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1.发明授权Method, system, and device for dynamic energy efficient job scheduling in a cloud computing environment 有权用于云计算环境中动态节能作业调度的方法，系统和设备公开(公告)号：US09342376B2
公开(公告)日：2016-05-17
申请号：US13534324
申请日：2012-06-27
CPC分类号： G06F9/5094 , G06F1/206 , G06F1/3203 , G06F9/465 , G06F9/5027 , H04L41/12 , Y02D10/22
摘要： A method, system, and device for energy efficient job scheduling in a datacenter computing environment includes a master node. The master node can periodically receive energy data from slave nodes and dynamically assign computing tasks to be executed by the slave nodes based on the energy data.
摘要翻译： 在数据中心计算环境中用于能量效率作业调度的方法，系统和设备包括主节点。 主节点可以周期性地从从节点接收能量数据，并且基于能量数据动态地分配由从节点执行的计算任务。
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2.发明申请METHOD, SYSTEM, AND DEVICE FOR DYNAMIC ENERGY EFFICIENT JOB SCHEDULING IN A CLOUD COMPUTING ENVIRONMENT 有权云计算环境中动态能源有效工作调度的方法，系统和设备公开(公告)号：US20140006534A1
公开(公告)日：2014-01-02
申请号：US13534324
申请日：2012-06-27
IPC分类号： G06F15/16
CPC分类号： G06F9/5094 , G06F1/206 , G06F1/3203 , G06F9/465 , G06F9/5027 , H04L41/12 , Y02D10/22
摘要： A method, system, and device for energy efficient job scheduling in a datacenter computing environment includes a master node. The master node can periodically receive energy data from slave nodes and dynamically assign computing tasks to be executed by the slave nodes based on the energy data.
摘要翻译： 在数据中心计算环境中用于能量效率作业调度的方法，系统和设备包括主节点。 主节点可以周期性地从从节点接收能量数据，并且基于能量数据动态地分配由从节点执行的计算任务。
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公开(公告)号：US20170371417A1
公开(公告)日：2017-12-28
申请号：US15195604
申请日：2016-06-28
申请人： Darshan Iyer , Nilesh K. Jain , Zhiqiang Liang
发明人： Darshan Iyer , Nilesh K. Jain , Zhiqiang Liang
IPC分类号： G06F3/01 , G06F3/0346 , G06K9/00 , G06K9/62
CPC分类号： G06F3/017 , G06F3/0346 , G06K9/00335 , G06K9/6218
摘要： Technologies for gesture recognition using downsampling are disclosed. A gesture recognition device may capture gesture data from a gesture measurement device, and downsample the captured data to a predefined number of data points. The gesture recognition device may then perform gesture recognition on the downsampled gesture data to recognize a gesture, and then perform an action based on the recognized gesture. The number of data points to which to downsample may be determined by downsampling to several different numbers of data points and comparing the performance of a gesture recognition algorithm performed on the downsampled gesture data for each different number of data points.
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公开(公告)号：US20170091657A1
公开(公告)日：2017-03-30
申请号：US14866895
申请日：2015-09-26
申请人： Luis S. Kida , Nilesh K. Jain , Darshan Iyer , Ebrahim Al Safadi
发明人： Luis S. Kida , Nilesh K. Jain , Darshan Iyer , Ebrahim Al Safadi
IPC分类号： G06N99/00
CPC分类号： G06N20/00
摘要： Technologies for platform-targeted machine learning include a computing device to generate a machine learning algorithm model indicative of a plurality of classes between which a user input is to be classified and translate the machine learning algorithm model into hardware code for execution on the target platform. The user input is to be classified as being associated with a particular class based on an application of one or more features to the user input, and each of the one or more features has an associated implementation cost indicative of a cost to perform on a target platform on which the corresponding feature is to be applied to the user input.
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