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    SYSTEM FOR APPLICATION SELF-OPTIMIZATION IN SERVERLESS EDGE COMPUTING ENVIRONMENTS
    2.
    发明公开
    SYSTEM FOR APPLICATION SELF-OPTIMIZATION IN SERVERLESS EDGE COMPUTING ENVIRONMENTS 审中-公开

    公开(公告)号：US20230153182A1

    公开(公告)日：2023-05-18

    申请号：US17964170

    申请日：2022-10-12

    Applicant: NEC Laboratories America, Inc.

    Inventor： Giuseppe Coviello ,  Kunal Rao ,  Biplob Debnath ,  Srimat Chakradhar

    IPC: G06F9/54 ,  G06F9/50

    CPC classification number: G06F9/543 ,  G06F9/505

    Abstract: A method for implementing application self-optimization in serverless edge computing environments is presented. The method includes requesting deployment of an application pipeline on data received from a plurality of sensors, the application pipeline including a plurality of microservices, enabling communication between a plurality of pods and a plurality of analytics units (AUs), each pod of the plurality of pods including a sidecar, determining whether each of the plurality of AUs maintains any state to differentiate between stateful AUs and stateless AUs, scaling the stateful AUs and the stateless AUs, enabling communication directly between the sidecars of the plurality of pods, and reusing and resharing common AUs of the plurality of AUs across different applications.

    Person search system based on multiple deep learning models
    4.
    发明授权
    Person search system based on multiple deep learning models 有权

    公开(公告)号：US11250243B2

    公开(公告)日：2022-02-15

    申请号：US16808983

    申请日：2020-03-04

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yi Yang ,  Giuseppe Coviello ,  Biplob Debnath ,  Srimat Chakradhar

    IPC: G06K9/00 ,  G06F16/9038

    Abstract: A computer-implemented method executed by at least one processor for person identification is presented. The method includes employing one or more cameras to receive a video stream including a plurality of frames to extract features therefrom, detecting, via an object detection model, objects within the plurality of frames, detecting, via a key point detection model, persons within the plurality of frames, detecting, via a color detection model, color of clothing worn by the persons, detecting, via a gender and age detection model, an age and a gender of the persons, establishing a spatial connection between the objects and the persons, storing the features in a feature database, each feature associated with a confidence value, and normalizing, via a ranking component, the confidence values of each of the features.

    ENCODING AND DECODING IMAGES USING DIFFERENTIABLE JPEG COMPRESSION
    8.
    发明申请
    ENCODING AND DECODING IMAGES USING DIFFERENTIABLE JPEG COMPRESSION 有权

    公开(公告)号：US20250008132A1

    公开(公告)日：2025-01-02

    申请号：US18755150

    申请日：2024-06-26

    Applicant: NEC Laboratories America, Inc.

    Inventor： Biplob Debnath ,  Deep Patel ,  Srimat Chakradhar ,  Christoph Reich

    IPC: H04N19/33 ,  H04N19/124 ,  H04N19/176 ,  H04N19/186 ,  H04N19/625

    Abstract: Systems and methods are provided for encoding and decoding images using differentiable JPEG compression, including converting images from RGB color space to YCbCr color space to obtain a luminance and chrominance channels, and applying chroma subsampling to the chrominance channels to reduce resolution. The YCbCr image is divided into pixel blocks and a DCT is performed on the pixel blocks to obtain DCT coefficients. DCT coefficients are quantized using a scaled quantization table to reduce precision, and quantized DCT coefficients are encoded using lossless entropy coding, forming a compressed JPEG file decoded by reversing the lossless entropy coding to obtain quantized DCT coefficients, which are dequantized using the scaled quantization table to restore the precision. The dequantized DCT coefficients are converted back to a spatial domain using an IDCT, the chrominance channels are upsampled to original resolution, and the YCbCr image is converted back to the RGB color space.

    System for application self-optimization in serverless edge computing environments
    9.
    发明授权
    System for application self-optimization in serverless edge computing environments 有权

    公开(公告)号：US11847510B2

    公开(公告)日：2023-12-19

    申请号：US17964170

    申请日：2022-10-12

    Applicant: NEC Laboratories America, Inc.

    Inventor： Giuseppe Coviello ,  Kunal Rao ,  Biplob Debnath ,  Srimat Chakradhar

    IPC: G06F9/44 ,  G06F9/54 ,  G06F9/50

    CPC classification number: G06F9/543 ,  G06F9/505

    Abstract: A method for implementing application self-optimization in serverless edge computing environments is presented. The method includes requesting deployment of an application pipeline on data received from a plurality of sensors, the application pipeline including a plurality of microservices, enabling communication between a plurality of pods and a plurality of analytics units (AUs), each pod of the plurality of pods including a sidecar, determining whether each of the plurality of AUs maintains any state to differentiate between stateful AUs and stateless AUs, scaling the stateful AUs and the stateless AUs, enabling communication directly between the sidecars of the plurality of pods, and reusing and resharing common AUs of the plurality of AUs across different applications.

    VIDEO CLASSIFIER
    10.
    发明公开
    VIDEO CLASSIFIER 审中-公开

    公开(公告)号：US20230237805A1

    公开(公告)日：2023-07-27

    申请号：US18157915

    申请日：2023-01-23

    Applicant: NEC Laboratories America, Inc.

    Inventor： Biplob Debnath ,  Oliver Po ,  Srimat Chakradhar

    IPC: G06V20/56 ,  G06V20/40 ,  G06V10/74 ,  G06V10/764 ,  B60W30/09

    CPC classification number: G06V20/56 ,  B60W30/09 ,  G06V10/761 ,  G06V10/764 ,  G06V20/46

    Abstract: A computer-implemented method is provided. The method includes classifying a video clip of consecutive video frames into one of predefined new classes in relation to a base training set class. The method further includes controlling a system of a motor vehicle for accident avoidance responsive to the one of the predefined classes indicating an impending collision. The classifying step includes extracting video frame features from the video clip. The classifying step further includes aggregating the video frame features of the consecutive video frames into a single frame feature to form a video level feature presentation. The classifying step also includes mapping, by a distance-based classifier, the video level feature presentation into a classification prediction based on cosine similarity.

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