stackmor

这是一本对MATLAB函数语句进行说明的一本电子书(This is one pairs of MATLAB statements illustrate the function of an e-book)

The important method is [COEFF,SCORE] = princomp(x) which takes in your data “x” and stores its projection into PCA space in “SCORE” which I then output to csv. I still need to find out how to project back into normal space but I think it should be just as straightforward as this was. For more info on “princomp” type “help princomp” into matlab and have a look at the help files.

说明：  gpops工具箱，含官方手册和例子，适合新手学习(Gpops toolbox, including the official manual and examples, suitable for beginners to learn)

convolution between two sequences

m序列带有白噪声干扰的的脉冲系统系统自相关函数误差分析(pulse system,M sequence, white disturbance)

有限差分法的Matlab程序（椭圆型方程）(Finite difference method in Matlab (elliptic equations))

一篇介绍matlab实现高效编程的文档，对于每一种方法都有相关的例子(An efficient introduction matlab programming documents, for each method are related to the case)

完成对于图像的DCT变换反变换，FFT变换反变换，动态模糊，加噪声去噪声等等，配有图形界面，功能强大(For complete image DCT transform inverse transform, FFT transform inverse transform, as well as some dynamic blurring function, and with a graphical interface)

说明：  针对四维目标跟踪模型的主程序，可以用来学习仿真模型参数(2017-ITAC-A Novel Adaptive Kalman Filter With Inaccurate Process and Measurement Noise Covariance Matrices)

创建一个n维的稀疏数组对象，n是任意值。 定义N可能是大于2的一类n维稀疏阵列。然而，它应该被认为是一种起动方式与普通的MATLAB稀疏矩阵和重塑它有N维度。换句话说，稀疏的数据，首先必须能够作为一个普通的2D MATLAB稀疏矩阵被前n维。事实上，如果目标数组的尺寸mxnxp……yxz，然后将它存储在内部类是一个普通的二维稀疏阵列的尺寸（M×N×P×……×Y）XZ。这导致了某些内存株时使用大量的尺寸。我发现有用的类主要用于中等尺寸像三维图像边缘检测，你经常要举行一个稀疏的3D的边缘地图。(Creates an N-dimensional sparse array object, for arbitrary N.This submission defines a class of N-dimensional sparse arrays for N possibly greater than 2. However, it should really be thought of as a way of starting with an ordinary MATLAB sparse matrix and reshaping it to have N dimensions. In other words, the sparse data must first be able to exist as an ordinary 2D MATLAB sparse matrix before being made N-dimensional. In fact, if the intended array has dimensions MxNxP...YxZ, then the class will store it internally as an ordinary 2D sparse array of dimensions (M*N*P*...*Y)xZ. This leads to certain memory strains when using large numbers of dimensions. I find the class useful mainly for moderate dimensional things like edge detection in 3D imaging, where you often want to hold a sparse 3D edge map. )