WaveletMcM

这是一个牛顿插值的例子。输入差值点，计算4次牛顿插值函数值。(This is an example of Newton interpolation. Enter the difference points to calculate the value of 4 Newton interpolation function.)

比较不同信道下的OFDM符号同步的算法，包括高斯，多径信道等。(Comparison under different channel OFDM symbol synchronization algorithms, including Gaussian, such as multi-path channel.)

Buck modeling Matlab/Simulink

天线的方向图仿真，并且给出了各种加窗情况，此外仿真了多信号情况(Antenna pattern simulation, and gives a windowed, in addition to the simulation of multi-signal)

MATLAB二叉树生成，可以生成平衡和非平衡二叉树(MATLAB binary generation, can generate balanced and unbalanced binary tree)

对小波分解进行了解决，从一维二维来进行了相关的分析，一维进行了具体程序的的编写，根据小波分析算法来分解，对于二维的情况，调用MATLAB来进行了相关的测试，对lena图像进行了小波的分解机合成(Wavelet decomposition of the solution, from a two-dimensional analysis, the specific procedures for the preparation of one-dimensional wavelet analysis to break down, for the two-dimensional case, calling MATLAB to the test lena image wavelet decomposition machine synthesis)

字符识别，MATLAB程序的。可以运行。基于神经网络(Character recognition)

本书为石端伟编写的《机械动力学》，介绍了机械动力学相关内容，单自由度、多自由度的响应分析，并附有matlab源代码。(Duan-Wei Shi written book for the " mechanical dynamics," describes the mechanical dynamics relevant content, single degree of freedom, more freedom of response analysis, together with the matlab source code.)

A nice and easy object tracking by A. Bhargav Anand.

小波变换进行变速箱故障诊断 首先将s1和s2信号时域谱（s1代表维修前的振动信号，s2代表维修后的振动信号），画出来进行对比，会发现s1信号的振动幅值会比较高；然后对s1和s2分别进行小波分解并画出各层的时域谱，只对比s1和s2分后的最高时域谱，s2更加的具有周期性，且幅值较小，s2周期性不够明显，且振动过于密集。分析完时域谱后开始分析频谱，首先对原信号s1和s2fft转换，得到频谱图，如图，然后再对小波分析后高频部分进行fft转换，做出频谱图。对比后我们发现，在f 2375HZ出（也就是齿轮的啮合频率），s1信号频谱幅值会有急剧的升高，然后，其谐波的幅值也会增大。所以可以推断出该维修前的增速箱的齿轮可能出现问题。(Gearbox fault diagnosis based on wavelet transform s1 and s2 first time domain signal spectrum (s1 behalf of the vibration signal before maintenance, vibration signal s2 representative of repaired), drawn comparison, you will find a signal s1 vibration amplitude will be higher then s1 and s2 respectively wavelet decomposition and draw the layers of time-domain spectrum to only a maximum time domain spectroscopy s1 and s2 points after, s2 is more cyclical and smaller magnitude, s2 cyclical enough obviously, and the vibration is too dense. End domain spectral analysis after the beginning of the spectrum analysis, the first of the original signal s1 and s2fft conversion to give the spectrum, as shown, and then after the high frequency part of wavelet analysis fft transformation to make spectrum. We found that after the comparison, at f 2375HZ out (i.e. the gear mesh frequency), there will be a sharp increase in the amplitude spectrum of the signal s1, and then, the amplitude of the ha)