Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) systems, methods, and structures that advantageously employ chirped interrogation pulses (sensing signals) that exhibit a smooth amplitude profile produced by appending leading and trailing edges of the sensing signals with out-of-band chirps. By appending leading and trailing out-of-band signals to a chirped pulse sensing signal to produce a “smooth” amplitude profile a coexistence of sensing and telecommunications signals on a same optical fiber is facilitated.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously provide DFOS and WDM communications over amplified, multi-span optical WDM optical telecommunications facilities using all Raman amplification and coding schemes. Our all-Raman amplification operates stably—without isolators—and provides sufficient gain to compensate for fiber span loss for both DFOS signals and WDM channel signals—at the same time. Furthermore, our inventive techniques employ signal coding, such as MB-TGD-OFDR for DAS, and we operate our DFOS operation power at a much lower power level as compared to pulse interrogation techniques. With improved OSNR and reduced power using signal coding along with our distributed Raman amplification, our DFOS systems can co-exist with WDM communication channels on the same amplified multi-span fiber optic links over great distances.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) systems, methods, and structures that employ machine learning and provide for the automatic remote inspection and condition evaluation of wooden utility poles. Operationally, audio (acoustic) signals are obtained using DFOS/DAS when a service technician/inspector strikes the wooden utility poles with an impact tool such as a hammer. Historical audio DFOS/DAS signals that include signals resulting from hollow (decayed) utility poles and solid (good) poles are used to train one or more machine learning models and the trained machine learning models are subsequently used to evaluate real-time impact data collected from DFOS/DAS and determine utility pole condition in real-time.

Abstract: Distributed fiber optic sensing systems (DFOS) methods, and structures that employ DVS / DAS point sensors and a two-stage processing methodology / structure that advantageously enable point sensors to send sensor data at any time - thereby providing significant processing advantages over the prior art.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensor systems, methods, and structures that advantageously enable/provide for the proper placement/assignment of sensors in the DFOS network to provide for high reliability, fault tolerant operation that survives multiple fiber failures.

Abstract: A photonics-assisted platform for time series prediction and classification that performs signal processing directly after the signal acquisition before any analog-to-digital conversion by using a hardware neural network with recurrent connections, implemented in a silicon photonic chip. This neural network recurrency can be implemented in silicon photonics with a much lower latency than state-of-the-art electronic systems. The recurrent neural network can detect temporal correlations and extract features from the time series signal, and therefore reduce the latency constraints for the analog-to-digital conversion and further digital signal processing.

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: 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.

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: 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 and may advantageously provide DOFS/DVS capabilities to existing DWDM communications infrastructure as a retrofit.

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 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: Remote terminal field device monitoring using distributed fiber optic sensing (DFOS) comprising a length of optical sensor fiber extending into the remote terminal, said remote terminal including one or more field devices located therein, wherein said length of optical sensor fiber located in the remote terminal includes one or more fiber loops formed in the length of optical sensor fiber, said one or more fiber loops respectively positioned proximate to the one or more field device(s) located in the remote terminal, a DFOS interrogator in optical communication with the optical sensor fiber and configured to generate optical pulses, introduce the generated pulses into the length of optical sensor fiber, and receive backscattered signals from the one or more fiber loops formed in 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, environmental activity occurring at the one or more fi

Abstract: Aspects of the present disclosure directed to frequency drift compensation for coded-DAS systems that use chirped pulses as a probe signal. Our inventive approach estimates timing jitter by correlating the amplitude of the estimated Rayleigh impulse response of every frame with a reference frame, and then re-aligns each frame by the estimated timing jitter. As the amount of timing jitter varies within a frame, every frame is divided into blocks where all samples have similar timing jitter, and perform timing jitter estimation and compensation on a block-by-block, frame-by-frame basis using an overlap-and-save method. Tracking of a slowly changing channel is enabled by allowing the reference frame to be periodically updated.

Abstract: 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.

Abstract: 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.

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: Aspects of the present disclosure describe dynamic road traffic noise mapping using DFOS over a telecommunications network that enables mapping of road traffic-induced noise at any observer location. DFOS is used to obtain instant traffic data including vehicle speed, volume, and vehicle types, based on vibration and acoustic signal along the length of a sensing fiber along with location information. A sound pressure level at a point of interest is determined, and traffic data associated with such point is incorporated into a reference noise emission database and a wave propagation theory for total sound pressure level prediction and mapping. Real-time wind speed using DFOS—such as distributed acoustic sensing (DAS)—is obtained to provide sound pressure adjustment due to the wind speed.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing/distributed acoustic sensing (DFOS/DAS) systems, methods, and structures that advantageously provide rainfall intensity measurements along an entire length of a fiber optic sensor. using existing telecommunications optical fiber—which may be part of a multi-fiber, fiber optic cable—that may simultaneously carry live telecommunications traffic. The DFOS/DAS fiber optic sensing is used to obtain vibration and/or sound data from which rainfall intensity measurements may be made along the entire length of the DFOS/DAS fiber optic sensor.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously sense/monitor intra-data center operations using self-coherent detection. Advantageously, sensing signal(s) and data signal(s) are optically multiplexed such that the sensing signal(s) are generated and detected using the same optoelectronic components as data generation and detection while requiring only minimal changes to transponder arrangements and no additional bandwidth to digital-to-analog converters (DAC) or analog-to-digital converters (ADC).