DIFFERENTIABLE RENDERING AND EVOLUTION STRATEGIES FOR LANDMARK DETECTION AND MATCHING UNDER UNCERTAINTY

    公开(公告)号:US20250124716A1

    公开(公告)日:2025-04-17

    申请号:US18901822

    申请日:2024-09-30

    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.

    SPATIOTEMPORAL AND SPECTRAL CLASSIFICATION OF ACOUSTIC SIGNALS FOR VEHICLE EVENT DETECTION OVER DEPLOYED FIBER NETWORKS

    公开(公告)号:US20240241275A1

    公开(公告)日:2024-07-18

    申请号:US18414419

    申请日:2024-01-16

    CPC classification number: G01V1/288 G01V1/001 G01V1/226 G08G1/0104

    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.

    UNDERGROUND CABLE LOCALIZATION BY FAST TIME SERIES TEMPLATE MATCHING

    公开(公告)号:US20230142932A1

    公开(公告)日:2023-05-11

    申请号:US17979755

    申请日:2022-11-02

    CPC classification number: G01D5/35358 G01H9/004

    Abstract: A method for underground cable localization by fast time series template matching and distributed fiber optic sensing (DFOS) includes: providing the DFOS system including a length of optical sensor fiber; a DFOS interrogator in optical communication with the optical sensor fiber, said DFOS interrogator configured to generate optical pulses, introduce the generated pulses into the length of optical sensor fiber, and receive backscattered signals from the length of the optical sensor fiber; and an intelligent analyzer configured to analyze DFOS data received by the DFOS interrogator and determine from the backscattered signals, vibrational activity occurring at locations along the length of the optical sensor fiber; deploying a programmable vibration generator to a field location proximate to the length of optical sensor fiber; transmitting to the programmable vibration generator a unique vibration pattern to be generated by the vibration generator; and operating the programmable vibration generator to generate the unique vibration pattern transmitted; and operating the DFOS system and collecting/analyzing the determined vibrational activity to further determine vibrational activity indicative of the unique vibration pattern generated by the vibration generator.

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