Matlab4---Eshelby-Tensor-in-Penny-Shape-created-f

说明：  这是一个基于matlab开发的关于c语言的图形转换程序,可实现直方图的生成和传输(This is a development based on the Matlab language c graphics conversion program, the histogram can be realized generation and transmission)

matlab中仿真模拟leach算法，绘图结果正确(good leach)

计算一维光子晶体能带结构，斜向入射(含kz)的能带结构，内含TE/TM 两种源码(The matlab code for TE/TM band structure calculation)

压缩感知 重构算法 迂回式匹配追踪 论文 附带 matlab代码(Compressed sensing reconstruction algorithm matlab code circuitous track comes with matching paper)

除氧器计算,主要用于除氧器蒸汽耗量计算，简单准确(pipe design)

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. )

用matlab编程实现一维的卡尔曼滤波算法，原图是梯形(Matlab programming with one-dimensional Kalman filter algorithm, artwork is the trapezoidal)

转换色彩空间，实现各种图像的Compress-Decompress(Color space conversion, to achieve a variety of image Compress-Decompress)

VC++调用Matlab Builder for.NET 的COM组件(VC++ Call Matlab Builder for.NET the COM components)

集群（CR）的加固阶段进展集群分离自组织映射（SOM）的。(The Cluster Reinforcement (CR) phase advances cluster separation in Self Organizing Maps(SOM).)