Nec Corporation

Yuheng Chen

Ming-Fang Huang

Ting Wang

Shaobo Han

Milad Salemi

Jingnan Zhao

Manmohan Chandraker

Yue Tian

Yi-Hsuan Tsai

Gaurav Sharma

Jian Fang

Masoud Faraki

Francesco Pittaluga

Tingfeng Li

Xiang Xu

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

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

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

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

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

An advance in the art is made according to aspects of the present disclosure directed to distributed fiber optic sensing systems (DFOS), methods, and structures that advantageously monitor and identify&#x2014;in real-time&#x2014;roadway traffic and patterns across a multiple-lane highway by employing a multiple-transverse fiber optic cable arrangement of optical fiber cable positioned under the highway/roadway to detect, monitor, and/or identify traffic.

Multiple lane real-time traffic monitor and vehicle analysis using distributed fiber sensing

Aspects of the present disclosure describe systems, methods and structures for determining any location on a deployed fiber cable from an optical time domain reflectometry (OTDR) curve using a movable mechanical vibration source to stimulate tiny vibration of fiber in deployed fiber cable along the cable route and a fiber sensing system at a central office to detect the vibration(s). Latitude and longitude of the location(s) of the vibration source is measured with a GPS device and a dynamic-OTDR distance is measured at central office (CO) simultaneously. The collected GPS location data and corresponding dynamic-OTDR distance data are paired and saved into a database. This saved data may be processed to graphically overlie a map thereby providing exact cable location on the map thereby providing carriers/service providers the ability to improve fiber fault location on a deployed fiber cable much faster and more accurately than presently possible using methods available in the art.

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South Brunswick, NJ, United States of America

Yuheng Chen