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    Context Encoder-Based Fiber Sensing Anomaly Detection
    2.
    发明申请
    Context Encoder-Based Fiber Sensing Anomaly Detection 有权

    公开(公告)号：US20220196464A1

    公开(公告)日：2022-06-23

    申请号：US17556939

    申请日：2021-12-20

    Applicant: NEC Laboratories America, Inc.

    Inventor： Shaobo HAN ,  Ming-Fang HUANG ,  Eric COSATTO

    IPC: G01H9/00 ,  H04B10/071

    Abstract: Aspects of the present disclosure describe an unsupervised context encoder-based fiber sensing method that detects anomalous vibrations proximate to a sensor fiber that is part of a distributed fiber optic sensing system (DFOS) such that damage to the sensor fiber by activities producing and anomalous vibrations are preventable. Advantageously, our method requires only normal data streams and a machine learning based operation is utilized to analyze the sensing data and report abnormal events related to construction or other fiber-threatening activities in real-time. Our machine learning algorithm is based on waterfall image inpainting by context encoder and is self-trained in an end-to-end manner and extended every time the DFOS sensor fiber is optically connected to a new route. Accordingly, our inventive method and system it is much easier to deploy as compared to supervised methods of the prior art.

    MACHINE LEARNING BASED CLASSIFICATION OF HIGHER-ORDER SPATIAL MODES
    5.
    发明申请
    MACHINE LEARNING BASED CLASSIFICATION OF HIGHER-ORDER SPATIAL MODES 审中-公开

    公开(公告)号：US20200119830A1

    公开(公告)日：2020-04-16

    申请号：US16655103

    申请日：2019-10-16

    Applicant: NEC Laboratories America, Inc.

    Inventor： Giovanni MILIONE ,  Philip JI ,  Eric COSATTO

    IPC: H04J14/04 ,  G06K9/62 ,  G06N20/00 ,  G06N3/02

    Abstract: Aspects of the present disclosure describe systems, methods and structures for classification of higher-order spatial modes using machine learning systems and methods in which the classification of high-order spatial modes emitted from a multimode optical fiber does not require indirect measurement of the complex amplitude of a light beam's electric field using interferometry or, holographic techniques via unconventional optical devices/elements, which have prohibitive cost and efficacy; classification of high-order spatial modes emitted from a multimode optical fiber is not dependent on a light beam's alignment, size, wave front (e.g. curvature, etc.), polarization, or wavelength, which has prohibitive cost and efficacy; classification of higher-order spatial modes from a multimode optical fiber does not require a prohibitive amount of experimentally generated training examples, which, in turn, has prohibitive efficacy; and the light beam from a multimode optical fiber can be advantageously separated into two orthogonal polarization components, such that, the different linear combination of higher order spatial modes comprising each polarization component can be classified.

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