Pressure sensing device
    132.
    发明授权

    公开(公告)号:US09927313B2

    公开(公告)日:2018-03-27

    申请号:US15011456

    申请日:2016-01-29

    CPC classification number: G01L19/0084 G01L9/0044

    Abstract: A pressure sensing device is disclosed in the present disclosure. The pressure sensing device includes a bottom plate, a flexible shell and a MEMS pressure sensor. The flexible shell covers the bottom plate for forming a hermetical cavity, and the MEMS pressure sensor is accommodated in the hermetical cavity. Air in the hermetical cavity is compressed when the flexible shell is pressed, the MEMS pressure sensor is configured for detecting variation of an air pressure within the hermetical cavity when the flexible shell is pressed, and convert the variation of the air pressure into an electric signal.

    Efficient system and method for body part detection and tracking
    136.
    发明授权
    Efficient system and method for body part detection and tracking 有权
    有效的身体部位检测和跟踪系统和方法

    公开(公告)号:US08903132B2

    公开(公告)日:2014-12-02

    申请号:US13610225

    申请日:2012-09-11

    CPC classification number: G06K9/00362 G06K9/00261

    Abstract: A method is provided for detecting a body part in a video stream from a mobile device. A video stream of a human subject is received from a camera connected to the mobile device. The video stream has frames. A first frame of the video stream is identified for processing. This first frame is then partitioned into observation windows, each observation window having pixels. In each observation window, non-skin-toned pixels are eliminated; and the remaining pixels are compared to determine a degree of entropy of the pixels in the observation window. In any observation window having a degree of entropy above a predetermined threshold, a bounded area is made around the region of high entropy pixels. The consistency of the entropy is analyzed in the bounded area. If the bounded area has inconsistently high entropy, a body part is determined to be detected at that bounded area.

    Abstract translation: 提供一种用于检测来自移动设备的视频流中的身体部位的方法。 从连接到移动设备的相机接收人类对象的视频流。 视频流具有帧。 视频流的第一帧被识别用于处理。 然后将该第一帧分割成观察窗,每个观察窗具有像素。 在每个观察窗口中,消除非皮肤色调像素; 并且比较剩余像素以确定观察窗中像素的熵程度。 在具有高于预定阈值的熵程度的任何观察窗中,围绕高熵像素的区域进行有界区域。 在有界区域分析熵的一致性。 如果有界区域具有不一致的高熵,则确定在该有界区域处检测到身体部位。

    Method and system of simulating magnetic resonance imaging signals
    137.
    发明授权
    Method and system of simulating magnetic resonance imaging signals 失效
    模拟磁共振成像信号的方法和系统

    公开(公告)号:US08781554B2

    公开(公告)日:2014-07-15

    申请号:US13116155

    申请日:2011-05-26

    CPC classification number: G01R33/543 A61B5/055 G01R33/50 G01R33/546 G01R33/58

    Abstract: A method for simulating magnetic resonance signals is proposed. A lattice array where each point in the array has several magnetic resonance sensitive particles is provided. Statistic property of each point is set. A raw magnetic resonance imaging data is calculated based on statistic property of each point and a magnetic resonance imaging sequence to be applied. A system for simulating magnetic resonance signals is further proposed. By considering statistic property of each point, it can distinguish every part of the object to be scanned and really reflect the structure of object without using a real magnetic resonance imaging device. It saves time and costs for avoiding several scanning by the real a magnetic resonance imaging device.

    Abstract translation: 提出了一种模拟磁共振信号的方法。 提供阵列中每个点具有几个磁共振敏感粒子的晶格阵列。 设置每个点的统计属性。 基于每个点的统计特性和要应用的磁共振成像序列计算原始磁共振成像数据。 进一步提出了一种用于模拟磁共振信号的系统。 通过考虑每个点的统计特性,它可以区分待扫描对象的每个部分,并且真正反映物体的结构,而不使用真正的磁共振成像装置。 它可以节省时间和成本,避免真正的磁共振成像设备进行多次扫描。

    AUTOMATICALLY TRIGGERING PREDICTIONS IN RECOMMENDATION SYSTEMS BASED ON AN ACTIVITY-PROBABILITY THRESHOLD
    139.
    发明申请
    AUTOMATICALLY TRIGGERING PREDICTIONS IN RECOMMENDATION SYSTEMS BASED ON AN ACTIVITY-PROBABILITY THRESHOLD 有权
    基于活动可靠性阈值的建议系统自动触发预测

    公开(公告)号:US20130218825A1

    公开(公告)日:2013-08-22

    申请号:US13402751

    申请日:2012-02-22

    CPC classification number: G06Q10/04 G06Q30/02

    Abstract: A recommender system determines a probability threshold for an activity-prediction model, and uses the probability threshold to predict whether a user is performing a target activity. To determine the probability threshold, the system computes a set of activity probabilities based on contextual information for a set of historical activities, and based on an activity-prediction model for a target activity. The system then compares a set of probability thresholds with the set of activity probabilities to determine a prediction success rate for each probability threshold. The system computes a utility score for each probability threshold based on the prediction success rates and a utility function, and selects a probability threshold whose utility score is optimal amongst the utility scores of the set of thresholds and greater than or equal to a baseline utility score. The system then assigns the probability threshold to the activity-prediction model.

    Abstract translation: 推荐系统确定活动预测模型的概率阈值,并使用概率阈值来预测用户是否正在执行目标活动。 为了确定概率阈值,系统基于一组历史活动的上下文信息,并且基于目标活动的活动预测模型来计算一组活动概率。 然后,系统将一组概率阈值与活动概率集合进行比较,以确定每个概率阈值的预测成功率。 该系统基于预测成功率和效用函数来计算每个概率阈值的效用得分,并且选择一个概率阈值,其效用评分在阈值集合的效用评分之间是最佳的,并且大于或等于基准效用得分 。 系统然后将概率阈值分配给活动预测模型。

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