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    • 1. 发明授权
    • Apparatus, system, and method for object detection and identification
    • 用于物体检测和识别的装置,系统和方法
    • US08527445B2
    • 2013-09-03
    • US12959207
    • 2010-12-02
    • James P. KarinsStuart A. Mills
    • James P. KarinsStuart A. Mills
    • G06N5/02
    • G06N7/005
    • An apparatus, system, and method are disclosed for identifying a target object. An object detection module detects objects by matching data from one or more sensors to known data of a target object and determining one or more correlation metrics for each object. An object tracking module tracks geographic locations for detected objects over time using subsequent data from the one or more sensors. A contextual data module determines one or more contextual indicators for detected objects based on the data from the one or more sensors. An artificial intelligence module estimates probabilities that detected objects comprise the target object based on the correlation metrics, the geographic locations, the contextual indicators, and one or more target contextual indicators associated with the target object. The artificial intelligence module estimates the probabilities using an artificial intelligence model, such as a Bayesian network.
    • 公开了用于识别目标对象的装置,系统和方法。 对象检测模块通过将来自一个或多个传感器的数据与目标对象的已知数据进行匹配来检测对象,并确定每个对象的一个​​或多个相关度量。 对象跟踪模块使用来自一个或多个传感器的后续数据随时间跟踪检测到的对象的地理位置。 上下文数据模块基于来自一个或多个传感器的数据确定检测到的对象的一个​​或多个上下文指示符。 人造智能模块基于相关度量,地理位置,上下文指示符以及与目标对象相关联的一个或多个目标上下文指示来估计检测到的对象包括目标对象的概率。 人工智能模块使用诸如贝叶斯网络的人造智能模型来估计概率。
    • 2. 发明授权
    • Multispectral or hyperspectral imaging system and method for tactical reconnaissance
    • 多光谱或高光谱成像系统和战术侦察方法
    • US06831688B2
    • 2004-12-14
    • US10118534
    • 2002-04-08
    • Andre G. LareauBradford SpeerWilliam R. PfisterStephen R. BeranBarry SwartzJames P. KarinsRobert J. LintellChristopher P. Warren
    • Andre G. LareauBradford SpeerWilliam R. PfisterStephen R. BeranBarry SwartzJames P. KarinsRobert J. LintellChristopher P. Warren
    • H04N5335
    • H04N5/37206G01J3/02G01J3/0256G01J3/2803G01J3/2823G01J3/51H04N5/353H04N5/3743
    • A two-dimensional focal plane array (FPA) is divided into sub-arrays of rows and columns of pixels, each sub-array being responsive to light energy from a target object which has been separated by a spectral filter or other spectrum dividing element into a predetermined number of spectral bands. There is preferably one sub-array on the FPA for each predetermined spectral band. Each sub-array has its own read out channel to allow parallel and simultaneous readout of all sub-arrays of the array. The scene is scanned onto the array for simultaneous imaging of the terrain in many spectral bands. Time Delay and Integrate (TDI) techniques are used as a clocking mechanism within the sub-arrays to increase the signal to noise ratio (SNR) of the detected image. Additionally, the TDI length (i.e., number of rows of integration during the exposure) within each sub-array is adjustable to optimize and normalize the response of the photosensitive substrate to each spectral band. The array provides for parallel and simultaneous readout of each sub-array to increase the collection rate of the spectral imagery. All of these features serve to provide a substantial improvement in the area coverage of a hyperspectral imaging system while at the same time increasing the SNR of the detected spectral image.
    • 二维焦平面阵列(FPA)被划分为行和列的像素的子阵列,每个子阵列响应于已经被光谱滤波器或其他频谱分割元件分离的目标对象的光能 预定数量的光谱带。 对于每个预定的光谱带,FPA上最好有一个子阵列。 每个子阵列都有自己的读出通道,以允许并行和同时读出阵列的所有子阵列。 将场景扫描到阵列上,以便在许多光谱带中同时成像地形。 时间延迟和积分(TDI)技术被用作子阵列内的时钟机制,以增加检测到的图像的信噪比(SNR)。 此外,每个子阵列内的TDI长度(即,曝光期间的积分行数)是可调整的,以使感光基片对每个光谱带的响应进行优化和归一化。 该阵列提供并行和同时读取每个子阵列以增加光谱图像的采集速率。 所有这些特征用于提供高光谱成像系统的面积覆盖的显着改进,同时增加检测到的光谱图像的SNR。
    • 3. 发明授权
    • Static event detection/protection device
    • 静态事件检测/保护装置
    • US06172496B2
    • 2001-01-09
    • US08714411
    • 1996-09-16
    • James P. KarinsNiels F. Jacksen
    • James P. KarinsNiels F. Jacksen
    • G01R1524
    • G01R5/28G01R31/002H05K1/0254H05K1/0266
    • A reusable miniaturized detector that utilizes magneto-optic elements to detect the occurrence of an electrostatic discharge during the manufacture or handling of electrostatic discharge sensitive electronic components and circuit boards. The device may also be used to determine the polarity and magnitude of the electrostatic discharge. The device may be manually or automatically read, either by removing the device from the environment being monitored or continuously monitoring in situ. The device can also be configured to provide protection to some electrostatic discharge events which could damage sensitive components being monitored.
    • 一种可重复使用的小型化检测器,其利用磁光元件来检测在制造或处理静电放电敏感电子部件和电路板期间的静电放电的发生。 该装置还可用于确定静电放电的极性和幅度。 可以手动或自动读取设备,方法是将设备从正在监视的环境中移除或者在现场连续监控。 该装置还可以被配置成为某些静电放电事件提供保护,这些静电放电事件可能会损坏被监测的敏感元件。
    • 5. 发明申请
    • APPARATUS, SYSTEM, AND METHOD FOR OBJECT DETECTION AND IDENTIFICATION
    • 用于物体检测和识别的装置,系统和方法
    • US20120143808A1
    • 2012-06-07
    • US12959207
    • 2010-12-02
    • James P. KarinsStuart A. Mills
    • James P. KarinsStuart A. Mills
    • G06N5/02
    • G06N7/005
    • An apparatus, system, and method are disclosed for identifying a target object. An object detection module detects objects by matching data from one or more sensors to known data of a target object and determining one or more correlation metrics for each object. An object tracking module tracks geographic locations for detected objects over time using subsequent data from the one or more sensors. A contextual data module determines one or more contextual indicators for detected objects based on the data from the one or more sensors. An artificial intelligence module estimates probabilities that detected objects comprise the target object based on the correlation metrics, the geographic locations, the contextual indicators, and one or more target contextual indicators associated with the target object. The artificial intelligence module estimates the probabilities using an artificial intelligence model, such as a Bayesian network.
    • 公开了用于识别目标对象的装置,系统和方法。 对象检测模块通过将来自一个或多个传感器的数据与目标对象的已知数据进行匹配来检测对象,并确定每个对象的一个​​或多个相关度量。 对象跟踪模块使用来自一个或多个传感器的后续数据随时间跟踪检测到的对象的地理位置。 上下文数据模块基于来自一个或多个传感器的数据确定检测到的对象的一个​​或多个上下文指示符。 人造智能模块基于相关度量,地理位置,上下文指示符以及与目标对象相关联的一个或多个目标上下文指示来估计检测到的对象包括目标对象的概率。 人工智能模块使用诸如贝叶斯网络的人造智能模型来估计概率。
    • 6. 发明授权
    • Static event detection/protection device
    • 静态事件检测/保护装置
    • US06583612B2
    • 2003-06-24
    • US09755712
    • 2001-01-05
    • James P. KarinsNiels F. Jacksen
    • James P. KarinsNiels F. Jacksen
    • G01R1524
    • G01R5/28G01R31/002H05K1/0254H05K1/0266
    • A reusable miniaturized detector that utilizes magneto-optic elements to detect the occurrence of an electrostatic discharge during the manufacture or handling of electrostatic discharge sensitive electronic components and circuit boards. The device may also be used to determine the polarity and magnitude of the electrostatic discharge. The device may be manually or automatically read, either by removing the device from the environment being monitored or continuously monitoring in situ. The device can also be configured to provide protection to some electrostatic discharge events which could damage sensitive components being monitored.
    • 一种可重复使用的小型化检测器,其利用磁光元件来检测在制造或处理静电放电敏感电子部件和电路板期间的静电放电的发生。 该装置还可用于确定静电放电的极性和幅度。 可以手动或自动读取设备,方法是将设备从正在监视的环境中移除或者在现场连续监控。 该装置还可以被配置成为某些静电放电事件提供保护,这些静电放电事件可能会损坏被监测的敏感元件。