RoboticToolbox

小波分析，基于DWT的故障信号特征提取，可以画出条形图，多层分解(Wavelet analysis, DWT-based feature extraction of fault signal can draw bar charts, multi-decomposition)

精通MATLAB图像处理，电子工业出版社， 该光盘文件中包括了全书所有实例对应的MATLAB M文件,以及用到的图片。所有代码按照章节存放在各个文件夹下，如“第2章”文件夹下存放了本书第2章所有的程序代码或实例代码,“第3章”文件夹下存放了第3章所有的实例代码，依此类推。(Proficient in MATLAB image processing, electronic publishing industry, the CD includes a book, document all instances of the corresponding MATLAB M files, and use the pictures. All of the code stored in accordance with sections in every folder, such as " Chapter 2" folder stored under Chapter 2 of this book to all of the program code or example code, " Chapter 3" folder stored under Chapter 3 of all example code, and so on.)

对随机信源进行16QAM调制，送入高斯信道，解调后计算误码率(Random source for 16QAM modulation, into the Gaussian channel, demodulation error rate were calculated)

水平集图像处理程序,利用一个新的变分公式进行图像处理，效果很好(Level set image processing program,we present a new variational formulation for geometric active contours that forces the level set function to be close to a signed distance function, and therefore completely eliminates the need of the costly re-initialization procedure.)

分数阶傅立叶变换，可用于LFM信号的相干源信号的估计(Fractional Fourier Transform can be used for LFM signal of the coherent source signal estimate)

求解真实商业周期模型的Matlab程序。(Benchmark Real Business Cycle Model by Henson 1985.DynaMo (version 2.0) is the latest version of DynaMo. In particular, this version contains is a menu driven Windows stand-alone application with advanced graphical user interfaces which allows you to solve and analyze selected standard dynamic stochastic general equilibrium models.)

matlab的m文件，是关于汉明码编码的，文件可以直接运行(matlab m-file is on the Hamming code, files can be run directly)

扩频通信系统仿真实验,　　　①传输的数据随机产生，要求采用频带传输（BPSK调制）； 　　　②扩频码要求采用周期为31的m序列； 　　　③仿真从基站发送数据到三个不同的用户，各不同用户分别进行数据接收； 　　　④设计三种不同的功率延迟分布，从基站到达三个不同的用户分别经过多径衰落（路径数分别为2，3，4）； ⑤三个用户接收端分别解出各自的数据并与发送前的数据进行差错比较。(Spread spectrum communication system simulation)

Linear Neighborhood Propagation 系数求解(Linear Neighborhood Propagation coefficients )

　支持向量机方法是建立在统计学习理论的VC 维理论和结构风险最小原理基础上的，根据有限的样本信息在模型的复杂性（即对特定训练样本的学习精度，Accuracy）和学习能力（即无错误地识别任意样本的能力）之间寻求最佳折衷，以期获得最好的推广能力[14]（或称泛化能力）。 (SVM is based on statistical learning theory and the theory of VC dimension based on structural risk minimization principle, according to the limited sample of information in the model complexity (ie, training samples of a specific learning accuracy, Accuracy) and learning ability (ie, error-free samples to identify any capacity) to find the best compromise between, in order to obtain the best generalization ability [14] (or generalization).)