Apparatus and method for paging overlap mitigation

    公开(公告)号:US10362623B2

    公开(公告)日:2019-07-23

    申请号:US15260972

    申请日:2016-09-09

    Abstract: An apparatus and a method. The apparatus includes a first subscriber identity module (SIM); a second SIM; a dual SIM resource controller (DSRC) connected to the first SIM and the second SIM; and a radio frequency (RF) communication entity connected to the DSRC, wherein the DSRC performs one of scheduling a first paging preparation period of the first SIM prior to a second paging preparation period of the second SIM and re-attempting by the first SIM an RF request for the RF communication entity after an initial RF request for the RF communication entity is not granted.

    System and method for cell search enhancement in an LTE system
    3.
    发明授权
    System and method for cell search enhancement in an LTE system 有权
    LTE系统中小区搜索增强的系统和方法

    公开(公告)号:US09432922B1

    公开(公告)日:2016-08-30

    申请号:US14964227

    申请日:2015-12-09

    CPC classification number: H04W48/16 H04W4/20

    Abstract: Apparatuses (including user equipment (UE) and modem chips for UE), systems, and methods for reducing false alarms (FAs) and miss-detections (MDs) in cell search results are described. In one method, the minimum of a value for the accumulated metric for a first short code and a value for the accumulated metric for a second short code is used as a pruning metric for each candidate cell. One or more thresholds are applied to the pruning metric to determine whether the candidate cell will be pruned from the final candidate cell list.

    Abstract translation: 描述了设备(包括UE的用户设备(UE)和用于UE的调制解调器芯片),用于减少小区搜索结果中的假警报(FA)和错误检测(MD)的系统和方法。 在一种方法中,将用于第一短码的累积度量的值的最小值和第二短码的累积度量的值用作每个候选小区的剪枝度量。 将一个或多个阈值应用于剪枝度量以确定候选小区是否将从最终候选小区列表中剪除。

    ESTIMATION MODEL FOR INTERACTION DETECTION BY A DEVICE

    公开(公告)号:US20230360425A1

    公开(公告)日:2023-11-09

    申请号:US18298128

    申请日:2023-04-10

    CPC classification number: G06V40/11 G06V10/7715 G06V10/82 G06T19/006

    Abstract: A method and device are disclosed for estimating an interaction with the device. The method includes configuring a first token and a second token of an estimation model according to first features of a 3D object, applying a first weight to the first token to produce a first-weighted input token and applying a second weight that is different from the first weight to the second token to produce a second-weighted input token, and generating, by a first encoder layer of an estimation-model encoder of the estimation model, an output token based on the first-weighted input token and the second-weighted input token. The method may include receiving, at a 2D feature extraction model, the first features from a backbone, extracting, by the 2D feature extraction model, second features including 2D features, and receiving, at the estimation-model encoder, data generated based on the 2D features.

    System and method for providing dolly zoom view synthesis

    公开(公告)号:US11423510B2

    公开(公告)日:2022-08-23

    申请号:US16814184

    申请日:2020-03-10

    Abstract: A method and an apparatus are provided for providing a dolly zoom effect by an electronic device. A first image with a first depth map and a second image with a second depth map are obtained. A first synthesized image and a corresponding first synthesized depth map are generated using the first image and the first depth map respectively. A second synthesized image and a corresponding second synthesized depth map are generated using the second image and the second depth map respectively. A fused image is generated from the first synthesized image and the second synthesized image. A fused depth map is generated from the first synthesized depth map and the second synthesized depth map. A final synthesized image is generated based on processing the fused image and the fused depth map.

    3D TEXTURING VIA A RENDERING LOSS

    公开(公告)号:US20220122311A1

    公开(公告)日:2022-04-21

    申请号:US17166586

    申请日:2021-02-03

    Abstract: An electronic device and method for texturing a three dimensional (3D) model are provided. The method includes rendering a texture atlas to obtain a first set of two dimensional (2D) images of the 3D model; rendering a ground truth texture atlas to obtain a second set of 2D images of the 3D model; comparing the first set of images with the second set of images to determine a rendering loss; applying the texture sampling properties to a convolutional neural network (CNN) to incorporate the rendering loss into a deep learning framework; and inputting a 2D texture atlas into the CNN to generate a texture of the 3D module.

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