parcorr

BodyBuilder is a MATLAB-based application that provides a graphical means to create Body Blocks for use in SimMechanics. It is hoped that the interested users will also use BodyBuilder to explore MATLAB s Object-Oriented Programming Language, which is part of the general release of MATLAB as of R2008a. Indeed, users are encouraged to extend BodyBuilder to serve their own needs. Some ideas for Customizing BodyBuilder are provided in the User s Guide.

Matlab for LLC resonant converter!

雷达电子对抗中的无源干扰的重要形式之一——箔条干扰(chaff jamming)

说明：  经典DSP的C程序和汇编程序库,有很多实用的程序供大家参考，很有用的！(Classical DSP C language program and assembler language libary. It has many practical programe for all of us. It is very useful.)

基于canny边缘检测，模糊c均值聚类的图像分割(Canny edge detection based on fuzzy c-means clustering image segmentation)

kalman filter implementation for matlab

这是通信系统仿真的第六章的例题的仿真程序代码！挺好的，希望对大家有用！(This is a communication system simulation examples of Chapter VI of the simulation program code! Very good, and I hope useful for everyone!)

计算Einstein场方程的mathematica程序包。输入变量和度规。自动计算里奇张量和曲率张量。( calculate Ricci Tensor and the Curvature Scalar)

利用MATLAB实现的最小包围矩形算法，精简实用(MATLAB implementation of the minimum bounding rectangle algorithm)

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. )