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    • 54. 发明申请
    • Cost-aware networking over heterogeneous data channels
    • 异构数据通道的成本感知网络
    • US20070171915A1
    • 2007-07-26
    • US11342147
    • 2006-01-26
    • Kentaro ToyamaRohan MurtyChandramohan ThekkathRanveer Chandra
    • Kentaro ToyamaRohan MurtyChandramohan ThekkathRanveer Chandra
    • H04L12/56
    • H04L47/762H04L47/15H04L47/70H04L47/788H04L47/826
    • Disclosed herein are scheduling techniques for transmitting time-critical data in a cost-aware manner over a network comprising a plurality of heterogeneous transmission interfaces. The scheduling problem is formulated as a linear programming problem with the deliver-by deadlines of the various data blocks as hard constraints and minimizing cost set as an objective (soft) constraint. The problem is simplified by assuming data blocks with the earliest deadlines should be scheduled first and the most aggressive interfaces should be used first. To formulate the linear programming problem, the time domain is divided into bins and various bin-level schedules are enumerated for switching the transmission of the data over various transmission interfaces. The linear programming techniques are applied to the various bin configurations and the least costly of the resulting transmission schedule is selected for submission to a switching layer.
    • 这里公开了用于通过包括多个异构传输接口的网络以成本感知方式传送时间关键数据的调度技术。 调度问题被形成为作为硬约束的各种数据块的交付期限的线性规划问题,并且将成本设定为目标(软)约束的最小化。 通过假设最早的最后期限的数据块应该首先被调度并且应该首先使用最积极的接口来简化问题。 为了制定线性规划问题,将时域划分成箱体,并列举各种二进制计划,用于切换各种传输接口上的数据传输。 线性编程技术被应用于各种箱体配置,并且选择最终的传输调度成本最低以提交给切换层。
    • 60. 发明申请
    • Probabilistic exemplar-based pattern tracking
    • 基于概率模式的模式跟踪
    • US20060093188A1
    • 2006-05-04
    • US11298798
    • 2005-12-09
    • Andrew BlakeKentaro Toyama
    • Andrew BlakeKentaro Toyama
    • G06K9/00G06K9/62
    • G06K9/6255G06K2009/3291G06T7/277
    • The present invention involves a new system and method for probabilistic exemplar-based tracking of patterns or objects. Tracking is accomplished by first extracting a set of exemplars from training data. The exemplars are then clustered using conventional statistical techniques. Such clustering techniques include k-medoids clustering which is based on a distance function for determining the distance or similarity between the exemplars. A dimensionality for each exemplar cluster is then estimated and used for generating a probabilistic likelihood function for each exemplar cluster. Any of a number of conventional tracking algorithms is then used in combination with the exemplars and the probabilistic likelihood functions for tracking patterns or objects in a sequence of images, or in a space, or frequency domain.
    • 本发明涉及一种用于模式或对象的概率示例性跟踪的新系统和方法。 通过首先从训练数据提取一组样本来实现跟踪。 然后使用常规统计技术将样本聚类。 这种聚类技术包括基于用于确定样本之间的距离或相似性的距离函数的k-聚类聚类。 然后估计每个样本簇的维数,并用于为每个样本簇生成概率似然函数。 然后将许多常规跟踪算法中的任何一种与用于跟踪图像序列中的图案或物体的样本和概率似然函数组合使用,或者在空间或频域中使用。