Nec Corporation

Nec Laboratories America, Inc.

Verizon Patent and Licensing Inc.

Ming-Fang Huang

Ting Wang

Shaobo Han

Yuheng Chen

Philip N Ji

Tiejun J Xia

Glenn A Wellbrock

Ezra Ip

Yaowen Li

Milad Salemi

Yue-Kai Huang

Yue Tian

Yoshiaki Aono

Jingnan Zhao

Hansi Liu

Manmohan Chandraker

Eric Cosatto

Shuji Murakami

Neda Cvijetic

Dayou Qian

Yi-Hsuan Tsai

Milorad Cvijetic

Gaurav Sharma

Jian Fang

Masoud Faraki

Da Pan

Francesco Pittaluga

Lei Tao

Tingfeng Li

Xiang Xu

Yueheng Chen

Sheng Zhong

A method for underground cable localization by fast time series template matching and distributed fiber optic sensing (DFOS) includes: providing the DFOS system including a length of optical sensor fiber; a DFOS interrogator in optical communication with the optical sensor fiber, said DFOS interrogator configured to generate optical pulses, introduce the generated pulses into the length of optical sensor fiber, and receive backscattered signals from the length of the optical sensor fiber; and an intelligent analyzer configured to analyze DFOS data received by the DFOS interrogator and determine from the backscattered signals, vibrational activity occurring at locations along the length of the optical sensor fiber; deploying a programmable vibration generator to a field location proximate to the length of optical sensor fiber; transmitting to the programmable vibration generator a unique vibration pattern to be generated by the vibration generator; and operating the programmable vibration generator to generate the unique vibration pattern transmitted; and operating the DFOS system and collecting/analyzing the determined vibrational activity to further determine vibrational activity indicative of the unique vibration pattern generated by the vibration generator.

Underground cable localization by fast time series template matching

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.

Fiber identification without cut point using distributed fiber optic sensing

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.

Road surface conditions detection by distributed optic fiber system

A fiber-based roadside condition sensing system is provided. The system includes a fiber optic cable arranged in various roadside locations for Distributed Vibration Sensing (DVS) and Distributed Acoustic Sensing (DAS) at the various roadside locations. The system further includes a machine-learning-based analyzer for selectively providing any of an early warning and a prevention of various detected conditions responsive to a machine-learning-based analysis of results from the DVS and the DAS.

Fiber sensing on roadside applications

Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously identify location(s) of construction—or other activities—taking place along fiber optic cable routes that can damage the fiber optic cables.

Object localization and threat classification for optical cable protection

Systems, and methods for automatically identifying an underground optical fiber cable length from DFOS systems in real time and pair it with GPS coordinates that advantageously eliminate the need for in-field inspection/work by service personnel to make such real-time distance/location determinations. As such, inefficient, error-prone and labor-intensive prior art methods are rendered obsolete. Operationally, our method disclosure involves driving vehicles including GPS to generate traffic patterns and automatically mapping traffic trajectory signals from a deployed buried fiber optic cable to locate geographic location(s) of the buried fiber optic cable. Traffic patterns are automatically recognized; slack in the fiber optic cable is accounted for; location of traffic lights and other traffic control devices/structures may be determined; and turns in the fiber optic cable may likewise be determined.

Fiber sensing using supervisory path of submarine cables

Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously extend DFOS techniques to anomaly detection using optical magnetism switches (OMC) that are integrated into the DFOS system.

DFOS self-anomaly detection system for safer infrastructures

Aspects of the present disclosure describe DFOS systems, methods, and structures that advantageously extract road traffic from DFOS vibration patterns such that anomaly detection is possible. Sensed vibration data is represented accurately as a set of points, where each point is denoted as a tuple with elements indicating a time stamp, a location along a length of a DFOS optical sensing cable, and vibration strength detected at the location at the time. Traffic pattern detection is based on a progressive probabilistic Hough transform (PPHT) that exploits global information from an entire spatial-temporal data snapshot to assess a cause of detected vibrations.

Road traffic extraction for unknown anomaly detection using distributed fiber optic sensing

A fiber optic sensing technology for vehicle run-off-road incident automatic detection by an indicator of sonic alert pattern (SNAP) vibration patterns. A machine learning method is employed and trained and evaluated against a variety of heterogeneous factors using controlled experiments, demonstrating applicability for future field deployment. Extracted events resulting from operation of our system may be advantageously incorporated into existing management systems for intelligent transportation and smart city applications, facilitating real-time alleviation of traffic congestion and/or providing a quick response rescue and clearance operation.

Distributed intelligent SNAP informatics

Distributed fiber optic sensing (DFOS) systems, methods and structures for determining the proximity of vibration sources located perpendicular to a sensor fiber that is part of the DFOS system that may potentially threaten/damage or otherwise compromise the sensor fiber itself. Systems, methods, and structures according to aspects of the present disclosure employ Artificial Intelligence (AI) methodology(ies) that use as input a fundamental physical understanding of wave propagation and attenuation in the ground along with Bayesian inference and Maximum Likelihood Estimation (MLE) techniques for estimating/determining the proximity of potentially damaging vibration sources to the optical sensor fiber.

Perpendicular distance prediction of vibrations by distributed fiber optic sensing

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.

Smart stadium applications using fiber optic sensing

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.

Systems and methods for utilizing machine learning to minimize a potential of damage to fiber optic cables

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.

Context encoder-based fiber sensing anomaly detection

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.

Facial recognition for masked individuals

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. The method provides a length of fiber optic cable, wherein said fiber optic cable conveys communications traffic; provides a DFOS interrogator system in optical communication with the communications fiber optic cable; extends the length of communications fiber optic cable with first and second lengths of fiber optic sensory cable, and operates the DFOS interrogator system such that first sensory data is generated in the first length of fiber optic sensory cable and second sensory data is generated in the second length of fiber optic sensory cable and conveyed to the DFOS interrogator system via the communications fiber optic cable, wherein the first type of sensory data and the second type of sensory data is a type selected from the group consisting of acoustic data, temperature data, and vibration data and the first type of sensory data is not the same type as the second type of sensory data.

Growing community of inventors

Princeton, NJ, United States of America

Ming-Fang Huang