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Matlab图形图像处理函数详细列表,包括所有的Matlab图形图像处理函数.(Matlab graphic image processing function, a detailed list, including all of the Matlab graphics image processing function.)

弹出式tooltips，codeproject上面一个国外人写的(Pop-up tooltips)

对于一个随机的非高斯信号或者一个确定信号有很多的信息（远不止功率谱...）。本工具用来分析高阶谱和时频分布。(For a random signal or a set of non-Gaussian signals have a lot of information (far more than the power spectrum ...)。 This tool is used to analyze higher-order spectra and time-frequency distribution.)

这篇文章也很不错，是有关DVV滴定的，最近一直在看，希望对感兴趣的朋友有帮助！谢谢！(This article is also very good, is about the DVV titration, has recently been looking at, want to help friends interested! Thank you!)

Batch Least Square ALgorithm

network coding matlab example

说明：  文件包含PID多种算法，相当齐全，并已通过系统仿真测试(PID file contains a variety of algorithms, is quite comprehensive, and has been tested by simulation)

传统的短时谱估计语音增强算法通常假设语音谱分量相互独立，没有考虑语音谱分量间的相关性。针对这 一问题，该文提出一种新的基于多元Laplace分布模型的短时谱估计算法。首先，假设语音的离散余弦变换(DCT) 系数服从多元Laplace分布，以此利用谱分量间的相关性；在此基础上，利用多元随机矢量的高斯尺度混合模型表 示，推导得到语音DCT系数矢量的最小均方误差(MMSE)估计的解析表达式；并进一步推导了基于该分布模型的 语音存在概率，对最小均方误差估计子进行修正。实验结果表明，该算法在抑制背景噪声和减少语音失真等方面优 于传统的语音增强方法。(The spectral components of speech are usually assumed to be independent in traditional short-time spectrum estimation, which is not the case in practice. Tosolve this problem, a new speech enhancement algorithm with multivariate Laplace speech model is proposed in this paper. Firstly, the speech Discrete Cosine Transform (DCT) coefficients are modeled by a multivariate Laplace distribution, so the correlations between speech spectral components can be exploited. And then a Minimum-Mean-Square-Error (MMSE) estimator based on the proposed model is derived using a Gaussian scale mixture representation of random vectors. Furthermore, the speech presence uncertainty with the new model is derived to modify the MMSE estimator. Experimental results show that the developed method has better noise suppression performance and lower speech distortion compared to the traditional speech enhancement method. )

OFDM for BER using qpsk

1) 已知x=[1:15], y=[12 34 56 78 99 123 165 198 243 277 353 345 303 288 275], 用多项式进行拟合，给出拟合多项式，(所选拟合多项式阶数不同，拟合结果不同，请你选择适当的阶数进行拟合)，并绘出原始数据点、拟合曲线和95％置信区间（confidence bounds）的图形 . 2) 根据下面数学模型建立仿真模型，并在一个示波器中把它们的波形仿真出来。 (1) are known to x = [1:15], y = [12 34 56 78 99 123 165 198 243 277 353 345 303 288 275], with polynomial fitting, given the polynomial fitting, (the election order polynomial fitting different fitting results to differ, please select the appropriate order of fit), and draw original data points, fitting curve and 95 confidence interval (confidence bounds) graphics .2 ) In accordance with the following mathematical model for the establishment of simulation model, and in an oscilloscope waveform in their simulation out.)