Abstract: Systems and methods are provided for utilizing polarization parameters obtained from an optical network to determine vibrations in optical fibers using coherent optics equipment and machine learning techniques. A method, according to one implementation, includes the step of obtaining a time-series dataset that includes measurements of polarization characteristics of light traversing an optical fiber of an optical network. The method also includes the step of detecting vibration characteristics of the optical fiber based on the time-series dataset. In some implementations, the time-series dataset may be a multi-variate dataset and the polarization characteristics may be related to transients in a State of Polarization (SOP). The SOP, for example, may be represented by an amplitude and a phase of an electric field vector and may be defined as having one of a linear polarization, elliptical polarization, and circular polarization.

Abstract: Systems and methods include receiving network data from a network; estimating large flows from the network data utilizing a first statistical approach; responsive to the network being large flow dominant, recursively estimating flows in the network utilizing the first statistical approach until an exit condition is reached; and combining the recursively estimated flows and forming a traffic matrix based thereon. The recursively estimating flows can include estimating a set of large flows utilizing the first statistical approach and freezing a resulting estimate while leaving smaller flows unsolved; repeating the estimating for a next set of large flows from the unsolved smaller flows; and continuing the repeating until the exit condition is reached.

Abstract: Deep Neural Networks (DNNs) for forecasting future data are provided. In one embodiment, a non-transitory computer-readable medium is configured to store computer logic having instructions that, when executed, cause one or more processing devices to receive, at each of a plurality of Deep Neural Network (DNN) forecasters, an input corresponding to a time-series dataset of a plurality of input time-series datasets. The instructions further cause the one or more processing devices to produce, from each of the plurality of DNN forecasters, a forecast output and provide the forecast output from each of the plurality of DNN forecasters to a DNN mixer for combining the forecast outputs to produce one or more output time-series datasets.

Abstract: Systems and methods include receiving network topology information of a network including a plurality of routers; receiving link measurements defining bandwidth on links in the network; determining routes in the network based on the network topology information; and utilizing the routes and the link measurements to determine an estimate of an initial traffic matrix that includes the bandwidth between origin routers and destination routers.

Abstract: An optical network element includes a connection to an optical fiber in an optical line system including a coherent receiver; a microphone configured to detect sound; and circuitry connected to the microphone and configured to cause transmission of information related to sounds detected by the microphone to a receiver at an end of the optical line system, wherein the transmission is over the optical fiber in the optical line system to the coherent receiver. The optical network element can include a polarization controlling device connected to the circuitry and configured to modulate a state-of-polarization (SOP) envelope for the transmission.

Abstract: Systems and methods for compressing network data are provided. According to one implementation, a method includes the step of collecting raw telemetry data from a network environment. The raw telemetry data is collected as time-series datasets. The method also includes the step of compressing the time-series datasets by deploying the time-series datasets as a Deep Neural Network (DNN) in the network environment itself. The time-series datasets are configured to be substantially reconstructed from the DNN using predictive functionality of the DNN.

Abstract: Systems and methods for forecasting time series data are provided. In one implementation, a method includes the steps of obtaining time series data from a network. The method also comprises the step of determining one or more forecasters to be used based on a type of the time series data and based on previous training that determine that the one or more forecasters from a number of forecasters are best suited for the type of time series data. The method further comprises making a forecast of the time series data using the one or more forecasters and to save and/or display the forecast.

Abstract: Systems and methods include receiving network topology information of a network including a plurality of routers; receiving link measurements defining bandwidth on links in the network; determining routes in the network based on the network topology information; and utilizing the routes and the link measurements to determine an estimate of an initial traffic matrix that includes the bandwidth between origin routers and destination routers.

Abstract: An optical network element includes a connection to an optical fiber in an optical line system including a coherent receiver; a microphone configured to detect sound; and circuitry connected to the microphone and configured to cause transmission of information related to sounds detected by the microphone to a receiver at an end of the optical line system, wherein the transmission is over the optical fiber in the optical line system to the coherent receiver. The optical network element can include a polarization controlling device connected to the circuitry and configured to modulate a state-of-polarization (SOP) envelope for the transmission.

Abstract: Systems and methods are provided for utilizing polarization parameters obtained from an optical network to determine vibrations in optical fibers using coherent optics equipment and machine learning techniques. A method, according to one implementation, includes the step of obtaining a time-series dataset that includes measurements of polarization characteristics of light traversing an optical fiber of an optical network. The method also includes the step of detecting vibration characteristics of the optical fiber based on the time-series dataset. In some implementations, the time-series dataset may be a multi-variate dataset and the polarization characteristics may be related to transients in a State of Polarization (SOP). The SOP, for example, may be represented by an amplitude and a phase of an electric field vector and may be defined as having one of a linear polarization, elliptical polarization, and circular polarization.

Abstract: Deep Neural Networks (DNNs) for forecasting future data are provided. In one embodiment, a non-transitory computer-readable medium is configured to store computer logic having instructions that, when executed, cause one or more processing devices to receive, at each of a plurality of Deep Neural Network (DNN) forecasters, an input corresponding to a time-series dataset of a plurality of input time-series datasets. The instructions further cause the one or more processing devices to produce, from each of the plurality of DNN forecasters, a forecast output and provide the forecast output from each of the plurality of DNN forecasters to a DNN mixer for combining the forecast outputs to produce one or more output time-series datasets.

Abstract: Systems, methods, and computer-readable medium for forecasting a time-series are provided. In one implementation, a method is configured to include a step of providing a time-series to a neural network including one or more branches for processing one or more portions of the time-series. In each of the one or more branches, the method includes separating the respective portion of the time-series into individual portions and applying each portion to a respective sub-branch of a plurality of sub-branches of the one or more branches. The method also includes generating forecasting coefficients for each output time point in each of the respective sub-branches and providing a forecast of the time-series based at least on the forecasting coefficients.

Abstract: Example embodiments relate to a power-driven, digitally metered dispenser where cylindrical piston driven jar dispensers of varying diameters are used for transferring repeatable and specific amounts of flowable composition into smaller containers, like HRTicker® dispensers, applicators, pumps, syringes, and jars. Dosing is accomplished by dialing the desired dosage and the pressing of a push-button to dispense. The various example embodiments consist of a motor powered threaded plunger that travels in the vertical axis in accordance with a predetermined and programmed linear displacement. The end user dials the desired dispensation into the computers program via a main control dial.

Abstract: Example embodiments relate to a power-driven, digitally metered dispenser where cylindrical piston driven jar dispensers of varying diameters are used for transferring repeatable and specific amounts of flowable composition into smaller containers, like HRTicker® dispensers, applicators, pumps, syringes, and jars. Dosing is accomplished by dialing the desired dosage and the pressing of a push-button to dispense. The various example embodiments consist of a motor powered threaded plunger that travels in the vertical axis in accordance with a predetermined and programmed linear displacement. The end user dials the desired dispensation into the computers program via a main control dial.