Abstract: A method for executing a multi-task deep learning model for learning trends in multivariate time series is presented. The method includes collecting multi-variate time series data from a plurality of sensors, jointly learning both local and global contextual features for predicting a trend of the multivariate time series by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends, and employing a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data to model a short-term dependency between local time series data and subsequent trends.

Abstract: A distributed acoustic sensing (DAS) system that collects many frequency bands within the system (in particular, analog to digital converter, or ADC) bandwidth. The bands are divided into multiple groups, and within each group, the inter-band interference from band i to band j is at least l1 dB lower than the power of band j. For different groups, the inter-band interference is at least l2 dB lower than the power of band j, that l2?l1+Cl, where Cl is a value related to round-trip fiber loss. The bands belonging to the same group are cascaded one after another, with little guard time in between, or packed back-to-back. This arrangement advantageously increases the overall signal power, resulting in an improved SNR.

Abstract: 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.

Abstract: Methods and systems for responding to changing conditions include training a model, using a processor, using trajectories that resulted in a positive outcome and trajectories that resulted in a negative outcome. Training is performed using an adversarial discriminator to train the model to generate trajectories that are similar to historical trajectories that resulted in a positive outcome, and using a cooperative discriminator to train the model to generate trajectories that are dissimilar to historical trajectories that resulted in a negative outcome. A dynamic response regime is generated using the trained model and environment information. A response to changing environment conditions is performed in accordance with the dynamic response regime.

Abstract: 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.

Abstract: A distributed fiber optic sensing (DFOS) system including a smart-mat that: 1) identifies indoor locations of moving persons/objects; 2) provides a 2D visual mapping; and 3) covers any blind spots with supplemental technologies including LiDAR, RF radar, etc. The DFOS system with smart-mat may be deployed virtually anywhere indoors and may even be constructed to replace carpeting. When our inventive DFOS system and smart-mat is deployed throughout an entire building, building safety and security is greatly improved by providing up-to-date localization information of building occupants while eliminating blind zones and reducing maintenance costs.

Abstract: Methods and systems for responding to changing conditions include training a model, using a processor, using trajectories that resulted in a positive outcome and trajectories that resulted in a negative outcome. Training is performed using an adversarial discriminator to train the model to generate trajectories that are similar to historical trajectories that resulted in a positive outcome, and using a cooperative discriminator to train the model to generate trajectories that are dissimilar to historical trajectories that resulted in a negative outcome. A dynamic response regime is generated using the trained model and environment information. A response to changing environment conditions is performed in accordance with the dynamic response regime.

Abstract: 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.

Abstract: An advance in the art is made according to aspects of the present disclosure directed to a method that determines virtual topology design and resource allocation for dynamic intent-based computing jobs in a mobile edge computing infrastructure when client requests are dynamic. Our method according to aspects of the present disclosure is an unsupervised machine learning approach, so that there is no need for manual labeling or pre-processing in advance, while a training process and decision making is performed online. In sharp contrast to the prior art, our method according to aspects of the present disclosure utilizes reinforcement learning techniques to make an efficient assignment in which two neural networks—a policy neural network and a value neural network—are used interactively to achieve the assignment. A training process is performed through a batch (or group) processing style in an online manner.

Abstract: 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.

Abstract: 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.

Abstract: An acoustic modem for operating with a distributed fiber optic sensing (DFOS) system that generates acoustic signals that are detected by the DFOS system. The acoustic modem is powered by energy harvesters includes one or more sensors that detect environmental information that is encoded in the acoustic signals for further reception by the DFOS system. The acoustic modem may include several vibrator elements that provide a vibration signals imparted on the DFOS system fiber sensor.

Abstract: 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.

Abstract: A distributed fiber optic sensing (DFOS) system and method employing a fiber optic sensor cable that autonomously collects DFOS data and employs artificial intelligence/machine learning (AI/ML) to distinguish sections of the fiber optic sensor cable that are above ground (aerial), below ground (buried), and buried but occasionally above ground, in addition to any change(s) that occur with respect to the fiber optic sensor cable at such sections.

Abstract: 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.

Abstract: 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 and such information may be transmitted from a fixed location or a mobile vehicle.

Abstract: 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.

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)/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: 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.