HuffmanCoding

Finite Difference Programming for Matlab in 2D (Poisuille flow)

file include model file of matlab that contains rayleigh fading environment in bpsk

功率谱估计的应用范围很广，在各学科和应用领域中受到了极大的重视。在《现代信号处理》课程中讲述了经典谱估计和现代谱估计这两大类谱估计方法；经典谱估计是基于傅立叶变换的，虽然具有运算效率高的优点，但是频谱分辨率低同时旁瓣泄漏严重，对长序列有着良好的估计。为了克服经典谱估计的缺点，人们开展了对现代谱估计方法的研究。现代谱估计是以随机过程的参数模型为基础的，有最大似然估计法、最大熵法、AR模型法、预测滤波器法。现代谱估计对短序列的估计精度高，同经典谱估计互为补充。在认真学习了现 代谱估计方法后，我选择了现代谱估计中的AR模型法的仿真作为题目。下面给出AR模型的相关理论和仿真实现。(Power Spectral Estimation of very extensive, in all disciplines and fields of application of a great deal of attention. The "modern signal processing" on the curriculum of classical and modern spectral estimation spectrum is estimated that the two types of spectrum estimation method; Classical spectrum estimation is based on Fourier transform, although high computing efficiency advantages, but also low-resolution spectrum Sidelobe serious leakage of long sequences have good estimates. In order to overcome the classic shortcomings of the spectrum estimation, there have been a pair of modern spectral estimation methods. Modern spectral estimation is the random process model parameters based on the maximum likelihood estimation, maximum entropy method, AR model, forecast filter. Spe)

This function performs "region growing" in an image from a specified seedpoint (x,y)

C语言写的基2fft算法，测试过了结果，比matlab的结果精度还是稍差(fft algorithm in C language.Have testd the result.)

Matlab的常用函数，查阅十分方便，不用再翻阅书籍那么麻烦(Matlab commonly used functions, access is very convenient, no longer need to read books so much trouble)

一篇很好的关于肌电信号的综述性文章，希望对大家有用(A good overview of the EMG signal on the article, I hope to be useful)

对输入的语音信号运用短时能量函数进行去静音，然后把去掉静音帧之后的信号进行重组。(remove the mute of the speech signal (using short-term energy function), and then remove the signal mute frame after restructuring.)

实习K——L分析，如有不足之处还请大家指点。进一步完成，给大家使用。(Practice K- L analysis, if the inadequacies also please advice. More complete, for everyone to use. )

37724108Volterra-Matlab混沌时间序列的Volterra一步预测的Matlab程序(37724108Volterra-Matlab chaotic time series prediction Volterra step of Matlab procedures)