计算振动信号各种参数的实验程序

说明：  gauss积分matlab代码，一个完整的程序(matlab code gauss points, a complete program)

仿真不同占空比，等概、非等概双极性矩形随机信号的归一化功率谱密度(Simulation different duty cycles, such as probability, non-equiprobable random signal bipolar rectangular normalized power spectral density)

Mann Morrison SOurce code

利用偏移量插值实现图像的透视变换（运用了双线性变换原理）(Offset interpolation using the image perspective transformation (using the bilinear transformation principle))

运用编程实现提取一段视频的每一帧并保存。利用Fbackground函数实现提取一段视频的静止背景。(Use programming to extract each frame of a video and save it. The static background of a video is extracted by Fbackground function.)

基于条纹反射的三维形貌测量技术中，由MATLAB建立的非远心光路下的条纹反射系统模型。程序中带有注释。(3-d topography measurement based on fringe reflection of technology, set up by MATLAB stripe reflection not under the telecentric light path system model. In the program with comments.)

商标文字分割 matlab 实现 保证可用(Matlab implementation to ensure that trademarks can be split text)

本程序是基于支持向量机的图像分割问题。通过支持向量机对图片的采样点进行训练，然后对整张图片进行预测。可以将图片分为前景与背景两个部分。(This procedure is based on the support vector machine image segmentation problem. Through the support vector machine to the picture of the sampling point for training, and then the whole picture to predict. Images can be divided into two parts: foreground and background. )

关于雷达相位法测角的论文：MUSIC，多基线相位测角法(Phase angle measured on radar papers: MUSIC, multi-baseline phase angle measurement method)

S4VM 对传统的S3VM 进行了改进。传统的S3VM 基于低密度假设，它试图找到一个低密度的分界线， 也就是更倾向于决策边界穿过特征空间的低密度区域。S4VM 和S3VM 的不同点在于，S3VM 试图把注意力 集中在一个最优的低密度分界线上，而S4VM 则同时关注多个可能的低密度分界线。之所以这样做，是因为 给定一些有标记的点和大量未标记的点，可能存在着不止一个“间隔”较大的低密度分界线（如图2 ），基于 有限的标记样本，很难决定哪个是最好的。虽然这些低密度分界线都与有限的标记样本吻合，但它们之间差 异很大，因此如果选择错了，会有一个很大的损失，导致性能下降。所以S4VM 试图考虑所有可能的低密度 分界线。 (Over the past decade, many improved versions are proposed to solve every different specific problem of semi-supervised support vector machines, for example: to the inefficiency issue, meanS3VM is introduced to performance degenerate problem, S4VM is proposed)