final-project

说明：  matlab科学计算继续上传，一共10个章节，由浅入深，几乎包括了绝大部分的科学计算方法以及仿真。本人把部分算法做了优化，更为完善(matlab scientific computing continue to upload, a total 10 chapters, easy-to-digest, almost, including most of the scientific method of calculation and simulation. I have done a part of algorithm optimization, a more perfect)

matlab code for Image overlay

Now interchange the “b” and “w” in the two words “be” and “we.” In splicing, be careful that you capture all important perceptual cues of the two phonemes. What is the effect? Can you interchange the two words by interchanging the two initial phones?

This is book aboud Mobile Broadband Including WiMAX and LTE in English version

学习matlab的ppt课件（华中科技大学的内部资料）(Ppt of learning courseware matlab (Huazhong University of Science and Technology)

图论算法及其对应的matlab源代码，可以当做练习教材使用，其中主要包含了网络最大流等(Graph algorithm and its corresponding Matlab source code, can be used as practice materials to use, which contains a maximum flow in networks)

MATLAB PROGRAM FOR BISECTION METHOD

分形学习进行轴承故障诊断及各个震动数据的数据降维的方法(Fractal study conducted bearing fault diagnosis and the various vibration data dimensionality reduction method)

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. )

Jacobi迭代法、Gauss-Seidel迭代法、超松弛迭代法（SOR方法）分别求解线性方程组的近似解(Jacobi iteration, Gauss-Seidel iteration, SOR (SOR method) for solving linear equations are approximate solutions)