Abstract: Disclosed are integrated systems and methods employing distributed fiber optic sensing (DFOS) systems and methods to locate buried and/or aerial cables, as well as loop-back aerial cable sections and slack fiber lengths, in real-time. In sharp contrast to the prior art, systems and methods according to aspects of the present disclosure provide the location of cables without necessitating the opening of manholes/hand holes or pull cables to the round—thereby making the overall process faster, more cost-effective and more accurate and precise. Our inventive systems and methods provide a reliable and accurate alternative to current methods utilizing optical time domain reflectometry (OTDR), which requires known and accessible locations and may be ineffective for legacy fibers. By implementing systems and methods according to the present disclosure, service providers, carriers, and owners can efficiently maintain optical fiber networks and ensure reliable services for users.

Abstract: Systems and methods include calibrating physical parameters of acoustic data using a deterministic model related to hardware configurations that generated the acoustic data to provide an intermediate layer of data. The intermediate layer of data is then calibrated using environmental factors related to the acoustic data by employing machine learning to provide a multichannel data output. A loss is optimized between the multichannel data output and multichannel distributed-optic fiber sensing (DFOS) data to train a hybrid transfer model to translate between DFOS data and acoustic data.

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: Disclosed are vehicle-infrastructure interaction systems and methods employing a distributed fiber optic sensing (DFOS) system operating with pre-deployed fiber-optic telecommunication cables buried alongside/proximate to highways/roadways which provide 24/7 continuous information stream of vehicle traffic at multiple sites; only require a single optical sensor cable that senses/monitors multiple locations of interest and multiple lanes of traffic; the single optical sensor cable measures multiple related information (multi-parameters) about a vehicle, including driving speed, wheelbase, number of axles, tire pressure, and others, that can be used to derive secondary information such as weight-in-motion; and overall information about a fleet of vehicles, such as traffic congestion or traffic-cargo volume. Different from merely traffic counts, our approach can provide the count grouped by vehicle-types and cargo weights.

Abstract: Aspects of the present disclosure describe improved distributed acoustic sensing using dynamic range suppression of optical time domain reflectometry either by using a feedback loop comprising optical and electrical elements or using a nonlinear element in the electrical domain after coherent detection. When using a feedback loop, the amplitude of the periodic waveform of coherent OTDR can be inverted. This allows optical pre-compensation of the received optical signal before coherent detection with the goal of minimizing amplitude dynamic range. Alternatively, a nonlinear element in the electrical domain can reduce amplitude dynamic range before sampling by analog-to-digital converters (ADC).

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: Described is a novel framework, we call intent-based computing jobs assignment framework, for efficiently accommodating a clients' computing job requests in a mobile edge computing infrastructure. We define the intent-based computing job assignment problem, which jointly optimizes the virtual topology design and virtual topology mapping with the objective of minimizing the total bandwidth consumption. We use the Integer Linear Programming (ILP) technique to formulate this problem, and to facilitate the optimal solution. In addition, we employ a novel and efficient heuristic algorithm, called modified Steiner tree-based (MST-based) heuristic, which coordinately determines the virtual topology design and the virtual topology mapping. Comprehensive simulations to evaluate the performance of our solutions show that the MST-based heuristic can achieve an efficient performance that is close to the optimal performance obtained by the ILP solution.

Abstract: A procedure to solve the DFOS placement problem that uses a genetic algorithm to achieve a global optimization of sensor placement. First, our procedure according to aspects of the present disclosure defines a fitness function that counts the number of DFOS sensors used. Second, the procedure uses a valid DFOS placement assignment to model an individual in the genetic algorithm. Each individual consists of N genes, where N is the number of nodes in the given network infrastructure, e.g., N=|V|. Each gene has two genomes: (1) a list of 0s and/or 1s, in which is represent the network nodes that are equipped with DFOS sensors, and 0s represent the nodes that are not equipped with DFOS sensors; (2) a list of sensing fiber routes. An individual that has smallest number of is in their genes will be considered as the strongest individual. Thirdly, the procedure randomly generates a population of individuals.

Abstract: Aspects of the present disclosure describe systems, methods, and structures for the machine learning based regression of complex coefficients of a linear combination of spatial modes from a multimode optical fiber.

Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that employ a distributed fiber optic sensor placement procedure that advantageously provides a desirable sensor coverage over a network at minimal cost.

Abstract: Described is a novel framework, we call intent-based computing jobs assignment framework, for efficiently accommodating a clients' computing job requests in a mobile edge computing infrastructure. We define the intent-based computing job assignment problem, which jointly optimizes the virtual topology design and virtual topology mapping with the objective of minimizing the total bandwidth consumption. We use the Integer Linear Programming (ILP) technique to formulate this problem, and to facilitate the optimal solution. In addition, we employ a novel and efficient heuristic algorithm, called modified Steiner tree-based (MST-based) heuristic, which coordinately determines the virtual topology design and the virtual topology mapping. Comprehensive simulations to evaluate the performance of our solutions show that the MST-based heuristic can achieve an efficient performance that is close to the optimal performance obtained by the ILP solution.

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: 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 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) systems, methods, and structures that advantageously provide traffic monitoring, and traffic management which improves the safety and efficiency of a roadway.

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 fiber failures.

Abstract: Aspects of the present disclosure describe a method of placement of sensors for DFOS systems, methods, and structures that advantageously employ concurrent sensing. In sharp contrast to the prior art, our inventive method—a heuristic method based on the Explore-and-Pick (EnP) algorithm, which we call a modified EnP (mEnP) method—includes two procedures. The first procedure of our mEnP method explores all possible sensing fiber routes (both linear and star-like routes) for each node in the given network. The second procedure applies a modified greedy algorithm for minimum set cover to select the minimum set of DFOS assignment to fully cover all the links in the given network.

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: 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.