Distributed real-time security monitoring and alerting

    公开(公告)号:US11158176B2

    公开(公告)日:2021-10-26

    申请号:US16808722

    申请日:2020-03-04

    Abstract: Systems and methods are disclosed for distributed real-time security monitoring and alerting. The methods include transmitting a selected portion of biometrics data as a watchlist to each worker unit. The portion of biometrics data is selected in response to respective characteristic data received from each worker unit. Facial recognition data is received from each worker unit. The facial recognition data includes a person of interest with an associated match confidence value calculated by each worker unit based on respective watchlists received by each worker unit. A combined match confidence value is calculated between a same person of interest identified in multiple facial recognition data received from each worker unit and the biometric data associated with an individual. The combined match confidence value is calculated in response to match confidence values associated with the same person of interest in respective facial recognition data being below a match confidence threshold.

    HIGH DYNAMIC RANGE DISTRIBUTED TEMPERATURE SENSING (DTS) USING PHOTON COUNTING DETECTION

    公开(公告)号:US20210318181A1

    公开(公告)日:2021-10-14

    申请号:US17191622

    申请日:2021-03-03

    Abstract: Aspects of the present disclosure describe distributed fiber optic sensing systems (DFOSa), methods, and structures for distributed temperature sensing (DTS) that 1) employs a GmAPD instead of a traditional LmAPD detector which advantageously produces a 10˜20 dB gain improvement of SNR for a far-end weak signal, thereby improving long range detectability; 2) employs an inventive gating scheme that advantageously and surprisingly overcomes the “dead time” problem for GmAPD working in SPC mode that plagues Geiger mode operation; and 3) third, employs an inventive post-processing technique that advantageously allows our methods to correct any dark noise caused signal distortion.

    JOINT ROLLING SHUTTER IMAGE STITCHING AND RECTIFICATION

    公开(公告)号:US20210279843A1

    公开(公告)日:2021-09-09

    申请号:US17182836

    申请日:2021-02-23

    Abstract: A computer-implemented method executed by at least one processor for applying rolling shutter (RS)-aware spatially varying differential homography fields for simultaneous RS distortion removal and image stitching is presented. The method includes inputting two consecutive frames including RS distortions from a video stream, performing keypoint detection and matching to extract correspondences between the two consecutive frames, feeding the correspondences between the two consecutive frames into an RS-aware differential homography estimation component to filter out outlier correspondences, sending inlier correspondences to an RS-aware spatially varying differential homography field estimation component to compute an RS-aware spatially varying differential homography field, and using the RS-aware spatially varying differential homography field in an RS stitching and correction component to produce stitched images with removal of the RS distortions.

    Deep learning based tattoo detection system with optimized data labeling for offline and real-time processing

    公开(公告)号:US11113838B2

    公开(公告)日:2021-09-07

    申请号:US16814248

    申请日:2020-03-10

    Abstract: A computer-implemented method executed by at least one processor for detecting tattoos on a human body is presented. The method includes inputting a plurality of images into a tattoo detection module, selecting one or more images of the plurality of images including tattoos with at least three keypoints, the at least three keypoints having auxiliary information related to the tattoos, manually labeling tattoo locations in the plurality of images including tattoos to create labeled tattoo images, increasing a size of the labeled tattoo images identified to be below a predetermined threshold by padding a width and height of the labeled tattoo images, training two different tattoo detection deep learning models with the labeled tattoo images defining tattoo training data, and executing either the first tattoo detection deep learning model or the second tattoo detection deep learning model based on a performance of a general-purpose graphical processing unit.

    EFFICIENT DECODING OF RFID TAGS BASED ON RELEVANT RN16 SELECTION

    公开(公告)号:US20210264122A1

    公开(公告)日:2021-08-26

    申请号:US17171172

    申请日:2021-02-09

    Abstract: A computer-implemented method is provided for picking a 16-bit random sequence (RN16) and generating an acknowledgement packet in a tag reading session. The method includes decoding RN16s from signals received by a plurality of antennas by treating signal interference as noise. The method further includes selecting the RN16 from the decoded RN16s based on properties of the decoded RN16s and the signals from which they are decoded in the tag reading session. The method also includes generating the acknowledgement packet based on the selected RN16.

    INTERPRETING CONVOLUTIONAL SEQUENCE MODEL BY LEARNING LOCAL AND RESOLUTION-CONTROLLABLE PROTOTYPES

    公开(公告)号:US20210248462A1

    公开(公告)日:2021-08-12

    申请号:US17158466

    申请日:2021-01-26

    Abstract: A method interprets a convolutional sequence model. The method converts an input data sequence having input segments into output features. The method clusters the input segments into clusters using respective resolution-controllable class prototypes allocated to each of classes. Each respective class prototype includes a respective output feature subset characterizing a respective associated class. The method calculates, using the clusters, similarity scores that indicate a similarity of an output feature to a respective class prototypes responsive to distances between the output feature and the respective class prototypes. The method concatenates the similarity scores to obtain a similarity vector. The method performs a prediction and prediction support operation that provides a value of prediction and an interpretation for the value responsive to the input segments and similarity vector. The interpretation for the value of prediction is provided using only non-negative weights and lacking a weight bias in the fully connected layer.

    GRAPH ENHANCED ATTENTION NETWORK FOR EXPLAINABLE POI RECOMMENDATION

    公开(公告)号:US20210248461A1

    公开(公告)日:2021-08-12

    申请号:US17153160

    申请日:2021-01-20

    Abstract: A method for employing a graph enhanced attention network for explainable point-of-interest (POI) recommendation (GEAPR) is presented. The method includes interpreting POI prediction in an end-to-end fashion by adopting an adaptive neural network, learning user representations by aggregating a plurality of factors, the plurality of factors including structural context, neighbor impact, user attributes, and geolocation influence, and quantifying each of the plurality of factors by numeric values as feature salience indicators.

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