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    • 85. 发明申请
    • UAV DECISION AND CONTROL SYSTEM
    • 无人机决策与控制系统
    • WO2007080584A2
    • 2007-07-19
    • PCT/IL2007000038
    • 2007-01-10
    • UNIV CARMEL HAIFA ECONOMIC CORTECHNION RES & DEV FOUNDATIONBEN ASHER YOSEFFELDMAN SHARONIGURFIL PINCHAS
    • BEN ASHER YOSEFFELDMAN SHARONIGURFIL PINCHAS
    • G06F19/00
    • G05D1/104G05D1/0088
    • The present invention relates to a hierarchical system and method for task assignment (TA), coordination and communication of multiple Unmanned Aerial Vehicles (UAV's) engaging multiple attack targets and conceives an ad-hoc routing algorithm for synchronization of target lists utilizing a distributed computing topology. Assuming limited communication bandwidth and range, coordination of UAV motion is achieved by implementing a simple behavioral flocking algorithm utilizing a tree topology for target list routing. The TA algorithm is based on a graph-theoretic approach, in which a node locates all the detectable targets, identifies them and computes its distance to each target. The node then produces an attack plan that minimizes the sum of distances of the UAV's in the subtree of a given node to the targets.
    • 本发明涉及一种用于任务分配(TA)的分层系统和方法,涉及多个攻击目标的多个无人机(UAV)的协调和通信,并且构思了利用分布式计算拓扑来同步目标列表的自组织路由算法 。 假设有限的通信带宽和范围,UAV运动的协调是通过使用用于目标列表路由的树形拓扑的简单行为植入算法来实现的。 TA算法基于图理论方法,其中节点定位所有可检测的目标,识别它们并计算其到每个目标的距离。 该节点然后产生一个攻击计划,最小化给定节点的子任务中的UAV在目标上的距离之和。
    • 86. 发明申请
    • LOW POWER HARDWARE ALGORITHMS AND ARCHITECTURES FOR SPIKE SORTING AND DETECTION
    • 低功耗硬件算法和SPIKE分类和检测的架构
    • WO2006003662A3
    • 2007-01-25
    • PCT/IL2005000717
    • 2005-07-06
    • TECHNION RES & DEV FOUNDATIONGINOSAR RANPERELMAN YEVGENYZVIAGINTSEV ALEX
    • GINOSAR RANPERELMAN YEVGENYZVIAGINTSEV ALEX
    • A61B5/05A61B5/04
    • G06K9/00543
    • A neuronal recording system featuring a large number of electrodes and a portable wireless front-end integrated circuit for signal processing for low-power spike detection and alignment. The system is configured as a Neuroprocessor and introduces hardware architectures for automatic spike detection and alignment algorithms. The Neuroprocessor can be placed next to the recording electrodes and provide for all stages of spike processing, stimulating neuronal tissues and wireless communications to a host computer. Some of the algorithms are based on principal component analysis(PCA). Others employ a novel Integral Transform. The algorithms execute autonomously, but require off-line training and setting of computational parameters. Pre-recorded neuronal signals evaluate the accuracy of the proposed algorithms and architectures: The recorded data are processed by a standard PCA spike sorting software algorithm, as well as by the several hardware algorithms, and the outcomes are compared.
    • 具有大量电极的神经元记录系统和用于低功率尖峰检测和对准的信号处理的便携式无线前端集成电路。 该系统被配置为神经处理器,并引入用于自动尖峰检测和对准算法的硬件体系结构。 神经处理器可以放置在记录电极旁边,并提供所有阶段的尖峰处理,刺激神经元组织和与主机的无线通信。 一些算法基于主成分分析(PCA)。 其他人采用了一个新颖的积分变换。 算法自主执行,但需要离线训练和计算参数设置。 预先记录的神经元信号评估所提出的算法和架构的准确性:记录的数据由标准PCA尖峰分类软件算法以及几种硬件算法进行处理,并且比较结果。