Abstract: Aspects of the present disclosure describe distributed optical fiber sensing for smart city applications in which distributed optical fiber sensing is integrated with a surveillance system into a single system such that the distributed sensing system may detect an event of interest and the surveillance system including cameras may be reoriented in response to verify and/or examine and/or acquire video of the event. Of particular advantage such distributed fiber sensing may include distributed acoustic sensing (DAS) for vibrational sensing and distributed temperature sensing (DTS) for temperature sensing. The integrated system employs shared optical fiber transport for the distributed sensing and the surveillance.

Abstract: Aspects of the present disclosure describe rapid temperature measurement by wavelength modulation spectroscopy (WMS) that determines gas temperature from 2ƒ signals from two absorption lines by WMS methodologies even when the gas concentration is sufficiently high to saturate optical absorptions. In sharp contrast to the prior art, rapid temperature measurement by WMS according to aspects of the present disclosure employs both a 2ƒ signal ratio and gas concentration determined from the 2ƒ signal.

Abstract: Aspects of the present disclosure describe optical fiber sensing systems, methods and structures disclosing a distributed fiber sensor network constructed on an existing, live network, data carrying, optical fiber telecommunications infrastructure to detect temperatures, acoustic effects, and vehicle traffic—among others. Of particular significance, sensing systems, methods, and structures according to aspects of the present disclosure may advantageously identify specific network locations relative to manholes/handholes and environmental conditions within those manholes/handholes namely, normal, flooded, frozen/iced, etc.

Abstract: Disclosed are improved distributed optical fiber sensing systems, methods, and structures employing disparate point sensors that utilize uni-directional signal transmission via the distributed optical fiber such that a separate communications network for the disparate point sensors is not required.

Abstract: Aspects of the present disclosure describe optical fiber sensing systems, methods and structures and application employing coherent detection of backscattered signals.

Abstract: Aspects of the present disclosure describe systems, methods and structures and applications of optical fiber sensing. Of significance, systems, methods, and structures according to aspects of the present disclosure may reuse and/or retrofit/upgrade existing optical fiber cables as part of optical fiber sensing that may find important societal application including intrusion detection, road traffic monitoring and infrastructure health monitoring. Combining such optical fiber sensing with artificial intelligence (AI) further enables powerful applications at low(er) cost.

Abstract: Aspects of the present disclosure describe systems, methods and structures including high-density submarine/undersea reconfigurable optical add/drop multiplexers (ROADM) having remote wavelength selective switch (WSS) redundancy.

Abstract: Aspects of the present disclosure describe systems, methods and structures including high-density submarine/undersea reconfigurable optical add/drop multiplexers (ROADM) having remote wavelength selective switch (WSS) redundancy.

Abstract: 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.

Abstract: Aspects of the present disclosure describe systems, methods and structures for classification of higher-order spatial modes using machine learning systems and methods in which the classification of high-order spatial modes emitted from a multimode optical fiber does not require indirect measurement of the complex amplitude of a light beam's electric field using interferometry or, holographic techniques via unconventional optical devices/elements, which have prohibitive cost and efficacy; classification of high-order spatial modes emitted from a multimode optical fiber is not dependent on a light beam's alignment, size, wave front (e.g. curvature, etc.

Abstract: Aspects of the present disclosure describe systems, methods and structures in which a hybrid neural network combining a CNN and several ANNs are shown useful for predicting G-ONSR for Ps-256QAM raw data in deployed SSMF metro networks with 0.27 dB RMSE. As demonstrated, the CNN classifier is trained with 80.96% testing accuracy to identify channel shaping factor. Several ANN regression models are trained to estimate G-OSNR with 0.2 dB for channels with various constellation shaping.

Abstract: A method is provided for danger prediction. The method includes generating fully-annotated simulated training data for a machine learning model responsive to receiving a set of computer-selected simulator-adjusting parameters. The method further includes training the machine learning model using reinforcement learning on the fully-annotated simulated training data. The method also includes measuring an accuracy of the trained machine learning model relative to learning a discriminative function for a given task. The discriminative function predicts a given label for a given image from the fully-annotated simulated training data. The method additionally includes adjusting the computer-selected simulator-adjusting parameters and repeating said training and measuring steps responsive to the accuracy being below a threshold accuracy.

Abstract: A battery management system is provided. The battery management system includes a memory for storing program code. The battery management system further includes a processor for running the program code to extract features from battery operation data. The processor further runs the program code to train a deep learning model to model a battery degradation process of a battery using the extracted features. The processor also runs the program code to generate, using the deep learning model, a prediction of a battery capacity degradation based on the battery operation data and a current battery capacity of the battery. The processor additionally runs the program code to control an operation of the battery responsive to the prediction of the battery capacity degradation.

Abstract: Aspects of the present disclosure describe systems, methods and structures for distributed fiber sensing systems including interrogator and attached fiber in which the interrogator includes a common line card and function-specific, pluggable front end in which the line card is configurable and supports different signal processing paths and automatically senses the front-end type and uses corresponding firmware/software or signal processing path(s) to process sensed data.

Abstract: Aspects of the present disclosure describe systems, methods and structures employing time-space de-noising for distributed sensor.

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 systems, methods and structures employing optical fiber sensing to monitor highway/roadway/street conditions (i.e., potholes, pavement cracks, etc.) in real-time, continuously, and while the highway/roadway/street remains in operation (in-service monitoring). Systems, methods, and structures according to aspects of the present disclosure may employ machine learning (ML) algorithms including neural networks to provide and or report on highway conditions so monitored/sensed. Of further advantage, systems, methods, and structures for optical fiber sensing for highway maintenance may operate in real-time, continuously, long-term, in-service, and may employ existing telecommunications optical cables without additional deployment cost(s) or disruption of telecommunications traffic.

Abstract: Aspects of the present disclosure describe systems, methods and structures employing a two-stage detection for distributed vibration detection (DVS) in which a first step provides an abstracted/pre-processing data and the second step—based on the first step result—only processes locations that have or might have activity.

Abstract: Aspects of the present disclosure describe systems, methods and structures for optical fiber nonlinearity compensation using neural networks that advantageously employ machine learning (ML) algorithms for nonlinearity compensation (NLC) that advantageously provide a system-agnostic model independent of link parameters, and yet still achieve a similar or better performance at a lower complexity as compared with prior-art methods. Systems, methods, and structures according to aspects of the present disclosure include a data-driven model using the neural network (NN) to predict received signal nonlinearity without prior knowledge of the link parameters. Operationally, the NN is provided with intra-channel cross-phase modulation (IXPM) and intra-channel four-wave mixing (IFWM) triplets that advantageously provide a more direct pathway to underlying nonlinear interactions.

Abstract: Aspects of the present disclosure describe a method for digital coherent transmission systems that advantageously provides low-complexity, single-step nonlinearity compensation based on artificial intelligence (AI) implemented in a deep neuron network (DNN).