A nonlinear background removal method for seismo-ionospheric anomaly analysis under a complex solar activity scenario: A case study of the M9.0 Tohoku earthquake

A precise determination of ionospheric total electron content (TEC) anomaly variations that are likely associated with large earthquakes as observed by global positioning system (GPS) requires the elimination of the ionospheric effect from irregular solar electromagnetic radiation. In particular, revealing the seismo-ionospheric anomalies when earthquakes occurred during periods of high solar activity is of utmost importance. To overcome this constraint, a multiresolution time series processing technique based on wavelet transform applicable to global ionosphere map (GIM) TEC data was used to remove the nonlinear effect from solar radiation for the earthquake that struck Tohoku, Japan, on 11 March, 2011. As a result, it was found that the extracted TEC have a good correlation with the measured solar extreme ultraviolet flux in 26–34 nm (EUV_26–34) and the 10.7 cm solar radio flux (F_10.7). After removing the influence of solar radiation origin in GIM TEC, the analysis results show that the TEC around the forthcoming epicenter and its conjugate were significantly enhanced in the afternoon period of 8 March 2011, 3 days before the earthquake. The spatial distributions of the TEC anomalous and extreme enhancements indicate that the earthquake preparation process had brought with a TEC anomaly area of size approximately 1650 and 5700 km in the latitudinal and longitudinal directions, respectively.     

Advanced fractal approach for unsupervised classification of SAR images

Unsupervised classification of Synthetic Aperture Radar (SAR) images is the alternative approach when no or minimum apriori information about the image is available. Therefore, an attempt has been made to develop an unsupervised classification scheme for SAR images based on textural information in present paper. For extraction of textural features two properties are used viz. fractal dimension D and Moran’s I. Using these indices an algorithm is proposed for contextual classification of SAR images. The novelty of the algorithm is that it implements the textural information available in SAR image with the help of two texture measures viz. D and I. For estimation of D, the Two Dimensional Variation Method (2DVM) has been revised and implemented whose performance is compared with another method, i.e., Triangular Prism Surface Area Method (TPSAM). It is also necessary to check the classification accuracy for various window sizes and optimize the window size for best classification. This exercise has been carried out to know the effect of window size on classification accuracy. The algorithm is applied on four SAR images of Hardwar region, India and classification accuracy has been computed. A comparison of the proposed algorithm using both fractal dimension estimation methods with the K-Means algorithm is discussed. The maximum overall classification accuracy with K-Means comes to be 53.26% whereas overall classification accuracy with proposed algorithm is 66.16% for TPSAM and 61.26% for 2DVM.     

民航卫星通信网可视通信图像压缩编码方法研究

对民航卫星通信网进行可视通信图像压缩编码方法进行了研究。通过在多媒体计算机上进行模拟实验表明，将视频信息及语音信息压缩为64kbps通过TES卫星网传输方案是可行的。     

Use of the ASTER satellite images for evaluation of structural changes in the Popocatépetl volcano related to microseismicity

Mexico is one of the most volcanically active regions in North America. Volcanic activity in central Mexico is associated with the subduction of the Cocos and Rivera plates beneath the North American plate. Periods of enhanced microseismic activity, associated with the volcanic activity of the Popocatépetl volcano are compared with periods, during which the microseismic activity was low. We detected systematical changes in the number of lineaments, associated with the microseismic activity due to lineament analysis of a temporal sequence of high resolution satellite images of the Popocatépetl volcano, provided by the ASTER/VNIR instrument. The Lineament Extraction and Stripes Statistic Analysis (LESSA) software package was used for the lineament extraction. In the future it would allow develop a methodology for detection of possible elevation of pressure in volcano edifice.     

二维计算机图象检测系统

目前,利用机器视觉系统监测生产过程和进行质量控制是一个非常活跃的研究领域。当把视觉系统用于几何量测量时,必须解决其硬件的分辨力极限所带来的问题:测量精度高意味着测量范围小,或者,测量范围大意味着测量精度低。提出了解决上述问题的方法:一个由步进电机驱动的XY工作台与图象处理系统相结合。产生出一个多坐标系统结构。测量参数在两个坐标系之间传送,并在100mm×100mm范围内,在X和Y两个方向,达到的测量精度为0.02mm。     

“自准”赝像及识别

置平面镜于凸透镜后,则凸透镜前焦平面上的物成像于该物平面上,即“自准”成像.满足其它条件也能成像于物平面.分析不同条件下像的形成及其特点,并给出识别自准成像的判据.     

一种基于视觉的微型飞行器姿态检测算法

探讨了基于视觉的微型飞行器的飞行状态检测方法，利用机载摄像头获得图像。根据某种准则识别出地平线，进而变换得到飞机的姿态角。文中给出了地平线判别准则及相应的算法和姿态角变换公式。最后，给出了一个实际算例及处理结果。     

高分影像辅助下的航空高光谱严密几何检校方法

 惯性测量单元(IMU)与传感器视准轴的偏心角和偏心矢量是造成航空线阵列高光谱数据几何校正误差的主要原因之一。在分析偏心角与偏心矢量误差来源之后提出该误差由IMU主轴与传感器主轴的角度偏差、测区固定偏差、GPS中心与传感器投影中心相对偏差组成,在此基础上建立了较为严密的检校模型。针对模型解算时需要大量高精度控制点的问题,提出了一种高分影像辅助下的亚像元精度控制点自动提取方法。通过多地区、多传感器高光谱航测实验表明,亚像元精度控制点能有效提高模型解算精度。新检校模型可获得亚像元校正精度,推扫式传感器——应用型机载成像光谱仪(AISA)建模中误差约为0.39个像元,摆扫式传感器——实用型模块化成像光谱仪(OMIS)建模中误差约为0.23个像元,校正后的影像可直接进行拼接。     

耳朵识别身份系统

打击恐怖主义首先要预防恐怖分子,生物识别技术有助于将恐怖分子从人群中识别出来.耳朵是长期保持稳定的一个生物特征,并且耳朵的独特性十分明显.介绍了耳朵识别系统的系统功能和设计思路,采用VC 6.0和SQL SERVER 2000数据库,实现耳朵图像数据采集、图像预处理、图像存储等基本功能.     

一种基于粗糙K-均值的椭球基函数神经网络

改进了一种椭球基函数神经网络,它与经典椭球单元神经网络的结构不同,而与径向基函数神经网络结构类似,即它有一个隐含层,并且隐层单元采用椭球基函数,区别于RBF网络的高斯函数。本文采用粗糙K-均值方法求取椭球函数的中心,并给出了该方法中确定初始阈值的步骤。这种改进方法不但使对输入空间的划分局部作用,而且划分区域封闭有界。因此,改进的神经网络具有较好的函数逼近能力和模式识别能力。仿真实验验证了该椭球基函数神经网络的正确性和有效性。