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    • 26. 发明授权
    • Transmitter diversity technique for wireless communications
    • 无线通信发射机分集技术
    • US07916806B2
    • 2011-03-29
    • US11828790
    • 2007-07-26
    • Siavash AlamoutiVahid Tarokh
    • Siavash AlamoutiVahid Tarokh
    • H04L27/00
    • H04B7/0857H04B1/02H04B7/0613H04B7/0615H04B7/0667H04B7/0669H04B7/0671H04B7/0689H04B7/0837H04B7/0845H04B7/0854H04B7/0891H04B7/12H04L1/0054H04L1/04H04L1/0606H04L1/0618H04L1/0625H04L1/0631H04L1/0643H04L1/0668H04L1/08H04L25/0202
    • A simple block coding arrangement is created with symbols transmitted over a plurality of transmit channels, in connection with coding that comprises only simple arithmetic operations, such as negation and conjugation. The diversity created by the transmitter utilizes space diversity and either time or frequency diversity. Space diversity is effected by redundantly transmitting over a plurality of antennas, time diversity is effected by redundantly transmitting at different times, and frequency diversity is effected by redundantly transmitting at different frequencies: Illustratively, using two transmit antennas and a single receive antenna, one of the disclosed embodiments provides the same diversity gain as the maximal-ratio receiver combining (MRRC) scheme with one transmit antenna and two receive antennas. The principles of this invention are applicable to arrangements with more than two antennas, and an illustrative embodiment is disclosed using the same space block code with two transmit and two receive antennas.
    • 结合仅包括诸如否定和共轭的简单算术运算的编码,创建具有通过多个发送信道发送的符号的简单块编码布置。 发射机产生的分集利用空间分集和时间或频率分集。 通过在多个天线上进行冗余发送来实现空间分集,通过在不同时间进行冗余发送实现时间分集,并且通过以不同频率进行冗余传输来实现频率分集:说明性地,使用两个发射天线和单个接收天线 所公开的实施例提供与最大比率接收机组合(MRRC)方案与一个发射天线和两个接收天线相同的分集增益。 本发明的原理可应用于具有两个以上天线的布置,并且使用具有两个发射天线和两个接收天线的相同空间块码公开了一个说明性实施例。
    • 29. 发明授权
    • Method and system for fuzzy clustering of images
    • 图像模糊聚类的方法和系统
    • US07460717B2
    • 2008-12-02
    • US11838668
    • 2007-08-14
    • Hamid JafarkhaniVahid Tarokh
    • Hamid JafarkhaniVahid Tarokh
    • G06K9/62G06F15/18G06F7/00G06F17/00
    • G06K9/6224Y10S707/99945Y10S707/99948
    • An approach to clustering a set of images based on similarity measures employs a fuzzy clustering paradigm in which each image is represented by a node in a graph. The graph is ultimately partitioned into subgraphs, each of which represent true clusters among which the various images are distributed. The partitioning is performed in a series of stages by identifying one true cluster at each stage, and removing the nodes belonging to each identified true cluster from further consideration so that the remaining, unclustered nodes may then be grouped. At the beginning of each such stage, the nodes that remain to be clustered are treated as all belonging to a single candidate cluster. Nodes are removed from this single candidate cluster in accordance with similarity and connectivity criteria, to arrive at a true cluster. The member nodes of this true cluster are then removed from further consideration, prior to the next stage in the process.
    • 基于相似性度量对一组图像进行聚类的方法采用模糊聚类范例,其中每个图像由图中的节点表示。 图形最终被划分为子图,每个子图表示其中分布了各种图像的真实集群。 通过在每个阶段识别一个真实集群,并从进一步的考虑中去除属于每个已识别的真实集群的节点,从而可以对剩余的未分组节点进行分组,从而在一系列阶段执行分区。 在每个这样的阶段的开始,保持被聚集的节点被视为全部属于单个候选集群。 根据相似性和连通性标准,从该单个候选集群中删除节点,以获得真正的集群。 然后,在此过程的下一阶段之前,将此真正集群的成员节点从进一步的考虑中移除。
    • 30. 发明申请
    • METHOD AND SYSTEM FOR FUZZY CLUSTERING OF IMAGES
    • 图像融合的方法与系统
    • US20070274597A1
    • 2007-11-29
    • US11838668
    • 2007-08-14
    • HAMID JAFARKHANIVahid Tarokh
    • HAMID JAFARKHANIVahid Tarokh
    • G06K9/64
    • G06K9/6224Y10S707/99945Y10S707/99948
    • An approach to clustering a set of images based on similarity measures employs a fuzzy clustering paradigm in which each image is represented by a node in a graph. The graph is ultimately partitioned into subgraphs, each of which represent true clusters among which the various images are distributed. The partitioning is performed in a series of stages by identifying one true cluster at each stage, and removing the nodes belonging to each identified true cluster from further consideration so that the remaining, unclustered nodes may then be grouped. At the beginning of each such stage, the nodes that remain to be clustered are treated as all belonging to a single candidate cluster. Nodes are removed from this single candidate cluster in accordance with similarity and connectivity criteria, to arrive at a true cluster. The member nodes of this true cluster are then removed from further consideration, prior to the next stage in the process.
    • 基于相似性度量对一组图像进行聚类的方法采用模糊聚类范例,其中每个图像由图中的节点表示。 图形最终被划分为子图,每个子图表示其中分布了各种图像的真实集群。 通过在每个阶段识别一个真实集群,并从进一步的考虑中去除属于每个已识别的真实集群的节点,从而可以对剩余的未分组节点进行分组,从而在一系列阶段执行分区。 在每个这样的阶段的开始,保持被聚集的节点被视为全部属于单个候选集群。 根据相似性和连通性标准,从该单个候选集群中删除节点,以获得真正的集群。 然后,在此过程的下一阶段之前,将此真正集群的成员节点从进一步的考虑中移除。