GSO2

说明：  数学建模算法全收录，囊括了常用的基本算法，很是实用，推荐给大家(mathematical modeling algorithm, commonly used to include the basic algorithm, it is useful to recommend to everyone)

direct kinematic of movil robot

应用matlab简单实现镜头畸变的校正，可用于cmos ccd(Application of a simple implementation of matlab lens distortion correction, can be used for cmos ccd)

说明：  本人经过对最小二乘法拟合程序进行了归纳汇总编写了这个MATLAB小程序，一般情况都适用。(General least squares fitting procedure code)

粒子群算法 很实在的程序敬请大家下载。保证不会后悔(PSO program is really please you to download. Guaranteed not to regret)

考虑L的三个不同值：L＝256（3个数据段），L＝128(7个数据段)和L=64（15个数据段）。各自的谱估计图如上图所示。可以明显的看到，加窗明显的减小了频谱上的假谱峰，但也更加进一步平滑了谱峰。所以，对于L＝64的情况，在ω＝0.8π的谱线可以很确定的辨认，但是那两个靠近的谱峰不容易区分。对于L＝128的情况，这种情况提供了在分离和检测间最好的均衡。当然，对于在L＝256时的情况，效果是更好的，能够从谱估计图上明显的分辨出三条谱线的存在以及它们幅度关系的强弱。 除了Welch法外，还可以采用对多个周期图求平均的功率谱估计方法的其他方法如Bartlett法等等，在功率谱估计上也能取得较好的结果。(consider three different values : L = 256 (3 data), L = 128 (7 data) and L = 64 (15 data). Their spectral estimation map as shown in Fig. It is clear to see that increase significantly decreasing window of the spectrum of false peaks, but much more to further smooth the peaks. Therefore, L = 64, in the case = 0.8 spectrum can determine the identification, but that the two peaks around is not easy to distinguish. L = 128 for the situation, which provided the separation and detection among the best balanced. Of course, L = 256 in the case, the effect is better, from the spectral estimation map clearly distinguish between the three lines of the existence and their relationship to the rate of strength. In addition to Welch law, but also can be used to map multiple cycle for the average power)

验证时域采样定理,奈奎斯特定律,奈奎斯特频率(The time domain sampling theorem)

matlab代码实现一个快速时钟恢复的代码，这里可以看到一个具体的clock and data recovery的例子(it is about clock and data recovery，is a very useful tools to simulate the problems in communication!)

数值分析里面的高斯消元法利用全主元消去的程序(Numerical analysis method which use the Gaussian elimination procedure all main element elimination)

Emd分解，通过峭度和相关系数选择IMF，进行信号重构，小波分解，小波包分解。(Emd decomposition, selecting IMF by kurtosis and correlation coefficient, signal reconstruction, wavelet decomposition, wavelet packet decomposition.)