DL

主要为数据分析和统计，通过matlab代码，用MATLAB实现的压缩传感，毕业设计有用，虚拟力的无线传感网络覆盖，LDPC码的完整的编译码。( Mainly for data analysis and statistics, By matlab code, Using MATLAB compressed sensing, Graduation useful Virtual power wireless sensor network coverage, Complete codec LDPC code.)

matlab code for adaptive median filter

matlab socket example

说明：   用最速下降法求最优化解 输入：f为函数名 grad为梯度函数 x0为解的初值 TolX,TolFun分别为变量和函数的误差阈值 dist0为初始步长 MaxIter为最大迭代次数 输出: xo为取最小值的点 fo为最小的函数值 f0 = f(x(0))( Steepest Descent Method with Optimum Solution input: f as a function name grad is gradient function x0 for the solution of the initial TolX, TolFun variables and functions were error threshold dist0 as the initial step MaxIter maximum Diego passage number Output: xo to take the minimum point of fo is the smallest function value f0 = f (x (0)))

遗传代码遗传代码遗传代码遗传代码遗传代码遗传代码遗传代码遗传代码遗传代码遗传代码(Genetic code genetic code genetic code genetic code genetic code genetic code genetic code genetic code genetic code genetic code)

matlab节点电压法算电路电压，很简单的学习资料(matlab calculating circuit voltage of the node voltage method, a very simple learning materials)

计算方法课程中矩阵分解，牛顿迭代以及特征值求解的全部代码(Matrix decomposition method of calculating course, Newton s iterative eigenvalue solution and all code )

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. )

FSK编码器和译码器,有需要的朋友可以看看！(FSK encoder and decoder, there is a need friends can see!)

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