av_power

ROOT-MUSIC的matlab仿真源码，基于求根方式的music算法，避免了谱峰搜索，优化了计算复杂度(ROOT-MUSIC for matlab simulation source code, algorithm-based Roots music the way to avoid the peak search, optimization of the computational complexity)

音乐推荐系统，matlab教程，很不错的，有兴趣的可以看看(Music recommendation system, matlab tutorial, very good, interested can look at)

利用matlab写成的窄带噪声发生，最小均方误差（MMSE）的算法，ML法能够很好的估计信号的信噪比。( Using matlab written narrowband noise occurs, Minimum mean square error (MMSE) algorithm, ML estimation method can be a good signal to noise ratio.)

自己编写的关于MATLAB与c++混合编程的小程序。(program)

说明：  基本的粒子滤波算法，所用数值模型为带控制量的非线性模型，可修改噪声类型，并可和EKF相比较(Basic particle filter algorithm for the numerical model used to control the volume with the non-linear model, can modify the type of noise can be compared and the EKF)

基于无刷直流电机的simulink仿真图，可以通过示波器查看电机的反电动势波形和电流波形以及可以使电机转速达到期望值(Based on the simulink simulation diagram of the brushless DC motor, you can view the motor s back electromotive force waveform and current waveform through the oscilloscope and can make the motor speed reach the desired value)

利用matlab程序编写的经验模态分解，介绍了emd方法的应用(emd)

说明：  matlab详细函数总汇，为您写程序时不能想起的函数做好准备。(Details of function matlab pool of written procedures for your function can not think of preparing.)

比较生存曲线可以用统计假设测试日志秩检验。它是用来测试的零假设，没有任何区别的人口的生存曲线（即概率事件发生在任何时间点是相同的每个人口）.这个函数使用卡普兰曲线的程序来估计生存函数.(Comparison of two survival curves can be done using a statistical hypothesis test called the log rank test. It is used to test the null hypothesis that there is no difference between the population survival curves (i.e. the probability of an event occurring at any time point is the same for each population). This function use the Kaplan-Meier procedure to estimate the survival function, so it is mandatory to download )

该代码实现的是压缩感知理论中的信号恢复问题。将压缩感知理论中的信号恢复问题转化为带参数约束的回归问题，从而利用贝叶斯理论实现参数估计，从而得到高效的重建稀疏信号。(The code to achieve the signal recovery problems in the theory of compressed sensing. Recovery issues into regression problems with parameter constraints will signal compression perception theory, Bayesian theory parameter estimation, resulting in efficient reconstruction of sparse signal.)