orbits

An instrumentation system applied to formation flight

主要讲解剪切波最新有关的资料或论文,其用于图像处理方面的应用(The main shear-wave on the latest relevant information or thesis, and its used for image processing applications)

MATLAB TOOLBOX FOR PETRI NETS

Addition and subtraction of signals matlab code

prsenting a single phase inverter simulation with pulse generation

simulink 实现了直接序列扩频通信系统，含详细模型图(simulink realization of the direct sequence spread spectrum communications systems, including a detailed model)

说明：  是一种关于无线传感器网络的质心定位程序的MATLAB原代码.希望对大家有帮助...(Is a wireless sensor network on the centroid position of the MATLAB program source code. I hope all of you help ...)

Matlab英文学习资料，里面有详细的Matlab功能和使用方法的介绍。(Learning Matlab)

一维连续功率谱分析 一维序列x(n)的连续功率谱分析，ol(0:m)频率，tl(0:m)周期，sl(0:m)连续功率谱，st95(0:m)红噪音或白噪音谱的95 置信上限，strw(0:m) 红噪音或白噪音的谱密度，其中m=[n/2.]。 (Continuous power spectral analysis of one-dimensional one-dimensional sequence x (n) of continuous power spectrum analysis, ol (0: m) frequency, tl (0: m) cycle, sl (0: m) continuous power spectrum, st95 (0: m ) red noise or white noise spectrum of the 95 confidence limit, strw (0: m) of red noise or white noise spectral density, where m = [n/2.].)

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. )