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    • 5. 发明申请
    • METHOD, BASE STATION AND RELAY NODE FOR UPLINK TRANSMISSION
    • 方法,基站和上行链路传输的继电器节点
    • US20130003650A1
    • 2013-01-03
    • US13635033
    • 2010-03-18
    • Feng HanWu ZhengXiaobing LengKaibin ZhangJimin Liu
    • Feng HanWu ZhengXiaobing LengKaibin ZhangJimin Liu
    • H04W72/12H04W72/14
    • H04W28/18H04B7/155H04W84/047
    • A method for uplink transmission is proposed in the present invention. The method comprises steps of: selecting, based on statistical parameters for uplink traffic of a relay node, an uplink transmission method from a set of candidate uplink transmission methods; informing the relay node of the selected uplink transmission method; and performing the uplink transmission by the relay node according to the selected uplink transmission method. A base station and a relay node for implementing the uplink transmission method are also proposed in the present invention. According to the present invention, the base station can dynamically select different uplink transmission methods based on the statistical parameters for uplink traffic of the relay node, and the relay node can perform the uplink transmission based on the selected uplink transmission method so as to achieve favorable delay performance and high resource usage efficiency.
    • 本发明提出了上行传输方法。 该方法包括以下步骤:根据一组候选上行链路传输方法,选择基于中继节点的上行流量的统计参数的上行链路传输方法; 向所述中继节点通知所选择的上行链路传输方法; 以及根据所选择的上行链路传输方法由中继节点执行上行链路传输。 在本发明中还提出了一种用于实现上行链路传输方法的基站和中继节点。 根据本发明,基站可以基于中继节点的上行业务的统计参数动态选择不同的上行传输方式,中继节点可以根据选择的上行传输方式进行上行传输,从而达到有利 延迟性能和高资源使用效率。
    • 6. 发明授权
    • Exemplar-based heterogeneous compositional method for object classification
    • 用于对象分类的基于示例的异构组合方法
    • US08233704B2
    • 2012-07-31
    • US12136138
    • 2008-06-10
    • Feng HanHui ChengJiangjian XiaoHarpreet Singh Sawhney
    • Feng HanHui ChengJiangjian XiaoHarpreet Singh Sawhney
    • G06K9/62G06E1/00G06E3/00G06F15/18G06G7/00
    • G06K9/3241G06K9/6256G06K9/6292
    • A method for automatically generating a strong classifier for determining whether at least one object is detected in at least one image is disclosed, comprising the steps of: (a) receiving a data set of training images having positive images; (b) randomly selecting a subset of positive images from the training images to create a set of candidate exemplars, wherein said positive images include at least one object of the same type as the object to be detected; (c) training a weak classifier based on at least one of the candidate exemplars, said training being based on at least one comparison of a plurality of heterogeneous compositional features located in the at least one image and corresponding heterogeneous compositional features in the one of set of candidate exemplars; (d) repeating steps (c) for each of the remaining candidate exemplars; and (e) combining the individual classifiers into a strong classifier, wherein the strong classifier is configured to determine the presence or absence in an image of the object to be detected.
    • 公开了一种用于自动生成强分类器以确定在至少一个图像中是否检测到至少一个对象的方法,包括以下步骤:(a)接收具有正图像的训练图像的数据集; (b)从所述训练图像中随机选择正图像的子集以创建一组候选样本,其中所述正图像包括与所述待检测对象相同类型的至少一个对象; (c)基于所述候选样本中的至少一个来训练弱分类器,所述训练基于位于所述至少一个图像中的多个异质成分特征和所述一个图像中的一个中的对应的异质组成特征的至少一个比较 候选人样本 (d)为每个其余的候选样本重复步骤(c); 以及(e)将各个分类器组合成强分类器,其中强分类器被配置为确定待检测对象的图像中的存在或不存在。
    • 9. 发明申请
    • EXEMPLAR-BASED HETEROGENEOUS COMPOSITIONAL METHOD FOR OBJECT CLASSIFICATION
    • 用于对象分类的基于EXEMPLAR的异构组合方法
    • US20080310737A1
    • 2008-12-18
    • US12136138
    • 2008-06-10
    • Feng HanHui ChengJiangjian XiaoHarpreet Singh Sawhney
    • Feng HanHui ChengJiangjian XiaoHarpreet Singh Sawhney
    • G06K9/62
    • G06K9/3241G06K9/6256G06K9/6292
    • A method for automatically generating a strong classifier for determining whether at least one object is detected in at least one image is disclosed, comprising the steps of: (a) receiving a data set of training images having positive images; (b) randomly selecting a subset of positive images from the training images to create a set of candidate exemplars, wherein said positive images include at least one object of the same type as the object to be detected; (c) training a weak classifier based on at least one of the candidate exemplars, said training being based on at least one comparison of a plurality of heterogeneous compositional features located in the at least one image and corresponding heterogeneous compositional features in the one of set of candidate exemplars; (d) repeating steps (c) for each of the remaining candidate exemplars; and (e) combining the individual classifiers into a strong classifier, wherein the strong classifier is configured to determine the presence or absence in an image of the object to be detected.
    • 公开了一种用于自动生成强分类器以确定在至少一个图像中是否检测到至少一个对象的方法,包括以下步骤:(a)接收具有正图像的训练图像的数据集; (b)从所述训练图像中随机选择正图像的子集以创建一组候选样本,其中所述正图像包括与所述待检测对象相同类型的至少一个对象; (c)基于所述候选样本中的至少一个训练弱分类器,所述训练基于位于所述至少一个图像中的多个异质成分特征和所述一个图像中的一个中的对应的异质组成特征的至少一个比较 候选人样本 (d)为每个其余的候选样本重复步骤(c); 以及(e)将各个分类器组合成强分类器,其中强分类器被配置为确定待检测对象的图像中的存在或不存在。