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Multiple Source EM Wave propagation modeling via Finite Differences

说明：  数据回归分析和拟合的Matlab实现教程 (Data regression analysis and fitting of the Matlab implementation tutorial)

Composite analysis and calculate ABD matrice and analys=ze using different composite failure theories

说明：  最新UWB 60GHz tg3c channel model(newest UWB 60GHz tg3c channel model)

基于最小值选中次数的分离谱加权算法的超声信号处理方法研究(Outrageous weighting algorithm based on the minimum value of the selected number of sub ultrasound signal processing method)

从X光片中提取出骨头影响，并对其区域进行标注。(X-ray film extracted from the bones of the impact, and its region mark.)

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This paper describes the impact of fuzzy parameter in FCM algorithm, and found 2.1 value as suitable one.

This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. Before getting to a description of PCA, this tutorial first introduces mathematical concepts that will be used in PCA. It covers standard deviation, covariance, eigenvectors and eigenvalues. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.