svpwm

Matlab编程学习课件，是初级学习非常重要(Matlab programming learning courseware, is the primary learning is important)

THIS IS A CURRENT DIFFERENTIAL PROTECTION SCHEME OF IEEE 39 BUS SYSTEM

灰色预测模型能够通过以往的数据预知未来的数据，准确率高，鲁棒性强(Gray prediction model can predict the future through the past data of data, high accuracy and robustness)

用matlab设计FIR和IIR滤波器的设计函数与例程(filter of iir and fir)

Finger region localisation

Arduino Uno Sketch to take measurements a strain gauge at precise depths into an elastic object. Interfaces the Uno with a micrometer using SPC and with strain gauge bridge circuit using Analog input pin. Tested to work on OSEPP Uno R3 Plus (Arduino Uno equivalent uC)

matlab编的4阶龙格库塔法解微分方程的程序(matlab Fourth-order Runge-Kutta method,)

矩量法及其在天线中的应用,西安电子科技大学科技大学的内部学习资料(Method of moments and its application in antennas, Xi' an University of Electronic Science and Technology University of Science and Technology within the learning materials)

基于交叉验证法小波降噪程序，主要应用白噪声(Cross-validation method based on wavelet noise reduction program, the main application of white noise)

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. )