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    • 8. 发明授权
    • Methods and nodes for fast handover using pre-allocation of resources in target nodes
    • 用于快速切换的方法和节点,使用目标节点中的资源预分配
    • US09549351B2
    • 2017-01-17
    • US14423788
    • 2012-08-30
    • Stefan WanstedtMin Wang
    • Stefan WanstedtMin Wang
    • H04W36/00H04W24/10H04W72/04
    • H04W36/0077H04W24/10H04W36/0088H04W72/044
    • The present invention relates to a method in an RBS of a wireless network, for supporting HO of a UE in a served cell. The method comprises pre-allocating resources for HO of the UE to a target cell candidate. The pre-allocated resources comprise a dedicated preamble for the target cell candidate. The method also comprises receiving a measurement report from the UE triggering a HO and comprising a list of neighbor cells, determining if a cell in the list of neighbor cells corresponds to one of the at least one target cell candidates, and when they correspond transmitting a HO command to the UE comprising the dedicated preamble for the target. The present invention also relates to a corresponding method in the target RBS and to the serving and target RBS themselves.
    • 本发明涉及无线网络的RBS中的方法,用于支持服务小区中的UE的HO。 该方法包括将UE的HO的资源预分配给目标小区候选。 预先分配的资源包括用于目标小区候选的专用前导码。 该方法还包括接收来自UE的测量报告触发HO并且包括相邻小区的列表,确定相邻小区列表中的小区是否对应于至少一个目标小区候选中的一个,并且当它们对应于发送 HO命令,包括用于目标的专用前置码。 本发明还涉及目标RBS和服务和目标RBS本身中的相应​​方法。
    • 9. 发明授权
    • Incremental image clustering
    • 增量图像聚类
    • US09239967B2
    • 2016-01-19
    • US14234099
    • 2011-07-29
    • Ke-Yan LiuXin-Yun SunTong ZhangLei WangMin Wang
    • Ke-Yan LiuXin-Yun SunTong ZhangLei WangMin Wang
    • G06K9/68G06K9/62G06F17/30
    • G06K9/6218G06F17/30247G06K9/6219
    • Methods, systems, and computer readable media with executable instructions, and/or logic are provided for incremental image clustering. An example method for incremental image clustering can include identifying, via a computing device, a number of candidate nodes from among evaluated leaf image cluster (LIC) nodes on an image cluster tree (ICT) based on a similarity between a feature of a new image and an average feature of each of the evaluated LIC nodes. The evaluated nodes include at least one node along each path from a root node to either a leaf node or a node having a similarity exceeding a first threshold. A most-similar node can be determined, via the computing device, from among the number of candidate nodes. The new image can be inserted to a node associated with the determined most-similar node, via the computing device.
    • 提供了具有可执行指令和/或逻辑的方法,系统和计算机可读介质用于增量图像聚类。 用于增量图像聚类的示例性方法可以包括基于新图像的特征之间的相似性,经由计算设备识别来自图像簇树(ICT)上的评估叶图像簇(LIC)节点中的候选节点的数量 以及每个评估的LIC节点的平均特征。 评估的节点包括沿着从根节点到叶节点或具有超过第一阈值的相似度的节点的每个路径的至少一个节点。 可以通过计算设备从候选节点的数量中确定最相似的节点。 可以通过计算设备将新图像插入到与所确定的最相似的节点相关联的节点。