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    Hardware allocation in RFIC based on machine learning
    3.
    发明授权
    Hardware allocation in RFIC based on machine learning 有权

    公开(公告)号：US11960952B2

    公开(公告)日：2024-04-16

    申请号：US17834845

    申请日：2022-06-07

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Shailesh Chaudhari ,  Hyukjoon Kwon ,  Kee-Bong Song ,  Dongwoon Bai

    IPC: G06K19/06 ,  G06K19/07 ,  G06N3/08

    CPC classification number: G06K19/0723 ,  G06N3/08

    Abstract: A system and method for configuring an RF network based on machine learning. In some embodiments, the method includes: receiving, by a first neural network, a first state and a first state transition, the first state including: one or more identifiers for available active ports, and a set of available connections between two or more circuit elements, each of the circuit elements being one of: (1) a first circuit type, (2) a second circuit type that operatively connects a circuit element of the first circuit type to one of the available active ports, and (3) the available active ports; and generating, by the first neural network, a first estimated quality value, for the first state transition.

    SYSTEMS AND METHODS FOR IMAGE DENOISING USING DEEP CONVOLUTIONAL NETWORKS
    5.
    发明申请
    SYSTEMS AND METHODS FOR IMAGE DENOISING USING DEEP CONVOLUTIONAL NETWORKS 有权

    公开(公告)号：US20230043310A1

    公开(公告)日：2023-02-09

    申请号：US17972961

    申请日：2022-10-25

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Zengli Yang ,  Long Bao ,  Shuangquan Wang ,  Dongwoon Bai ,  Jungwon Lee

    IPC: G06T5/00 ,  G06T5/30 ,  G06T5/50 ,  G06N3/08 ,  G06N3/04

    Abstract: A method includes: computing noise data by subtracting, by a processing circuit, a noisy image from a corresponding ground truth image; clustering, by the processing circuit, a plurality of noise values of the noise data based on intensity values of the corresponding ground truth image; permuting, by the processing circuit, a plurality of locations of the noise values of the noise data within each cluster; generating, by the processing circuit, a synthetic noise image based on the permuted locations of the noise values; adding, by the processing circuit, the synthetic noise image to the corresponding ground truth image to generate a synthetic noisy image; and augmenting an image dataset for training a neural network to perform image denoising with the synthetic noisy image.

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