matlab_c++

Those who are working on power system optimisatio, for them here is all the load flow programs from book hadi sadat. It i the 1st part. Second part is also uploaded in this site by me

Fast mean of rectangular subarrays (MEX)

说明：  自适应遗传算法MATLAB源程序，供遗传算法初学者学习(Adaptive Genetic Algorithm MATLAB source code for genetic algorithm for beginners to learn)

Matlab fdtd （时域有限差分）一维，二维，三维集合(Matlab fdtd (FDTD) one-dimensional, two-dimensional, three-dimensional collection)

this is an method for mode estimation with random decerment technique for ambient techniqe with wavevelet method

EMD的实例测试函数，自己修改过的，可以显示imf分量频谱(Examples of EMD test function, with my own modify, you can display imf component spectrum)

In this paper, we show how support vector machine (SVM) can be employed as a powerful tool for $k$-nearest neighbor (kNN) classifier. A novel multi-class dimensionality reduction approach, Discriminant Analysis via Support Vectors (SVDA), is introduced by using the SVM. The kernel mapping idea is used to derive the non-linear version, Kernel Discriminant via Support Vectors (SVKD). In SVDA, only support vectors are involved to obtain the transformation matrix. Thus, the computational complexity can be greatly reduced for kernel based feature extraction. Experiments carried out on several standard databases show a clear improvement on LDA-based recognition

Simulation of a plane wave impinging on a dielectric cylinder using FDTD

语音识别基于dtw算法,可以识别特定的汉字，里面有10个字的模板(speech recognition)

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. )