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    Blue-printing interference for LTE access in unlicensed spectrum
    651.
    发明授权
    Blue-printing interference for LTE access in unlicensed spectrum 有权

    公开(公告)号：US10405209B2

    公开(公告)日：2019-09-03

    申请号：US15915436

    申请日：2018-03-08

    Applicant: NEC Laboratories America, Inc.

    Inventor： Karthikeyan Sundaresan ,  Eugene Chai ,  Mohammad Khojastepour ,  Sampath Rangarajan

    IPC: H04W24/02 ,  H04W72/08 ,  H04W24/10 ,  H04W16/22 ,  H04W16/14

    Abstract: A system, method, and computer program product are provided for blue-printing interference for mobile access in an unlicensed spectrum of a synchronous scheduled cellular access system. The system includes a cellular base station having a processor. The processor constructs and executes an intelligent measurement schedule of clients for uplink transmissions to obtain access measurements for the uplink transmissions. The intelligent measurement schedule is constructed for scalable access measurement overhead. The access measurements indicate interference dependencies between the clients. The processor estimates an interference topology and statistics of the interference topology, from the access measurements to form an interference blueprint. The processor adjusts the intelligent measurement schedule to overschedule the clients for the uplink transmissions to reduce spectrum utilization loss while minimizing client transmission collisions, based on the interference blueprint. The processor initiates the uplink transmissions for the clients in accordance with the adjusted intelligent measurement schedule.

    Surveillance system using deep network flow for multi-object tracking
    652.
    发明授权
    Surveillance system using deep network flow for multi-object tracking 有权

    公开(公告)号：US10402983B2

    公开(公告)日：2019-09-03

    申请号：US15695625

    申请日：2017-09-05

    Applicant: NEC Laboratories America, Inc.

    Inventor： Samuel Schulter ,  Wongun Choi ,  Paul Vernaza ,  Manmohan Chandraker

    IPC: G06K9/00 ,  G06T7/20 ,  G06T7/77 ,  G06T7/70 ,  H04N7/18 ,  G06K9/62

    Abstract: A surveillance system and method are provided. The surveillance system includes at least one camera configured to capture a set of images of a given target area that includes a set of objects to be tracked. The surveillance system includes a memory storing a learning model configured to perform multi-object tracking by jointly learning arbitrarily parameterized and differentiable cost functions for all variables in a linear program that associates object detections with bounding boxes to form trajectories. The surveillance system includes a processor configured to perform surveillance of the target area to (i) detect the objects and track locations of the objects by applying the learning model to the images in a surveillance task that uses the multi-object tracking, and (ii), provide a listing of the objects and their locations for surveillance task. A bi-level optimization is used to minimize a loss defined on a solution of the linear program.

    Large margin high-order deep learning with auxiliary tasks for video-based anomaly detection
    654.
    发明授权
    Large margin high-order deep learning with auxiliary tasks for video-based anomaly detection 有权

    公开(公告)号：US10402653B2

    公开(公告)日：2019-09-03

    申请号：US15380014

    申请日：2016-12-15

    Applicant: NEC Laboratories America, Inc.

    Inventor： Renqiang Min ,  Dongjin Song ,  Eric Cosatto

    IPC: G06K9/00 ,  H04N5/21 ,  G06N3/04 ,  G06N3/08 ,  G06K9/46 ,  G06K9/66 ,  G06K9/62

    Abstract: A computer-implemented method and system are provided for video-based anomaly detection. The method includes forming, by a processor, a Deep High-Order Convolutional Neural Network (DHOCNN)-based model having a one-class Support Vector Machine (SVM) as a loss layer of the DHOCNN-based model. An objective of the SVM is configured to perform the video-based anomaly detection. The method further includes generating, by the processor, one or more predictions of an impending anomaly based on the high-order deep learning based model applied to an input image. The method also includes initiating, by the processor, an action to a hardware device to mitigate expected harm to at least one item selected from the group consisting of the hardware device, another hardware device related to the hardware device, and a person related to the hardware device.

    UNSUPERVISED SPOOFING DETECTION FROM TRAFFIC DATA IN MOBILE NETWORKS
    657.
    发明申请
    UNSUPERVISED SPOOFING DETECTION FROM TRAFFIC DATA IN MOBILE NETWORKS 审中-公开

    公开(公告)号：US20190260778A1

    公开(公告)日：2019-08-22

    申请号：US16246774

    申请日：2019-01-14

    Applicant: NEC Laboratories America, Inc.

    Inventor： Haifeng Chen ,  Bo Zong ,  Christian Lumezanu

    IPC: H04L29/06 ,  H04W12/12 ,  G06N20/00

    Abstract: A method for detecting spoofing attacks from network traffic log data is presented. The method includes training a spoofing attack detector with the network traffic log data received from one or more mobile networks by extracting features that are relevant to spoofing attacks for training data, building a first set of vector representations for the network traffic log data, training an anomaly detection model by employing DAGMM, and obtaining learned parameters of DAGMM. The method includes testing the spoofing attack detector with the network traffic log data received from the one or more mobile networks by extracting features that are relevant to spoofing attacks for testing data, building a second set of vector representations for the network traffic log data, obtaining latent representations of the testing data, computing a z-score of the testing data, and creating a spoofing attack alert report listing traffic logs generating z-scores exceeding a predetermined threshold.

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