linkMQ

在一个自己设置的已知障碍的环境下，用势场法做路径规划，起点，目标位置都是已知。我用matlab做的(themselves in a set of known obstacles to the environment, with potential field do path planning, the starting point, target locations are known. I used Matlab to do)

关于合成孔径雷达点目标仿真的小文章，其中包括具体的matlab程序，简单明了。(Synthetic aperture radar on a small point target simulation of the article, including specific matlab program, plain and simple.)

用vba程序遍历所有目录下的excel文件，把各文件中两列经度和纬度的内容合并，并插入新列。很好的高效率的遍历程序。(Vba program to traverse all excel file directory, each file merge the contents of two of the latitude and longitude, and insert a new column. Good high efficiency of the traversal process.)

E=input( enter the generator voltage: ) V=input( enter the infinite bus voltage: ) Gx=input( enter the reactance of generator: ) L1x=input( enter the line reactance: ) L2x=input( enter the line reactance: ) L3x=input( enter the line reactance: ) L4x=input( enter the line reactance: ) Pi=input( enter the input power: ) X1=Gx+L1x+((L2x*L3x)/(L2x+L3x))+L4x Pm1=E*V/X1 DO=asin(Pi/Pm1) A=((L2x*L3x)/(2*(L2x+L3x))) C=A B=((L3x/2)^2)/(L2x+L3x) X2=(Gx+L1x+A)+(L4x+A)+(Gx+L1x+A)*(A+L4x)/B Pm2=(E*V)/X2 X3=Gx+L1x+L2x+L4x Pm3=(E*V)/X3 DC=input( enter the fault clearing angle: ) Dm=(3.14-asin(Pi/Pm3)) a1=quad( sin ,DO,DC) A1=(Pi*(DC-DO)-Pm2*a1) a2=quad( sin ,DC,Dm) A2=Pm3*a2-Pi*(Dm-DC) if(A2>=A1) disp( the system is stable ) else disp( the system is unstable ) end Dcc=acos((Pi*(Dm-DO)-Pm2*(cos(DO))+Pm3*(cos(Dm)))/(Pm3-Pm2)) disp( critical clearing angle: ) Dcc

说明：  ex9_1 脉冲信号调制 ex9_2 载波10MHz，带宽2MHz的线性调频信号及其频谱图 ex9_3 产生7位巴克码编码的二相码 ex9_4 产生7位巴克码和线性调频的混合调制信号 ex9_5 瑞利分布实现程序 ex9_6 瑞利分布＋杂波 ex9_7 相关对数正态分布杂波 ex9_8 相关weibull分布杂波 ex9_9 相干相关K分布杂波 ex9_10 线性调频信号的数字化正交解调 ex9_11 雷达脉冲压缩处理 ex9_12 二相编码信号的脉压处理 ex9_13 用FFT和FIR方法实现MTD处理(程序有错误) ex9_14 雷达的恒虚警处理（CFAR） ex9_15 比较相参积累和非相参积累 ex9_14 计算加性高斯白噪声准则下雷达探测的经典CFAR 二进制检测阈值和指定的虚警概率。 (瑞利噪声下快慢门限CFAR处理)(Ex9_1 pulse signal modulates ex9_2 carrier 10MHz, Linear Frequency Modulation signal of bandwidth 2MHz and its Spectrum Diagram ex9_14 calculation of classical CFAR under additive Gaussian white noise criterion Binary detection threshold and specified false alarm probability. (fast and slow threshold CFAR processing under Riley noise))

本程序是基于ridgelet和小波的图像分割，内附有程序运行的图像(This procedure is based on the Ridgelet and wavelet for image segmentation, enclosed to run image)

基于平面模板法镜头标定方法，本程序采用matlab编写，提供具体源码(graphic templates based on the camera calibration methods, the procedures used Matlab prepared to provide specific FOSS)

主成分分析的matlab编码,用于矩阵求特征值特征向量(Principal component analysis matlab coding for matrix eigenvalue eigenvector)

说明：  空间直角坐标转换，BURSA模型解算七参数(Transformation of space rectangular coordinate and solution of seven parameters with bursa model)

来自澳大利亚Qeensland大学的计算机视觉Matlab工具箱。 This Toolbox provides a number of functions that are useful in computer vision, machine vision and related areas. It is a somewhat eclectic collection reflecting the author s personal interest in areas of photometry, photogrammetry, colorimetry. It covers functions such as image file reading and writing, filtering, segmentation, feature extraction, camera calibration, camera exterior orientation, display, color space conversion and blackbody radiators. The Toolbox, combined with MATLAB and a modern workstation computer, is a useful and convenient environment for investigation of machine vision algorithms. It is possible to use MEX files to interface with image acquisition hardware ranging from simple framegrabbers to Datacube servers.(from Australia Qeensland University of Computer Vision Matlab toolbox. This Toolbox provides a number of functions tha t are useful in computer vision, machine vision and related areas. It is a somewh at eclectic collection reflecting the author's personal interest in areas of photometry. photogrammetry, colorimetry. It covers functions such as image file reading and writing, filtering, segmentation and feature extraction. camera calibration. camera exterior orientation, display, color space conversion and blackbody radiator s. The Toolbox. MATLAB and combined with a modern workstation c omputer. is a useful and convenient environment for OAS stigation of machine vision algorithms. It is p ossible to use MEX files to interface with image acquisition hardware ranging from simple fram eg)