matlabxiaobofenxigaojijishudequanbuyuanma

LMS算法，可以实现在计算机上的仿真，通过仿真可以清楚的了解结果(LMS)

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)

通信系统中对快速傅里叶变换的MATLAB仿真程序(Communication systems in the fast Fourier transform MATLAB simulation program)

pca算法，有详细注释，通过光谱区分苹果酸甜度，对于想学习pca算法的初学者，有很大帮助(pca algorithm, detailed notes, by spectroscopic distinguish sour apple degree, want to learn pca algorithm for beginners, there are a great help)

current compensator control

自己编写的matlabanli，其中包含二维MUSIC和CAPON算法，及其改进算法，对研究有一定帮助(Own prepared matlabanli, which contains two-dimensional MUSIC and CAPON algorithm, and its improved algorithm, the study has some help)

我们导师编写的802.15.3A程序，相对IEEE的程序做了相当大的简化，很值得参考(We tutor prepared 802.15.3a procedure, the relative IEEE procedures to do a considerable simplification, it is worth considering)

用线性插值法求单摆运动周期的近似解,及做图比较.(Find by linear interpolation of the approximate solution pendulum movement cycle, and do Charts.)

createimage函数实现当前零水平集（演化曲线）在原图上的显示和嵌入函数u的重新初始(Createimage functions implementing the current zero level set (curve evolution in the display on the original) and inline function u initial again )

智能控制中的编程例子，有关于模糊控制的。(Intelligent control of the programming examples on the fuzzy control.)