Poincare

小波包在信号消噪中的应用，利用小波包对给定含早信号进行消噪处理(Wavelet Packet Denoising in the application of wavelet packet to a given signal denoising with early treatment)

这是在MATLAB下编写的一个代码运行后会出现一个地球,还能旋转,很有意思的程序和大家分享(This is prepared in a MATLAB code to run after an earth, but also rotation, a very interesting process and U.S. share)

莫尔斯码的幅度调制与解调:信号x(t)含有一段简单的消息，由文件ctftmod.mat定义，由x(t)解出所需信息(The amplitude modulation and demodulation of the Morse code:the signal x (t) contains a simple piece of information, as defined by the file ctftmod.mat information required by the solution x (t))

图像配准定义为：对从不同传感器、不同时相、不同角度所获得的两幅或多幅图像进行最佳匹配的处理过程.图像配准需要分析各分量图像上的几何畸变，然后采用一种几何变换将图像归化到统一的坐标系统中。在配准过程中，通常取其中的一幅图像作为配准的标准，称之为参考图像；另一幅图像作为配准图像(Image registration algorithm)

This document provides a brief description of the models implemented in Matlab/Simulink and used the cases studies presented throughout the book. Although the models were developed in different recent versions of Simulink a final test was conducted in MATLAB R2013a. The models included are the following: • DFIG WT robust model for steady state simulation • DFIG WT robust model for steady state simulation in a HVDC system • Offshore Wind Farm Connection Using Point-to-Point VSC-HVDC (A) (changes in wind farm power output) • Offshore Wind Farm Connection Using Point-to-Point VSC-HVDC (B) (FRT analysis)

得到可观测到的卫星的坐标后，遍历所有的4个卫星组合（4点定位）计算GDOP。(To obtain the coordinates of the satellite can be observed after traversing all combinations of four satellites (4-point positioning) Calculation GDOP.)

说明：  实现了GMSK调制方式下OFDM系统的仿真(Achieved a GMSK modulation mode OFDM system simulation)

产生一个三分量地震图，有助于理解近场和远场的波场形态(Helps to understand the the wavefield morphology of the near-field and far-field to generate a three-component seismograms,)

特徵粹取（feature extraction）是特徵選取（feature selection）的延伸，簡單地說，我們希望將資料群由高維度的空間中投影到低維度的空間，因此，我們必須找出一組基底向量（base）來進行線性座標轉換，使得轉換後的座標，能夠符合某一些特性。 我們可以將特徵粹取分成「包含類別資訊」和「不包含類別資訊」兩大類。包含類別資訊指的是我們已經知道哪些資料分別歸屬於哪一類；而不包含類別資訊的特徵粹取則適用於我們不知道手上的資料點分別該歸屬於哪一類，甚至連該劃分成幾類都不知道的情況。對於這兩大類資料，可以分述如下： 對於「不包含類別資訊」的資料，我們通常使用「主要分量分析」（principal component analysis），簡稱 PCA。 (Cuiqu features (feature extraction) is feature selection (feature selection) extension, simply put, we want to base the projection data the high-dimensional space to low-dimensional space, so we have to find a set of basis vectors ( base) to the linear coordinate conversion, so that after the coordinate conversion, can meet a number of features. We can feature Cuiqu into " category contains information" and " does not contain a category of information," two categories. Contains categories of information refers to what we already know what kind of information are attributed to Cuiqu features include categories of information are not applicable to information we do not know the points are in the hands of the home in which category, and even that is divided into several class do not know the situation. For these two categories of information, can be divided as follows: For information " does not include the categories of information" , we usually use the " )

Mimo with Rayleigh channel and Maximum Likelihood