Abstract: Disclosed are systems, methods, and structures that provide superior DFOS rain intensity measurements and introduce a universal solution for rain intensity detection based on the data collected by distributed acoustic sensing (DAS) technology and a designed domain generalization method. As a result, systems and methods according to the present disclosure distinguish the rain intensity of a large area through which the fiber optic cables traverse and address the domain shift issue, by employing a domain generalization technique based on machine learning technology in which newly collected target domain inference data may be distributed differently from the previously captured training source domain data. To generalize the trained model to different target domains, source domain distributions are enriched by disturbing the distribution in the frequency domain.

Abstract: Disclosed are systems, methods, and structures that mitigate accumulative error in relative-measurement DFOS by employing, for each segment of the DFOS arrangement that records a certain number of an earlier estimation of each spatial segment. A predictive model in each buffer learns trends from the recorded history and predicts an output from the previous history. The reference is also updated using the prediction from the buffer. Systems, methods, and structures according to the present disclosure include the buffer structure that records a certain number of earlier estimations for each segment, a predictive model in each buffer that predicts the output of each segment according to the earlier estimations, reference updates using the prediction from the buffer tracker and workflow of real-time data processing with the buffer structure and tracker.

Abstract: Disclosed are systems and methods directed to transfer learning of cascaded EDFA models error accumulations in a multi-span system in which a two-step method using transfer learning is employed to reduce EDFA model error accumulation in a multi-span system. A first step of employs existing pretrained component-level ML-based EDFA models in chain to create a large synthetic dataset. The synthetic dataset includes all related features and labels for a specific end-to-end link. A source model is trained based on the large synthetic dataset. To accommodate a performance prediction gap between real link condition and the source model, which is trained on synthetic dataset, our method employs a second step that collects a few measurements from the real end-to-end link and makes few-shots learning to transfer the synthesis-data-based source model to real-data-based target model.

Abstract: Disclosed are systems, methods, and structures that employ distributed fiber sensing that utilizes existing fiber cables for detecting tree events. Our inventive systems and methods can detect when a tree or other types of vibration occur on the telecom cable or poles, creating a high-resolution spatial profile of the cable. Systems and methods according to aspects of the present disclosure distinguish between tree events and other vibrations, such as hammer strikes on a utility pole, by employing Information Noise Contrastive Estimation (InfoNCE) and contrastive learning. Our inventive approach accurately differentiates between different types of vibrations and identifies patterns specific to tree events.

Abstract: An improved near-infrared spectroscopy based handheld tissue oxygenation scanner and method in which the handheld tissue oxygenation scanner is a small device-like an optical mouse-employing a single transmitter-receiver pair, an optical switch, and a plurality of optical fibers that transmit light into tissue under examination and receive light after interacting with that tissue. The integration of a plurality of optical fibers wherein each individual one provides emission/reception of infrared radiation advantageously provides a higher density of tissue interrogation by the infrared radiation thereby providing faster operation and more information in a given time period. Additional features and advantages of our inventive system and method include computer control and the ability to connect to a variety of computing devices via BlueTooth and other communications techniques.

Abstract: Disclosed are systems and methods directed to a MIMO method that detects and localizes an object without requiring the object to emit a sound. Operationally, multiple speakers generate tone signals at different frequencies, while multiple acoustic sensors demodulate the complex amplitude of each frequency. By monitoring the feature variation of the relative phase (or intensity) vector from the complex amplitude, our method detects or localizes movement of the object. For large-scale applications, a fiber-optic system and method that employs distributed acoustic sensing (DAS) in which an optical fiber is used as one or more acoustic sensors located at points along the length of the optical fiber. A single DAS system provides numerous sensors using only a single optical fiber thereby enabling perfect synchronization and centralized signal processing. Notably, with such a DAS arrangement, cost and complexity is significantly reduced, while privacy is preserved.

Abstract: Disclosed are systems, methods, and structures that enhance advanced metering infrastructure (AMI) event classification with graph neural networks (GNNs) in which AMI data is represented as a graph, where each meter is a node and connections between nodes represent the physical or functional relationship between meters. As a result, our systems and methods capture dependency information between different meters and use this information to predict events and anomalies.

Abstract: Disclosed are systems, methods, and structures that increase overall sensing reach of a DFOS system and reduces system cost without sacrificing sensed signal quality by employing a cascaded arrangement of DFOS interrogators and operating method providing backscattering DFOS, maintaining each span within a desired length which can advantageously be determined by signal quality, pulse rate, or other factors such as physical layout. The cascaded DFOS interrogators work independently while sharing the pulse light produced by the first interrogator in a cascaded series of interrogators. The light is amplified in successive fiber spans and a circulator may be employed to cut-off any backscattered signal.

Abstract: Disclosed are systems, methods, and structures that provide more accurate temperature measurements and/or derived measurements using distributed fiber optic sensing (DFOS) systems and methods. DFOS systems and methods according to aspects of the present disclosure employ distributed fiber optic sensing that determines real-time temperature changes and vehicle trajectories from two-dimensional (2D) DFOS data with very few labeled data. The 2D data is first divided into multiple grids and then pre-processed with image distortion methods to enrich diversity of temperature change patterns. The transformed grids are used to pre-train a masked autoencoder, which advantageously does not require labels. The encoder of the autoencoder learns intrinsic features of temperature and traffic patterns, which are later connected to an estimation network to solve downstream tasks trained on a small set of labeled data.

Abstract: Disclosed is a stroke-based differentiable rendering for both representation of spatiotemporal sensor data and unsupervised spatiotemporal events detection wherein we encode DAS waterfall data into a structured latent space based on parameterized brushstrokes. The structured brushstroke representation can (1) suppress background noise and distracting clutters from the original waterfall data, (2) allow easy leverage of geometrical prior knowledge for physics-informed pattern recognition. Guided by multiple specially designed targets that emphasize different aspects of the original data, the optimized strokes not only preserve the salient information, but also align well with the original data in terms of spatial and temporal coordinates. As a results, it also provides pixel-level annotation as a byproduct. Based on long term DFOS data and cumulative statistics, we can further localize landmarks (such as traffic lights, manholes, etc.) from the waterfall data. These landmarks can be used for cable mapping.

Abstract: Disclosed are twin-based systems and methods for predicting solar power generation and optimizing power generation and distribution processes. Employed are a digital twin model of a solar power plant, which includes detailed representations of various components, such as solar panels, inverters, and transformers, as well as real-time weather data and historical data. This advantageously allows for accurate simulations of plant performance under various weather conditions and operational scenarios. Our systems and methods Incorporate novel machine learning algorithms that are trained on historical and real-time data from the digital twin model, weather data, solar power generation data, and other relevant factors.

Abstract: Disclosed are systems, methods, and structures employing a distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) system operating as an underwater wireless acoustic antenna in which an optical fiber sensor cable serves as a distributed acoustic antenna that receives multiple data from transmitters using an optical interrogator. Sensing channels, in the form of acoustic-antenna-array systems are located near transmitters taking advantage of the fact that these channels are automatically synchronized. The sensing channels may also be manually selected from software controlling the interrogator, and acoustic repeaters may be introduced as one data transmission mechanism. Acoustic tata transmission using an orthogonal frequency division multiplexed (OFDM) signal is demonstrated as a cure for data transmission using a DAS, such as multipath fading impacting bit error rate (BER) and limitations in acoustic transmission bandwidth.

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: Disclosed is a forward phase method using regular narrow line width CW laser, to cover the acoustic band with reduced processing speed, while tolerant laser frequency drift. A narrow linewidth CW laser is used to launch its power into an optical fiber at a transmitter side. At a receiver side, another narrow linewidth CW laser is used to coherently detect the received signal. The detected signal, which includes both X and Y polarizations, each having in-phase and quadrature to represent a “complex” channel, are connected to an ADC's inputs. Signal processing following the ADC inputs and extracts the phase change of the acoustic band.

Abstract: An improved near-infrared spectroscopy (NIRS) based handheld tissue oxygenation scanner employing a single transmitter-receiver pair, an integral inertial measurement sensor, an optional optical tracker, and wireless communications. Operational coordination among/between the elements are performed by a processor. The NIRS scanner device has a small foot print and permits the use of only a single transmitter/receiver pair to scan a large area of tissue manually, in real-time. Additional features and advantages of include battery power, hand held, may connect to a variety of computing devices via BlueTooth and/or WiFi, and may be modified to employ multiple transmitter/receivers to improve sensitivity and obtain absolute measurements.

Abstract: Disclosed are systems and methods that estimate machine distance to an optical fiber cable from sensing data collected using distributed fiber optic sensing (DFOS). Specialized hardware, DFOS that uses optical sensor fiber as a continuous spatial sensor along with a real time Artificial Intelligence (AI) processing unit, that detects threats within a proximity of buried fiber optic cable and a determines a moving direction of the threats such that it can effectively mitigate and contain the threats before damage to the buried fiber optic cable occurs. Advantageously, the system according to the present disclosure does not require any prior location knowledge or surveying prior to performing its monitoring.

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: Disclosed are machine learning (ML) based Distributed Fiber Optic Sensing (DFOS) systems, methods, and structures for Sonic Alert Pattern (SNAP) event detection performed in real time including an intelligent SNAP informatic system in conjunction with DFOS/Distributed Acoustic Sensing (DAS) and machine learning technologies that utilize SNAP vibration signals as an indicator. Without installation of additional sensors, vibration signals indicative of SNAP events are detected along a length of an existing optical fiber through DAS. Raw DFOS data is utilized—and not DFOS waterfall data—resulting in faster and more accurate information derivation as rich, time-frequency information in the raw DFOS/DAS waveform data is preserved. A deep learning module Temporal Relation Network (TRN) that accurately detects SNAP events from among chaotic signals of normal traffic is employed, making it reliable when applied to busy roads with dense traffic and vehicles of different speed.

Abstract: Dependence of EDFA gain shape on input power and input spectrum shape is modelled using a simple neural network-based architecture for amplifiers with different gains and output powers. The model can predict the gain within ±0.1 dB. While the model has good success predicting the performance of an EDFA it is trained with, it is not as successful when predicting a different EDFA, or the same EDFA with different pump power. Retraining the model with a small amount of supplementary data from a separate EDFA makes the model able to predict the performance of the second EDFA with little loss in performance. Experiments show that machine learning model of an EDFA is capable of modelling spectralhole burning effects accurately. As a result, it significantly outperforms black-box models that neglect inhomogenous effects. Model achieves an average RMSE error of 0.016 dB between the model and measurements.

Abstract: A fast optical fiber identification system and method employing an acoustic pen that is connected to a portable device (such as a laptop, a smartphone, an iPad). The pen generates acoustic signals under the control of the portable device. The portable device interacts with a DFOS (Distributed Fiber Optic Sensor, e.g., a DAS or DVS) interrogator to notify the interrogator about the generated signals and receives a detection result from the interrogator. The result is either illustrated using a graph on the portable device, or as a tone of different volume, to indicate the strength of the pen's signal detected by the interrogator. As the pen touches/excites vibrationally/acoustically each of the fibers, the portable device notifies the user about the detected signal's strength or presence/no-presence, which allows a technician to quickly identify the fiber of interest.