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公开(公告)号：US08077036B2
公开(公告)日：2011-12-13
申请号：US12244549
申请日：2008-10-02
IPC分类号： G08B13/00
CPC分类号： G01V1/001 , G08B13/1663
摘要： A system for detecting and classifying a security breach may include at least one sensor configured to detect seismic vibration from a source, and to generate an output signal that represents the detected seismic vibration. The system may further include a controller that is configured to extract a feature vector from the output signal of the sensor and to measure one or more likelihoods of the extracted feature vector relative to set {bi} (i=1, . . . , imax) of breach classes bi. The controller may be further configured to classify the detected seismic vibration as a security breach belonging to one of the breach classes bi, by choosing a breach class within the set {bi} that has a maximum likelihood.
摘要翻译： 用于检测和分类安全漏洞的系统可以包括被配置为检测来自源的地震振动并且产生表示检测到的地震振动的输出信号的至少一个传感器。 该系统还可以包括控制器，其被配置为从传感器的输出信号中提取特征向量，并且测量提取的特征向量相对于集合{bi}（i = 1，...，imax ）违规行为 控制器还可以被配置为通过选择具有最大可能性的集合{bi}内的违约等级，将检测到的地震振动分类为属于违约类别bi之一的安全漏洞。
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公开(公告)号：US20090309725A1
公开(公告)日：2009-12-17
申请号：US12244549
申请日：2008-10-02
CPC分类号： G01V1/001 , G08B13/1663
摘要： A system for detecting and classifying a security breach may include at least one sensor configured to detect seismic vibration from a source, and to generate an output signal that represents the detected seismic vibration. The system may further include a controller that is configured to extract a feature vector from the output signal of the sensor and to measure one or more likelihoods of the extracted feature vector relative to set {bi} (i=1, . . . , imax) of breach classes bi. The controller may be further configured to classify the detected seismic vibration as a security breach belonging to one of the breach classes bi, by choosing a breach class within the set {bi} that has a maximum likelihood.
摘要翻译： 用于检测和分类安全漏洞的系统可以包括被配置为检测来自源的地震振动并且产生表示检测到的地震振动的输出信号的至少一个传感器。 该系统还可以包括控制器，其被配置为从传感器的输出信号中提取特征向量，并且测量提取的特征向量相对于集合{bi}（i = 1，...，imax ）违规行为 控制器还可以被配置为通过选择具有最大可能性的集合{bi}内的违约等级，将检测到的地震振动分类为属于违约类别bi之一的安全漏洞。
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公开(公告)号：US20110172954A1
公开(公告)日：2011-07-14
申请号：US12763974
申请日：2010-04-20
申请人： Theodore W. Berger , Alireza Dibazar , Ali Yousefi
发明人： Theodore W. Berger , Alireza Dibazar , Ali Yousefi
IPC分类号： G06F15/00
CPC分类号： G08B13/122
摘要： A compact, inexpensive, and reliable fence intrusion detection system may detect activity on a fence and determine the type of activity based on discrimination. The hardware may include a 3-axis accelerometer and a RISC microprocessor. The system may be equipped with a wireless device which enables the system to work remotely and communicate with a base station. An algorithm may detect activity vs. no-activity on the fence. The algorithm may thereafter recognize the type of the activity; such as whether it is due to rattling caused by strong wind or a breach such as a person climbing the fence. The recognition algorithm may be computationally inexpensive and therefore also may be embedded inside a local RISC microcontroller. The system has been tested on different fences and demonstrated an over 90% correct recognition rate.
摘要翻译： 一个紧凑，廉价和可靠的围栏入侵检测系统可以检测围栏的活动，并根据歧视确定活动的类型。 硬件可以包括3轴加速度计和RISC微处理器。 该系统可以配备有使得系统能够远程工作并与基站通信的无线设备。 算法可以检测围栏上的活动与无活动。 该算法之后可以识别该活动的类型; 是否是由于强风引起的嘎嘎声，还是因为爬上篱笆的人而造成的。 识别算法可能在计算上是廉价的，因此也可以嵌入在本地RISC微控制器内部。 该系统已经在不同的围栏进行了测试，证明了90％以上的正确识别率。
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4.发明授权Protecting military perimeters from approaching human and vehicle using biologically realistic neural network 有权使用生物学现实的神经网络保护军事周界免受人和车辆的接近公开(公告)号：US08615476B2
公开(公告)日：2013-12-24
申请号：US12759556
申请日：2010-04-13
IPC分类号： G06F15/18
CPC分类号： G06N3/049 , G08B13/1663
摘要： An approaching human threat or vehicle, such as a suicide bomber nearing a secured zone such as a military base, may be detected and classified. A vibration recognition system may detect a systematic vibration event. The entity might be a medium, human, animal, or a passenger vehicle. The system may discriminate between such an event and a background or other vibration event, such as a falling tree limb. A seismic sensor may be employed to detect vibrations generated by footsteps and a vehicle. Seismic waves may be processed locally where the sensor is located. The system may wirelessly communicate with a remote command center. Temporal features of the vibration signals may be modeled by a biologically realistic neural network with good false recognition rates. The models may reject quadrupedal animal footsteps.
摘要翻译： 可以检测和分类接近的人类威胁或车辆，例如靠近诸如军事基地的安全区域附近的自杀炸弹手。 振动识别系统可以检测系统振动事件。 实体可能是中型，人类，动物或乘用车。 该系统可以区分这种事件和背景或其他振动事件，例如落下的树枝。 可以采用地震传感器来检测脚步声和车辆产生的振动。 地震波可以在传感器所在的地方进行处理。 系统可以与远程命令中心进行无线通信。 振动信号的时间特征可以用具有良好的误识别率的生物学上逼真的神经网络来建模。 这些模型可能会拒绝四足动物脚步声。
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5.发明申请PROTECTING MILITARY PERIMETERS FROM APPROACHING HUMAN AND VEHICLE USING BIOLOGICALLY REALISTIC NEURAL NETWORK 有权使用生物学现实神经网络来保护人类和车辆的军事人员公开(公告)号：US20100268671A1
公开(公告)日：2010-10-21
申请号：US12759556
申请日：2010-04-13
IPC分类号： G06N3/02
CPC分类号： G06N3/049 , G08B13/1663
摘要： An approaching human threat or vehicle, such as a suicide bomber nearing a secured zone such as a military base, may be detected and classified. A vibration recognition system may detect a systematic vibration event. The entity might be a medium, human, animal, or a passenger vehicle. The system may discriminate between such an event and a background or other vibration event, such as a falling tree limb. A seismic sensor may be employed to detect vibrations generated by footsteps and a vehicle. Seismic waves may be processed locally where the sensor is located. The system may wirelessly communicate with a remote command center. Temporal features of the vibration signals may be modeled by a Dynamic Synapse Neural Network (DSNN) with good false recognition rates. The models may reject quadrupedal animal footsteps.
摘要翻译： 可以检测和分类接近的人类威胁或车辆，例如靠近诸如军事基地的安全区域附近的自杀炸弹手。 振动识别系统可以检测系统振动事件。 实体可能是中型，人类，动物或乘用车。 该系统可以区分这种事件和背景或其他振动事件，例如落下的树枝。 可以采用地震传感器来检测脚步声和车辆产生的振动。 地震波可以在传感器所在的地方进行处理。 系统可以与远程命令中心进行无线通信。 振动信号的时间特征可以由具有良好的错误识别率的动态突触神经网络（DSNN）来建模。 这些模型可能会拒绝四足动物脚步声。
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6.发明申请公开(公告)号：US20100260011A1
公开(公告)日：2010-10-14
申请号：US12756657
申请日：2010-04-08
IPC分类号： G01V1/30
CPC分类号： G01V1/30
摘要： Systems, methods, and apparatus are described that provide for analysis of seismic data. Features of temporal gait patterns can be extracted from seismic/vibration data. A mean temporal gait pattern can be determined. A statistical classifier can be used to model features of the data. The model can be used to classify the data. As a result, discrimination of seismic sources can be performed. Systems for discrimination of seismic data are also described. A system can include a vibration sensor system configured and arranged to detect vibrations. A system can also include a processor system configured and arranged to receive data from the vibration sensor, recognize the seismic data as belonging to a particular class of seismic data, and produce an output signal corresponding to the recognized particular class of seismic data.
摘要翻译： 描述了提供地震数据分析的系统，方法和装置。 时间步态模式的特征可以从地震/振动数据中提取出来。 可以确定平均时间步态模式。 统计分类器可用于对数据的特征进行建模。 该模型可用于对数据进行分类。 因此，可以进行地震源的辨别。 还描述了用于区分地震数据的系统。 系统可以包括被配置和布置成检测振动的振动传感器系统。 系统还可以包括配置和布置成从振动传感器接收数据的处理器系统，将地震数据识别为属于特定类别的地震数据，并且产生对应于所识别的特定等级的地震数据的输出信号。
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7.发明授权Acoustic signature recognition of running vehicles using spectro-temporal dynamic neural network 有权使用光谱时间动态神经网络的运行车辆的声学签名识别公开(公告)号：US08111174B2
公开(公告)日：2012-02-07
申请号：US12245575
申请日：2008-10-03
申请人： Theodore W. Berger , Alireza Dibazar , Bing Lu
发明人： Theodore W. Berger , Alireza Dibazar , Bing Lu
IPC分类号： G08G1/04
摘要： A method and apparatus for identifying running vehicles in an area to be monitored using acoustic signature recognition. The apparatus includes an input sensor for capturing an acoustic waveform produced by a vehicle source, and a processing system. The waveform is digitized and divided into frames. Each frame is filtered into a plurality of gammatone filtered signals. At least one spectral feature vector is computed for each frame. The vectors are integrated across a plurality of frames to create a spectro-temporal representation of the vehicle waveform. In a training mode, values from the spectro-temporal representation are used as inputs to a Nonlinear Hebbian learning function to extract acoustic signatures and synaptic weights. In an active mode, the synaptic weights and acoustic signatures are used as patterns in a supervised associative network to identify whether a vehicle is present in the area to be monitored. In response to a vehicle being present, the class of vehicle is identified. Results may be provided to a central computer.
摘要翻译： 一种用于使用声学签名识别识别待监视区域中的行驶车辆的方法和装置。 该装置包括用于捕获由车辆源产生的声波形的输入传感器和处理系统。 波形被数字化并分成帧。 每个帧被过滤成多个经过Gammatone滤波的信号。 为每个帧计算至少一个光谱特征向量。 矢量被集成在多个帧之间以产生车辆波形的频谱表示。 在训练模式中，来自频谱表示的值被用作非线性Hebbian学习函数的输入，以提取声学签名和突触权重。 在主动模式中，突触权重和声学特征被用作监督关联网络中的模式，以识别车辆是否存在于要监视的区域中。 响应于存在的车辆，识别出车辆等级。 可以向中央计算机提供结果。
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8.发明申请公开(公告)号：US20090115635A1
公开(公告)日：2009-05-07
申请号：US12245564
申请日：2008-10-03
申请人： Theodore W. Berger , Alireza Dibazar , Bing Lu
发明人： Theodore W. Berger , Alireza Dibazar , Bing Lu
摘要： A method and apparatus for identifying running vehicles in an area to be monitored using acoustic signature recognition. The apparatus includes an input sensor for capturing an acoustic waveform produced by a vehicle source, and a processing system. The waveform is digitized and divided into frames. Each frame is filtered into a plurality of gammatone filtered signals. At least one spectral feature vector is computed for each frame. The vectors are integrated across a plurality of frames to create a spectro-temporal representation of the vehicle waveform. In a training mode, values from the spectro-temporal representation are used as inputs to a Nonlinear Hebbian learning function to extract acoustic signatures and synaptic weights. In an active mode, the synaptic weights and acoustic signatures are used as patterns in a supervised associative network to identify whether a vehicle is present in the area to be monitored. In response to a vehicle being present, the class of vehicle is identified. Results may be provided to a central computer.
摘要翻译： 一种用于使用声学签名识别识别待监视区域中的行驶车辆的方法和装置。 该装置包括用于捕获由车辆源产生的声波形的输入传感器和处理系统。 波形被数字化并分成帧。 每个帧被过滤成多个经过Gammatone滤波的信号。 为每个帧计算至少一个光谱特征向量。 矢量被集成在多个帧之间以产生车辆波形的频谱表示。 在训练模式中，来自频谱表示的值被用作非线性Hebbian学习函数的输入，以提取声学签名和突触权重。 在主动模式中，突触权重和声学特征被用作监督关联网络中的模式，以识别车辆是否存在于要监视的区域中。 响应于存在的车辆，识别出车辆等级。 可以向中央计算机提供结果。
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9.发明申请ACOUSTIC SIGNATURE RECOGNITION OF RUNNING VEHICLES USING SPECTRO-TEMPORAL DYNAMIC NEURAL NETWORK 有权运动光谱使用光谱动态神经网络的声学识别识别公开(公告)号：US20110169664A1
公开(公告)日：2011-07-14
申请号：US12245575
申请日：2008-10-03
申请人： Theodore W. Berger , Alireza Dibazar , Bing Lu
发明人： Theodore W. Berger , Alireza Dibazar , Bing Lu
IPC分类号： G08G1/04
摘要： A method and apparatus for identifying running vehicles in an area to be monitored using acoustic signature recognition. The apparatus includes an input sensor for capturing an acoustic waveform produced by a vehicle source, and a processing system. The waveform is digitized and divided into frames. Each frame is filtered into a plurality of gammatone filtered signals. At least one spectral feature vector is computed for each frame. The vectors are integrated across a plurality of frames to create a spectro-temporal representation of the vehicle waveform. In a training mode, values from the spectro-temporal representation are used as inputs to a Nonlinear Hebbian learning function to extract acoustic signatures and synaptic weights. In an active mode, the synaptic weights and acoustic signatures are used as patterns in a supervised associative network to identify whether a vehicle is present in the area to be monitored. In response to a vehicle being present, the class of vehicle is identified. Results may be provided to a central computer.
摘要翻译： 一种用于使用声学签名识别识别待监视区域中的行驶车辆的方法和装置。 该装置包括用于捕获由车辆源产生的声波形的输入传感器和处理系统。 波形被数字化并分成帧。 每个帧被过滤成多个经过Gammatone滤波的信号。 为每个帧计算至少一个光谱特征向量。 矢量被集成在多个帧之间以产生车辆波形的频谱表示。 在训练模式中，来自频谱表示的值被用作非线性Hebbian学习函数的输入，以提取声学签名和突触权重。 在主动模式中，突触权重和声学特征被用作监督关联网络中的模式，以识别车辆是否存在于要监视的区域中。 响应于存在的车辆，识别出车辆等级。 可以向中央计算机提供结果。
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