Abstract: Aspects of the present disclosure describe systems, methods and structures providing detection and localization of disaster-related indoor events via hybrid ethernet/optical fiber cable.

Abstract: 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

Abstract: Aspects of the present disclosure describe monitoring of optical fiber by distributed temperature sensing (DTS) and determining optical fiber degradation and/or abnormal environmental events including landslides, fires, etc., from DTS data.

Abstract: 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).

Abstract: A method for the non-destructive localization of underground optical fiber cables that utilizes distributed fiber optic sensing (DFOS) and time difference of arrival (TDOA) data associated with received signals of impulsive surface vibrations to localize the underground optical fiber cable by solving non-linear least square optimization problem(s). The method utilizes only TDOA information of received signals of impulsive surface vibrations to determine such localizations and does not require synchronization between a vibration source and DFOS receiver. The method may employ man-made impulsive vibrations to determine the localization. In operation the method involves 1) impulse-like vibration excitation and signal collection; 2) TDOA estimation of the received signals from DFOS; and 3) non-linear least square optimization applied to the TDOA information for underground optical fiber localization.

Abstract: Systems and methods for determining fiber optic facility (cable) location using distributed fiber optic sensing (DFOS) and sequence pattern matching of vibration excitation signals applied to a sensor fiber. The use of sequence pattern matching with unique pattern codes allow for the precise determination of location and length of deployed fiber cable while exhibiting an immunity from environmental vibrations proximate to the fiber. As a result improved measurements are realized and false alarms are eliminated.

Abstract: Systems, and methods for automatically identifying individual fibers within an optical fiber cable that are experiencing some form of significant signal impairment such as a fiber cut. Operationally, distributed fiber optic sensing (DFOS) systems are used to detect reflected signals along the length of the affected fiber(s) and a determination of affected fiber(s) is made from changes in reflection characteristics.

Abstract: 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.

Abstract: 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—in real-time—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.

Abstract: Distributed fiber optic sensing (DFOS) systems and methods that automatically detect vibration signal patterns from waterfall data recorded by DFOS system operations in real- time and associate the detected vibration signal patterns to GPS location coordinates without human intervention or interpretation. When embodied as a computer vision-based operation according to aspects of the present disclosure, our inventive systems and method provide accurate, cost-efficient, and objective determination without relying on humans and their resulting bias' and inconsistencies.

Abstract: Aspects of the present disclosure describe physical layer security in optical telecommunications networks wherein a filtering-based physical security is provided by a wavelength tunable distributed fiber optical sensing (DFOS) system operating simultaneously on the telecommunications network.

Abstract: Aspects of the present disclosure describe systems, methods and structures providing wide-area traffic monitoring based on distributed fiber-optic sensing (DFOS) that employs deep neural network(s) for denoising noisy waterfall traces measured by the DFOS. Such systems, methods, and structures according to aspects of the present disclosure may advantageously monitor multiple highways/roadways using a single interrogator and optical fiber switch(es) which provides traffic information along every sensing point of existing, deployed, in-service optical telecommunications facilities.

Abstract: 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.

Abstract: Aspects of the present disclosure describe systems, methods and structures providing bidirectional optical fiber communication and sensing using the same fiber transmission band and bidirectional WDM fiber sharing such that communications channels and optical fiber sensing channel(s) coexist on the same fiber. As a result, nonlinear interaction between communications channels and interrogating pulse(s) of sensing are much reduced or eliminated.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously measure wind speed at utility poles that support fiber optic cables and—in many applications—electrical and/or other communications cables (i.e., cable television).

Abstract: 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.

Abstract: 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.

Abstract: Aspects of the present disclosure describe optical fiber sensing systems, methods and structures disclosing a distributed optical fiber sensor network constructed on a switched optical fiber telecommunications infrastructure to detect temperatures, acoustic effects, and vehicle traffic—among others—demonstrated with a number of different network topologies.

Abstract: 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.

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.