cyclostationspectrum-estimation-

说明：  用matlab软件计算电力系统的潮流，使电力系统运行可靠(Power systems using matlab software to calculate the trend, so that the power system is reliable, )

说明：  用matlab调制解调2fsk信号,以及相应的作图方法(2fsk signal modulation and demodulation using matlab, and the corresponding mapping method)

QAM modulation scheme for spreading codes gray

数字散斑相关(DSCM)程序，可以精确算出前后两幅图片的位移，精确到亚像素(Digital speckle correlation (DSCM) program, you can accurately calculate the two pictures before and after the displacement, to the sub-pixel precision)

现代通信系统matlab版随书代码，很好用的(Modern communication system with matlab edition book code )

相位恢复算法，里面的滤波器用Matlab编写(phase recovery algorithm)

MFCC参数和LPCC参数需要频繁使用的语音的说话人识别分析还需要使用大量的这些特征参数,这就需要研究MFCC和LPCC算法和参数提取的原则。因为我们需要了解与人交谈的过程中,我们要进行建模 率不构成线性关系，而更偏向于对数关系，这就使得人耳对低频信号比高频信号更敏感。Mel频率的提出是为了方便人耳对不同频率语音的感知特性的研究。频率与Mel频率的转换公式为(MFCC parameters and LPCC parameters are parameters of speaker recognition which requires frequent use of voice analysis also need to use a lot of these characteristic parameters, This requires research MFCC and LPCC algorithm and the principle of parameter extraction. Because we need to understand the process of talking to people, we want to conduct a speaker modeling the perception of the human ear does not constitute a linear relationship with frequency, and prefer to logarithmic relationship, which makes the human ear is sensitive to low frequency signal is better than high frequency signal. Mel frequency is put forward in order to facilitate the ear of the study of speech perception characteristics of different frequency. For frequency and Mel frequency conversion formula)

在matlab环境中自动识别连通区域的大小,从先验概率中采样，计算权重，小波包分析提取振动信号中的特征频率。( Automatic identification in the matlab environment the size of the connected area, Sampling a priori probability, calculate the weight, Wavelet packet analysis to extract vibration signal characteristic frequency.)

在matlab环境下产生指数衰减信号，从硬盘读取数据，对信号进行正交检波(in Matlab environment under exponential signal from the hard disk to read data, the detection signal orthogonal)

Artificial Neural Networks