公开(公告)号：US06464392B1

公开(公告)日：2002-10-15

申请号：US09546742

申请日：2000-04-11

申请人： Arthur H. Carrieri ,  Irving F. Barditch ,  David J. Owens ,  Erik S. Roese ,  Pascal I. Lim ,  Michael V. Talbard

发明人： Arthur H. Carrieri ,  Irving F. Barditch ,  David J. Owens ,  Erik S. Roese ,  Pascal I. Lim ,  Michael V. Talbard

IPC分类号： G01N2572

CPC分类号： G01N21/64 ,  G01N25/72

摘要： Chemical agent warfare materials and their simulant liquids are identified on terrestrial surfaces at a distance by recognizing the contaminant's infrared fingerprint spectrum brought out in thermal luminescence (TL). Suspect surfaces are irradiated with microwave light that is absorbed into the surface and, subsequently, TL is released by the surface. An optics receiver collects the released TL radiant light, and a data acquisition system searches this TL radiant flux for the contaminant's fingerprint infrared spectrum. A decision on the presence or absence of any-of-N contaminants is done by a neural network system that acts as a filter through real-time pattern recognition of the contaminant's unique infrared absorption or emission spectra.

摘要翻译： 通过识别在热发光（TL）中产生的污染物的红外指纹谱，在远处的地面上识别化学试剂材料及其模拟液体。 用表面吸收的微波照射可疑表面，随后TL被表面释放。 光学接收器收集释放的TL辐射光，并且数据采集系统搜索该TL辐射通量用于污染物的指纹红外光谱。 关于任何N污染物的存在或不存在的决定是通过神经网络系统完成的，神经网络系统通过对污染物独特的红外吸收或发射光谱的实时模式识别而充当过滤器。

公开(公告)号：US08164742B1

公开(公告)日：2012-04-24

申请号：US11779457

申请日：2007-07-18

申请人： Arthur H. Carrieri ,  Erik S. Roese ,  David J. Owens ,  Jonathan C. Schultz ,  Michael V. Talbard ,  Pascal I. Lim ,  Kevin C. Hung ,  Jerold R. Bottiger

发明人： Arthur H. Carrieri ,  Erik S. Roese ,  David J. Owens ,  Jonathan C. Schultz ,  Michael V. Talbard ,  Pascal I. Lim ,  Kevin C. Hung ,  Jerold R. Bottiger

IPC分类号： G01C3/08

CPC分类号： G01N21/21 ,  G01S7/4811 ,  G01S7/499 ,  G01S17/88

摘要： An optomechanical switching device, a control system, and a graphical user interface for a photopolarimetric lidar standoff detection that employs differential-absorption Mueller matrix spectroscopy. An output train of alternate continuous-wave CO2 laser beams [ . . . L1:L2 . . . ] is directed onto a suspect chemical-biological (CB) aerosol plume or the land mass it contaminates (S) vis-à-vis the OSD, with L1 [L2] tuned on [detuned off] a resonant molecular absorption moiety of CB analyte. Both incident beams and their backscattered radiances from S are polarization-modulated synchronously so as to produce gated temporal voltage waveforms (scattergrams) recorded on a focus at the receiver end of a sensor (lidar) system. All 16 elements of the Mueller matrix (Mij) of S are measured via digital or analog filtration of constituent frequency components in these running scattergram data streams (phase-sensitive detection). A collective set of normalized elements {ΔMi,j} (ratio to M11) susceptible to analyte, probed on-then-off its molecular absorption band, form a unique detection domain that is scrutinized; i.e., any mapping onto this domain by incoming lidar data—by means of a trained neural network pattern recognition system for instance—cues a standoff detection event.

摘要翻译： 光学机械开关装置，控制系统和用于使用差分吸收Mueller矩阵光谱的光偏振激光雷达检测的图形用户界面。 交替连续波CO2激光束的输出列[ 。 。 L1：L2。 。 。 ]被引导到可疑化学生物（CB）气溶胶羽流或其相对于OSD污染的陆地（S），其中L1 [L2]调谐到CB分析物的共振分子吸收部分[失谐] 。 来自S的入射光束和它们的反向散射辐射均被同步偏振调制，以便产生记录在传感器（激光雷达）系统的接收器端的焦点上的门控时间电压波形（散点图）。 S的Mueller矩阵（Mij）的所有16个元素通过数字或模拟过滤在这些运行散点图数据流（相敏检测）中的组成频率分量来测量。 对分析物敏感的一组归一化元素{＆Dgr; Mi，j}（与M11的比例）形成了一个独特的检测结构域，被检测出来; 即通过输入的激光雷达数据通过训练的神经网络模式识别系统对该域进行任何映射，例如提示了间隔检测事件。

公开(公告)号：US5631469A

公开(公告)日：1997-05-20

申请号：US636994

申请日：1996-04-15

申请人： Arthur H. Carrieri ,  Pascal I. Lim

发明人： Arthur H. Carrieri ,  Pascal I. Lim

IPC分类号： G01N21/35 ,  G06K9/66 ,  G01J3/433

CPC分类号： G06K9/66 ,  G01N21/3504

摘要： A four-layer neural network is trained with data of midinfrared absorption by nerve and blister agent compounds (and simulants of this chemical group) in a standoff detection application. Known infrared absorption spectra by these analyte compounds and their computed first derivative are scaled and then transformed into binary or decimal arrays for network training by a backward-error-propagation (BEP) algorithm with gradient descent paradigm. The neural network transfer function gain and learning rate are adjusted on occasion per training session so that a global minimum in final epoch convergence is attained. Three successful neural network filters have been built around an architecture design containing: (1) an input layer of 350 neurons, one neuron per absorption intensity spanning 700.ltoreq..nu..ltoreq.1400 wavenumbers with resolution .DELTA..nu.=2; (2) two hidden layers in 256- and 128-neuron groups, respectively, providing good training convergence and adaptable for downloading to a configured group of neural IC chips; and (3) an output layer of one neuron per analyte--each analyte defined by a singular vector in the training data set. Such a neural network is preferably implemented with a network of known microprocessor chips.

摘要翻译： 在间隔检测应用中，通过神经和起泡剂化合物（以及该化学基团的模拟物）的数据来训练四层神经网络。 通过这些分析化合物及其计算的一阶导数的已知红外吸收光谱进行缩放，然后通过具有梯度下降范例的反向误差传播（BEP）算法将其转换为二进制或十进制数组进行网络训练。 神经网络传递函数增益和学习率在每次训练中偶尔进行调整，从而达到最终时代收敛的全局最小值。 已经建立了三个成功的神经网络滤波器，包括：（1）350个神经元的输入层，每个吸收强度的一个神经元跨越700