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    • 12. 发明申请
    • WIRELESS-BASED POSITIONING ADJUSTMENTS USING A MOTION SENSOR
    • 使用运动传感器的基于无线的定位调整
    • WO2010059935A9
    • 2011-06-09
    • PCT/US2009065322
    • 2009-11-20
    • QUALCOMM INCSRIDHARA VINAYAGGARWAL ALOKNAGUIB AYMAN FAWZY
    • SRIDHARA VINAYAGGARWAL ALOKNAGUIB AYMAN FAWZY
    • G01C21/36G01S5/14
    • G01S5/10G01S5/021G01S7/40G01S13/76G01S19/49H04W4/027
    • Apparatuses and methods for adjusting wireless-derived positions of a mobile station using a motion sensor are presented. One method includes estimating a position of a mobile station based upon wireless signal measurements and measuring a movement of the mobile station using a relative motion sensor. The method further includes detecting a displacement of the mobile station based upon the measured movement, determining that the displacement is below a threshold and then adjusting the estimated position of the mobile station using information from the relative motion sensor. An apparatus includes a wireless transceiver, a relative motion sensor, a processor coupled to the wireless transceiver and the relative motion sensor, and a memory coupled to the processor. The memory stores executable instructions and data for causing the processor to execute methods for adjusting wireless-derived positions using a motion sensor.
    • 提出了使用运动传感器来调整移动台的无线来源位置的装置和方法。 一种方法包括基于无线信号测量来估计移动站的位置,并使用相对运动传感器测量移动站的移动。 该方法还包括基于测量的移动来检测移动站的位移,确定位移低于阈值,然后使用来自相对运动传感器的信息来调整移动站的估计位置。 一种装置包括无线收发器,相对运动传感器,耦合到无线收发器和相对运动传感器的处理器以及耦合到处理器的存储器。 存储器存储用于使处理器执行使用运动传感器来调整无线来源位置的方法的可执行指令和数据。
    • 13. 发明申请
    • WIRELESS POSITION DETERMINATION USING ADJUSTED ROUND TRIP TIME MEASUREMENTS
    • 使用调整回合时间测量的无线位置确定
    • WO2010059934A3
    • 2010-08-12
    • PCT/US2009065319
    • 2009-11-20
    • QUALCOMM INCAGGARWAL ALOKNAGUIB AYMAN FAWZYSRIDHARA VINAYDAS SAUMITRA MOHAN
    • AGGARWAL ALOKNAGUIB AYMAN FAWZYSRIDHARA VINAYDAS SAUMITRA MOHAN
    • G01S5/02H04L12/28H04W64/00
    • G01S5/10G01S5/0205G01S5/14G01S13/765H04W24/00H04W64/00
    • One method for wirelessly determining a position of a mobile station includes measuring a round trip time (RTT) to a plurality of wireless access points, estimating a first distance to each wireless access point based upon the round trip time delay and an initial processing time associated with each wireless access point, estimating a second distance to each wireless access point based upon supplemental information, combining the first and second distance estimates to each wireless access point, and calculating the position based upon the combined distance estimates. Another method includes measuring a distance to each wireless access point based upon a wireless signal model, calculating a position of the mobile station based upon the measured distance, determining a computed distance to each wireless access point based upon the calculated position of the mobile station, updating the wireless signal model, and determining whether the wireless signal model has converged.
    • 用于无线地确定移动站的位置的一种方法包括测量到多个无线接入点的往返时间(RTT),基于往返时间延迟估计到每个无线接入点的第一距离和相关联的初始处理时间 利用每个无线接入点,基于补充信息估计到每个无线接入点的第二距离,将第一和第二距离估计合并到每个无线接入点,以及基于组合的距离估计来计算位置。 另一种方法包括基于无线信号模型来测量到每个无线接入点的距离,基于测量的距离来计算移动台的位置,基于计算出的移动台的位置确定到每个无线接入点的计算距离, 更新无线信号模型,以及确定无线信号模型是否收敛。
    • 16. 发明申请
    • METHODS AND SYSTEMS OF USING BOOSTED DECISION STUMPS AND JOINT FEATURE SELECTION AND CULLING ALGORITHMS FOR THE EFFICIENT CLASSIFICATION OF MOBILE DEVICE BEHAVIORS
    • 使用增强决策库的方法和系统以及联合功能选择和获取算法来有效地分类移动设备行为
    • WO2014107439A2
    • 2014-07-10
    • PCT/US2013078352
    • 2013-12-30
    • QUALCOMM INC
    • FAWAZ KASSEMSRIDHARA VINAYGUPTA RAJARSHI
    • G06N5/04
    • G06N5/043G06N5/025
    • Methods and systems for classifying mobile device behavior include configuring a server use a large corpus of mobile device behaviors to generate a full classifier model that includes a finite state machine suitable for conversion into boosted decision stumps and/or which describes all or many of the features relevant to determining whether a mobile device behavior is benign or contributing to the mobile device's degradation over time. A mobile device may receive the full classifier model and use the model to generate a full set of boosted decision stumps from which a more focused or lean classifier model is generated by culling the full set to a subset suitable for efficiently determining whether mobile device behavior are benign. Boosted decision stumps may be culled by selecting all boosted decision stumps that depend upon a limited set of test conditions.
    • 用于分类移动设备行为的方法和系统包括配置服务器使用大的移动设备行为语料库来生成包括适合于转换为增强的决策树桩的有限状态机和/或描述所有或许多特征的完整分类器模型 与确定移动设备行为是否良好或对移动设备随着时间的退化有所贡献相关。 移动设备可以接收完整的分类器模型并且使用该模型来产生一整套增强的决策树桩,通过将整个集合剔除,从而可以将整个集合或精益分类器模型剔除,从而从整个集合或精益分类模型生成适合于有效地确定移动设备行为 良性。 通过选择依赖于有限的测试条件的所有提升的决策树桩,可以剔除增强的决策树桩。
    • 17. 发明申请
    • METHODS AND SYSTEMS OF DYNAMICALLY GENERATING AND USING DEVICE-SPECIFIC AND DEVICE-STATE-SPECIFIC CLASSIFIER MODELS FOR THE EFFICIENT CLASSIFICATION OF MOBILE DEVICE BEHAVIORS
    • 动态生成和使用特定于器件的器件状态特定的分类器模型的方法和系统,用于有效分类移动设备行为
    • WO2014107438A2
    • 2014-07-10
    • PCT/US2013078350
    • 2013-12-30
    • QUALCOMM INC
    • SRIDHARA VINAYGUPTA RAJARSHIFAWAZ KASSEM
    • G06N5/04
    • H04B17/00G06N5/043H04B17/391
    • The various aspects provide a mobile device and methods implemented on the mobile device for modifying behavior models to account for device-specific or device-state-specific features. In the various aspects, a behavior analyzer module may leverage a full feature set of behavior models (i.e. a large classifier model) received from a network server to create lean classifier models for use in monitoring for malicious behavior on the mobile device, and the behavior analyzer module may dynamically modify these lean classifier models to include features specific to the mobile device and/or the mobile device's current configuration. Thus, the various aspects may enhance overall security for a particular mobile device by taking the mobile device and its current configuration into account and may improve overall performance by monitoring only features that are relevant to the mobile device.
    • 各个方面提供了在移动设备上实现的用于修改行为模型以考虑设备特定或设备状态特定特征的移动设备和方法。 在各个方面中,行为分析器模块可以利用从网络服务器接收的行为模型的全特征集合(即,大分类器模型)来创建用于监视移动设备上的恶意行为的贫分类器模型,并且行为 分析器模块可以动态地修改这些瘦分类器模型以包括特定于移动设备和/或移动设备的当前配置的特征。 因此,通过考虑移动设备及其当前配置,各个方面可以增强特定移动设备的整体安全性,并且可以通过仅监视与移动设备相关的特征来改善整体性能。