Nec Corporation

Nec Laboratories America, Inc.

Nec Research Institute, Inc.

Verizon Patent and Licensing Inc.

At&t Intellectual Property I, LP

Nec USA, Inc.

Other

Leland Stanford Junior University

Nec Corportation

Ting Wang

Philip N Ji

Lei Xu

Ivan B Djordjevic

Junqiang Nochur Hu

Dayou Qian

Ming-Fang Huang

Shaoliang Zhang

Yue Tian

Sarper Ozharar

Neda Cvijetic

Yoshiaki Aono

Fatih Yaman

Yangmin Ding

Jianjun Yu

Milorad Cvijetic

Yue-Kai Huang

Yoshihisa Inada

Ankitkumar N Patel

Yao Li

Chonggang Wang

Ezra Ip

Takaaki Ogata

Shaobo Han

Yasuhiro Aoki

Ryuji Aida

Giovanni Milione

Jian Fang

Yi Weng

Yaowen Li

Yuheng Chen

Zilong Ye

Tiejun J Xia

Glenn A Wellbrock

Yuanqiu Luo

Shuji Murakami

Yutaka Yano

Konstantinos Kanonakis

Wei Wei

Lusheng Ji

Narayan Prasad

Osamu Matsuda

Kenneth W Fasanella

Kojiro Watanabe

Yuanda Xu

Jingjing Zhang

Tsutomu Tajima

Junquiang Hu

Lei Zong

Allan Schweitzer

Hussam G Batshon

Akihiro Tanaka

Yuji Abe

Kazuo Takagi

Maximilian A Ott

Eduardo F Mateo

Eduardo Mateo Rodriquez

Yawei Yin

Tyrone Kwok

Robert Saperstein

Chuanhui Ma

Milorad Cvjetic

Lei Zhong

Sampath Rangarajan

Manmohan Chandraker

Guofei Jiang

Eric Cosatto

Takanori Inoue

Yoshihiko Suemura

Kohei Nakamura

Yi-Hsuan Tsai

Makoto Shibutani

Yueping Zhang

Shinya Nakamura

Tineke Thio

Takefumi Oguma

Eduardo Rodriguez

Toshihiko Kusano

Michael P Snyder

Naoto Maeda

Gaurav Sharma

Richard A Linke

Stephen B Weinstein

Kai Yang

Arthur Dogariu

Martin Roetteler

Ruixue Fan

Rauf Izmailov

Katsuyuki Mino

Masoud Faraki

Francesco Pittaluga

Milad Salemi

Xin Wang

Atul Singh

Jason P Jue

Lei Tao

Xiyan Li

Tingfeng Li

Hadi F Ghaemi

Yequn Zhang

Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously sense/monitor outdoor facilities and structures including outdoor cabinets containing fiber optic facilities in which the cabinet/fiber optic cable contained therein are configured to provide superior acoustic sensing. Further outdoor facilities and structures that are monitored include manhole structures. Superior DFOS/DAS monitoring results are obtained by employing a machine learning-based analysis method that employs a temporal relation network (TRN).

Outdoor application of distributed fiber optic sensing / acoustic sensing

A radio-controlled, two-way acoustic modem for operating with a distributed fiber optic sensing (DFOS) system including circuitry that receives radio signals including configuration information, configures the modem to operate according to the configuration information, and generate acoustic signals that are detected by the DFOS system. The acoustic modem includes one or more sensors that detect environmental information that is encoded in the acoustic signals for further reception by the DFOS system. The received configuration information may change the operating times, sensors or other operating aspects of the modem as desired an such information may be transmitted from a fixed location or a mobile vehicle. The acoustic modem may include several vibrator elements that provide a spatially multiplexed vibration signal imparted on the DFOS system fiber sensor.

Spatially multiplexed acoustic modem

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

Disclosed are systems and methods to determine barometric pressure 1) using multiple low-cost pressure sensors located at known heights instead of a single high-cost sensor; 2) determines an actual pressure value—not by averaging multiple sensors but rather optimizing an expected error in each individual one of them and utilize their known sensor heights thereby defining a new error function; 3) our approach is scalable, i.e. the number of sensors can be increased, and multiple sensors can be grouped together into smaller cells such that each group of cell can be corrected separately, and can even be corrected among themselves. Finally, our systems and methods according to the present disclosure can advantageously be integrated with a distributed fiber optic sensing (DFOS) system via acoustic modems thereby providing extremely wide-area external, or interior buildings pressure readings.

Low-cost high precision barometric pressure measurement system

A distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) system and method employing a fiber optic sensor cable that collects vibrational data of individual utility poles suspending the fiber optic sensor cable and stores the vibrational data in a central office (CO). Machine learning (ML) models are developed, trained, and utilized to analyze vibrational features of the utility poles and determine their integrity. Additionally, DFOS/DAS systems and methods according to the present disclosure determine the location(s) of fiber coils that exist along a length of a fiber optic sensor cable.

System to measure coil locations and lengths on aerial fiber cables by distributed fiber sensing for decision making

Computer vision based, wide-area snow/water level estimation methods using disparity maps. In one embodiment our method provides rich depth-information using a stereo camera and image processing. Scene images at normal and snow/rain weather conditions are obtained by a double-lens stereo camera and a disparity map is generated from the scene images at left and right lenses using a self-supervised deep convolutional network. In another embodiment, our method uses a single point snow/water level sensor, a stationary monocular camera to measure snow/water levels covering a wide area.

Computer vision based wide-area snow/water level estimation using disparity map

A flexible, rapid deployable perimeter monitoring system and method that employs distributed fiber optic sensing (DFOS) technologies and includes a deployment/operations field vehicle including an interrogator and analyzer/processor. The deployment/operations field vehicle is configured to field deploy a ruggedized fiber optic sensor cable in an arrangement that meets a specific application need, and subsequently interrogate/sense via DFOS any environmental conditions affecting the deployed fiber optic sensor cable. Such sensed conditions include mechanical vibration, acoustic, and temperature that may be advantageously sensed/evaluated/analyzed in the deployment/operations vehicle and subsequently communicated to a central location for further evaluation and/or coordination with other monitoring systems. Upon completion, the field vehicle and DFOS reconfigure a current location or redeployed to another location.

Flexible and rapid deployable field monitoring system

Systems, methods, and structures for colorless distributed fiber optic sensing/distributed vibration sensing (DOFS/DVS) over dense wavelength division multiplexing (DWDM) telecommunications facilities that operate over a C-band wavelength range spanning from 1525 nm to 1565 nm wherein the DOFS/DVS systems exhibit suitable reconfigurability of its wavelength to match a wavelength of a desired testing channel providing DOFS/DVS capabilities to existing DWDM communications infrastructure as a retrofit. The DFOS/DVS systems include an optical sensor fiber; a colorless DFOS/DVS interrogator in optical communication with the optical sensor fiber and configured to generate optical pulses, introduce the generated pulses into the optical sensor fiber, and receive backscattered signals from the optical sensor fiber; and an intelligent analyzer configured to analyze colorless DFOS/DVS data received by the DFOS/DVS interrogator and determine from the backscattered signals, vibrational activity occurring at locations along the optical sensor fiber.

Colorless distributed fiber optic sensing / distributed vibration sensing

A radio-controlled, two-way acoustic modem for operating with a distributed fiber optic sensing (DFOS) system including circuitry that receives radio signals including configuration information, configures the modem to operate according to the configuration information, and generate acoustic signals that are detected by the DFOS system. The acoustic modem includes one or more sensors that detect environmental information that is encoded in the acoustic signals for further reception by the DFOS system. The received configuration information may change the operating times, sensors or other operating aspects of the modem as desired an such information may be transmitted from a fixed location or a mobile vehicle.

Telecom cable tension screening technique based on wave propagation and distributed acoustic sensing

Aspects of the present disclosure describe systems and methods that advantageously enable vibration-induced optical phase measurement at a centralized optical line terminal (OLT) in a PON architecture. In sharp contrast to existing distributed fiber sensing systems and methods, the optical phase measurements of the present disclosure do not rely on back scattering mechanisms and maintain a sufficient optical signal to noise ratio (OSNR) even after round-trip splitting loss in the PON.

Vibration sensing over passive optical networks (PONs) using forwarding optical phase retrieval and time-domain multiplexed (TDM) switching

Aspects of the present disclosure describe snow/water level detection using distributed fiber optic sensing/distributed acoustic sensing (DFOS/DAS) and an integrated ultrasonic device that advantageously operates over existing optical telecommunications facilities carrying live telecommunications traffic—or optical facilities deployed specifically for such detection. DFOS/DAS monitoring of snow/water level advantageously monitors large areas with high sensitivity while exhibiting robustness to changing environmental conditions and employs a remote (utility pole or other mounting) mounted ultrasonic sensor/transducer that provides snow/water level data in real-time as a coded vibrational signal.

Snow / water level detection with distributed acoustic sensing integrated ultrasonic device

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

Aspects of the present disclosure describe Rayleigh-based DTSS that utilizes few-mode fiber (FMF), which supports multiple spatial modes. For each spatial mode, a wavelength-scanning configuration gives the relative wavelength (or frequency) shift between two consecutive measurements. The temperature and strain changes can therefore be separated through different temperature/strain sensitivities of various mode-pairs. Advantageously, Rayleigh-based DTSS according to aspects of the present disclosure removes temperature-strain ambiguity, enhances measurement accuracy, reduces errors. and enables new features for multi-parameter sensing.

Few-mode Rayleigh-based distributed fiber sensor for simultaneous temperature and strain sensing

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

Growing community of inventors

West Windsor, NJ, United States of America

Ting Wang