as an optimization problem, and both location of the sources and the propagation speed are treated as unknown. This advantageously enables our method to adapt to variances of the velocity and produce a better generalized performance with respect to environmental changes experienced in the field. Our method operates using a DFOS system and AI techniques as an integrated solution for vibration source localization along an entire optical sensor fiber cable route and process real-time DFOS data and extract features that are related to a location of a source of vibrations that may threaten optical fiber facilities.

Abstract: A DFOS system and machine learning method that automatically localizes manholes, which forms a key step in a fiber optic cable mapping process. Our system and method utilize weakly supervised learning techniques to predict manhole locations based on ambient data captured along the fiber optic cable route. To improve any non-informative ambient data, we employ data selection and label assignment strategies and verify their effectiveness extensively in a variety of settings, including data efficiency and generalizability to different fiber optic cable routes. We describe post-processing steps that bridge the gap between classification and localization and combining results from multiple predictions.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously are employed in smart stadium or other venue applications, such applications including: parking lot security and management; intrusion detection; social sensing; air quality monitoring and early fire detection—among others.

Abstract: A device may receive, from a fiber sensor device, sensing data associated with a fiber optic cable, the sensing data being produced by an activity that poses a threat of damage to the fiber optic cable, and the sensing data identifying: amplitudes of vibration signals, frequencies of the vibration signals, patterns of the vibration signals, times associated with the vibration signals, and locations along the fiber optic cable associated with the vibration signals. The device may process, with a machine learning model, the sensing data to determine a threat level of the activity to the fiber optic cable, the machine learning model having been trained based on historical information regarding detected vibrations, historical information regarding sources of the detected vibrations, and historical information regarding threat levels to the fiber optic cable. The device may perform one or more actions based on the threat level to the fiber optic cable.

Abstract: A DFOS system and method providing detection and localization of vehicle emergency stops which employs DFOS and machine learning techniques as an integrated solution for automatic, real-time, detection and localization of vehicle emergency stop events. Real time-location data from a buried optical sensing fiber located along a roadway is used to derive continuous vehicle trajectories while providing a wide coverage area for more accurate assessments. AI techniques are employed which track vehicles' speed and acceleration, locate vehicle deceleration events, and localize emergency stop events. Danger assessment is determined by analyzing a vehicle's anticipated and reproducible trajectory after an emergency breaking event—while ignoring stop-and-go events as low-risk events—and discovering stop-no-go events exhibiting large deceleration as high-risk events.

Abstract: A machine learning (ML)/artificial intelligence (AI) based distributed fiber optic sensing (DFOS) system and method providing detection and localization of gunshot events. In addition to the ML/AI DFOS, a signal processing pipeline that compresses an audible distributed acoustic sensing (DAS) waveform data into a small set of features that protects privacy of individuals while preserving the utility of acoustic events to detect the gunshot events and discriminate same from other events. A data-driven deep learning approach automatically predicts acoustic event types with higher accuracy that realized by prior art methods.

Abstract: A distributed fiber optic sensing (DFOS) system including a smart-mat that: 1) identifies indoor locations of moving persons/objects; 2) provides a 2D visual mapping; and 3) covers any blind spots with supplemental technologies including LiDAR, RF radar, etc. The DFOS system with smart-mat may be deployed virtually anywhere indoors and may even be constructed to replace carpeting. When our inventive DFOS system and smart-mat is deployed throughout an entire building, building safety and security is greatly improved by providing up-to-date localization information of building occupants while eliminating blind zones and reducing maintenance costs.

Abstract: A distributed fiber optic sensing (DFOS) system and method employing a fiber optic sensor cable that autonomously collects DFOS data and employs artificial intelligence/machine learning (AI/ML) to distinguish sections of the fiber optic sensor cable that are above ground (aerial), below ground (buried), and buried but occasionally above ground, in addition to any change(s) that occur with respect to the fiber optic sensor cable at such sections.

Abstract: Aspects of the present disclosure describe an unsupervised context encoder-based fiber sensing method that detects anomalous vibrations proximate to a sensor fiber that is part of a distributed fiber optic sensing system (DFOS) such that damage to the sensor fiber by activities producing and anomalous vibrations are preventable. Advantageously, our method requires only normal data streams and a machine learning based operation is utilized to analyze the sensing data and report abnormal events related to construction or other fiber-threatening activities in real-time. Our machine learning algorithm is based on waterfall image inpainting by context encoder and is self-trained in an end-to-end manner and extended every time the DFOS sensor fiber is optically connected to a new route. Accordingly, our inventive method and system it is much easier to deploy as compared to supervised methods of the prior art.

Abstract: Methods and systems for training a neural network include generate an image of a mask. A copy of an image is generated from an original set of training data. The copy is altered to add the image of a mask to a face detected within the copy. An augmented set of training data is generated that includes the original set of training data and the altered copy. A neural network model is trained to recognize masked faces using the augmented set of training data.

Abstract: A method of providing a hybrid distributed fiber optic sensing system (DFOS) that extends an existing fiber optic telecommunications network thereby providing that existing fiber optic telecommunications network with DFOS capabilities.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously enable smart refrigeration systems including retail.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing systems, methods, and structures that advantageously are employed to determine the location and depth of underground fiber-optic facilities that may be carrying telecommunications traffic.

Abstract: Disclosed are buried cable protection systems and methods that employ impulse signal detection by optical fiber sensing technologies, and which provide such protection automatically and in real-time. The methods theoretically model a time difference of arrival (TDoA) of an impulse wave travelling to a DFOS sensor fiber cable. A model employing a set of propagation relationships that account for vague knowledge about wave propagation speed and threat range(s) is fitted with parameters based on a numerical simulation—without specific knowledge of a source of vibration. As compared to vibration magnitude information, time of arrival (ToA) information is more consistent and less sensitive to ambiguities and inaccuracies. In addition, the model parameter can be adjusted adaptively when temporal resolution of the sensor changes or fluctuates.

Abstract: Systems, methods, and structures for efficiently identifying individual fibers located in a deployed cable that advantageously reduces laborious field efforts while reducing service outage time. The systems and methods locate a targeted fiber in a cable (“Cable ID”) and then identify the targeted fiber (“Fiber ID”) by detecting DFOS signal attentions—without cutting the optical fiber. Two distinct determinations may be made namely, Cable ID and Fiber ID. DFOS operation detects vibration signals occurring along a sensor fiber. As implemented, Cable ID is an interactive-machine learning-based algorithm that automatically locates cable position along a sensor fiber route. Fiber ID detects a signal attenuation by bending a group of fibers with bifurcation to pinpoint a targeted individual fiber within a fiber cable.

Abstract: Disclosed are distributed fiber optic sensing arrangements that—in sharp contrast to the prior art—utilize C-OTDR capabilities to detect an optical fiber end point while still maintaining operational DFOS vibration/acoustic signal sensing functions. Advantageously, such operations are performed automatically without requiring a manual confirmation. A change is made in digital signal processing in the C-OTDR operation by bypassing a high-pass-filtering stage when calculating intensity changes such that the DC signal component is preserved and used to differentiate from a “no-fiber” section. It then calculates the no-fiber section's signal level and uses a back-tracking operation to determine the fiber end automatically.

Abstract: A fiber optic sensing cable located along a side of a paved road and runs parallel to a driving direction is monitored by distributed fiber optic sensing (DFOS) using Rayleigh backscattering generated along the length of the optical sensor fiber cable under dynamic vehicle loads. The interaction of vehicles with roadway locations exhibiting distressed pavement generates unique patterns of localized signals that are identified/distinguished from signals resulting from vehicles operating on roadway exhibiting a smooth pavement surface. Machine learning methods are employed to estimate an overall road surface quality as well as localizing pavement damage. Power spectral density estimation, principal component analysis, support vector machine (SVM) combined with principal component analysis (PCA), local binary pattern (LBP), and convolutional neural network (CNN) are applied to develop the machine learning models.