untitled

说明：  比较俯仰角度分别为60°，90°，以及极化参数为60°和90°时的三极子天线和两极子天线的均方根误差(Comparison of tilt angles of 60 °, 90 °, and the polarization of 60 ° and 90 °, three-pole antenna and polarization Antenna root mean square error)

svpwm的源代码实现，对于正在做矢量控制系统的同志很有帮助，大家支持呀。(svpwm the source code for the vector control system is doing useful comrades, we support ah.)

求解任意阶数（大于0）的分数积分，要求输入时间数组和函数值数组(Solving any order (greater than 0) fractional integral, requiring the input time array and the array of function values)

学习用matlab求解最优化问题，提高matlab实际应用能力(Learn to use matlab to solve the optimization problem, the ability to improve the practical application of matlab)

基于兴趣点的对互信息度的图像匹配算法，介绍详细(Based on the mutual information of the image matching algorithm point of interest, Detailed)

运用Matlab仿真工具，结合机械臂运运动学特性，实现了机械臂系统的运动学仿真。 (Use the Matlab simulation tools, combined with the kinematic characteristics of the robotic arm transported the kinematics of the manipulator system simulation.)

imagem polar signature matlab

MATLAB操作极其简述，是书籍，供初学者参考(MATLAB operation is extremely brief, are books for beginners reference)

在计算机上产生一组实验数据，首先产生一段零均值白噪声数据u（n）,令功率为 ，让u(n)通过一个三阶FIR： 得到y(n). .y(n)上加三个实正弦信号f1’=0.1,f2’=0.25,f3’=0.26调整 和正弦信号幅度信噪比大致为10dB,50dB,50dB. (1) 令N=256,描绘xn波形； (2)得出真实功率谱密度 . (3) 利用此实验数据Pisarenko谐波分解法估计该实验数据的正弦频率及幅度。 (On the computer to generate a set of experimental data, first generate a zero mean white noise data u (n), so that power, let u (n) through a third-order FIR: get y (n).. Y (n) added three real sinusoidal signal f1 ' = 0.1, f2' = 0.25, f3 ' = 0.26 and sine signal amplitude adjustment signal to noise ratio is approximately 10dB, 50dB, 50dB. (1) that N = 256, depicted xn waveform (2) the true power spectral density obtained. (3) using this experimental data, Pisarenko harmonic decomposition method to estimate the experimental data of the sinusoidal frequency and amplitude.)

边缘检测，用matlab编程。边缘检测是图像处理和计算机视觉中的基本问题，边缘检测的目的是标识数字图像中亮度变化明显的点。图像属性中的显著变化通常反映了属性的重要事件和变化。(Edge detection, using matlab programming. Edge detection is an image processing and computer vision in the basic problems, edge detection is to identify the purpose of the digital image brightness variations obvious point. Image attribute significant changes usually reflect important events and changes in properties.)