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Patent Agency Ranking
公开(公告)号：WO2018164969A1
公开(公告)日：2018-09-13
申请号：PCT/US2018/020774
申请日：2018-03-02
Applicant: RAYTHEON COMPANY
Inventor： KADAMBE, Shubha , SPARIOSU, Kalin , SOTELO, Juan C.
Abstract: An optical system for capturing an image using compressive sensing includes a digital micromirror device (DMD) array; an optical lens system; a first optical detector array; a first optical channel for projecting spatial information onto the first detector array; a second optical detector array; a second optical channel; a spectral filter and a polarization filter for projecting spectral and polarization information onto the second detector array; and an image processor to control the DMD array to generate a first and a second set of samples of the image using a sampling rate lower than required by the Shannon-Nyquist sampling theorem, and to reconstruct the image from the samples collected and digitized by the first and second optical detector arrays.
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公开(公告)号：WO2016137531A1
公开(公告)日：2016-09-01
申请号：PCT/US2015/050371
申请日：2015-09-16
Applicant: RAYTHEON COMPANY
Inventor： KADAMBE, Shubha , SLEPICKA, Jason , WRIGHT, Benjamin T. , PHAN, Kim A.
CPC classification number: H04L63/1416 , G06N7/005 , H04L63/1441
Abstract: A system creates a dynamic stochastic network using data relating to events. The dynamic stochastic network includes super nodes, local nodes, and agents. Connections among the super nodes and local nodes include events that are related to the super nodes and the local nodes. Strengths of the connections between the super nodes and local nodes are a function of a number of events that are common to the super nodes and local nodes. The connections are made and broken as the agents interact over time. The strengths of the connections increase and decrease as a function of a change in the number of events that the super nodes and local nodes have in common. An instability metric is calculated for the dynamic stochastic network, and an emerging group threat behavior is detected based on a deviation from the instability metric.
Abstract translation: 系统使用与事件相关的数据创建动态随机网络。 动态随机网络包括超节点，本地节点和代理。 超级节点和本地节点之间的连接包括与超级节点和本地节点相关的事件。 超级节点和本地节点之间的连接优势是超级节点和本地节点共有的事件数量的函数。 当代理人随时间进行交互时，连接被建立和断开。 连接的优点随着超级节点和本地节点共同的事件数量的变化而增加和减少。 针对动态随机网络计算不稳定度量，并且基于偏离不稳定性度量来检测出现的组威胁行为。
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公开(公告)号：WO2008118706A1
公开(公告)日：2008-10-02
申请号：PCT/US2008/057423
申请日：2008-03-19
Applicant: RAYTHEON COMPANY , KADAMBE, Shubha , SAISAN, Payam
Inventor： KADAMBE, Shubha , SAISAN, Payam
CPC classification number: G06K9/48 , G01S15/8902 , G06K9/6247
Abstract: In one aspect, a method to identify a candidate object includes receiving an image of the candidate object and projecting the received image onto an image subspace. The image subspace is formed from images of known objects of a class. The method also includes determining whether the candidate object is in the object class based on the received image and the image subspace using a likelihood ratio. The likelihood ratio includes a first probability density indicating a probability an object is in the object class and a second probability density indicating a probability an object is not in the class. The first probability density and the second probability are each a function of a distance of the received image to the image subspace.
Abstract translation: 一方面，一种识别候选对象的方法包括接收候选对象的图像并将接收的图像投影到图像子空间上。 图像子空间由类的已知对象的图像形成。 该方法还包括基于接收到的图像和使用似然比的图像子空间来确定候选对象是否在对象类中。 似然比包括指示对象在对象类中的概率的第一概率密度和指示对象不在类中的概率的第二概率密度。 第一概率密度和第二概率各自是接收到的图像与图像子空间的距离的函数。
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公开(公告)号：EP3593527A1
公开(公告)日：2020-01-15
申请号：EP18712058.9
申请日：2018-03-02
Applicant: Raytheon Company
Inventor： KADAMBE, Shubha , SPARIOSU, Kalin , SOTELO, Juan C.
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公开(公告)号：EP3262530A1
公开(公告)日：2018-01-03
申请号：EP15849813.9
申请日：2015-09-16
Applicant: Raytheon Company
Inventor： KADAMBE, Shubha , SLEPICKA, Jason , WRIGHT, Benjamin T. , PHAN, Kim A.
CPC classification number: H04L63/1416 , G06N7/005 , H04L63/1441
Abstract: A system creates a dynamic stochastic network using data relating to events. The dynamic stochastic network includes super nodes, local nodes, and agents. Connections among the super nodes and local nodes include events that are related to the super nodes and the local nodes. Strengths of the connections between the super nodes and local nodes are a function of a number of events that are common to the super nodes and local nodes. The connections are made and broken as the agents interact over time. The strengths of the connections increase and decrease as a function of a change in the number of events that the super nodes and local nodes have in common. An instability metric is calculated for the dynamic stochastic network, and an emerging group threat behavior is detected based on a deviation from the instability metric.
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