Mom_comput_Vdipole

linear quadratic model

通过输入区间端点a和b以及精度，初始等分数和最大外推层数来近似f(x)在a到b上的积分！(By entering a and b as well as the interval endpoint accuracy, the initial fraction and the like to approximate the maximum number of layers extrapolation f (x) at the points a to the b!)

simulink仿真框图来实现BPSK调制(simulation simulink block diagram to realize BPSK modulation)

在matlab中，用小波实现语音去噪。噪声为正态随机噪声(In matlab, the Speech Denoising by Wavelet. Noise for the normal random noise)

NAVIER_SOLUTION.m for rectangular plate

关于RAKE接收机的三种合并方式的性能比较的matlab仿真程序，对接收信号进行MRC、EGC、SC合并的输出信噪比比较。(To compare the performance of the merger of three methods on the receiving end of RAKE receiver: MRC, EGC, SC with the output signal to noise ratio comparison )

drive design for induction machine

电力系统暂态稳定程序，可以进行暂态稳定计算,是学习PCA特征提取的很好的学习资料，pwm整流器的建模仿真。( Power System Transient Stability Program, can be transient stability, Is a good learning materials to learn PCA feature extraction, Modeling and simulation pwm rectifie.)

说明：  雷达信号相关内容，已打包成函数，bpsk，fsk，lfm等(Radar signal related content, packed into functions, BPSK, FSK, LFM, etc)

EMD的Toolbox及使用方法 经验模态分解（Empirical Mode Decomposition, 简称EMD）是由美国NASA的黄锷博士提出的一种信号分析方法.它依据数据自身的时间尺度特征来进行信号分解, 无须预先设定任何基函数。这一点与建立在先验性的谐波基函数和小波基函数上的傅里叶分解与小波分解方法具有本质性的差别。正是由于这样的特点, EMD 方法在理论上可以应用于任何类型的信号的分解, 因而在处理非平稳及非线性数据上, 具有非常明显的优势。所以, EMD方法一经提出就在不同的工程领域得到了迅速有效的应用, 例如用在海洋、大气、天体观测资料与地震记录分析、机械故障诊断、密频动力系统的阻尼识别以及大型土木工程结构的模态参数识别方面。本工具书主要分以下几个部分：导论、非稳态信号介绍、一维时频信号分解、二维时频信号分解、时频影像信息提取等。 (Time frequency toolbox for use with MATLAB The Time-Frequency Toolbox has been mainly developed under the auspices of the French CNRS (Centre National de la Recherche Scientifique). It results from a research effort conducted within its Groupements de Recherche ”Traitement du Signal et Images” (O. Macchi) and ”Information, Signal et Images” (J.-M. Chassery). Parts of the Toolbox have also been developed at Rice University, when one of the authors (PG) was visiting the Department of Electrical and Computer Engineering, supported by NSF. Supporting institutions are gratefully acknowledged, as well as M. Guglielmi, M. Najim, R. Settineri, R.G. Baraniuk, M. Chausse, D. Roche, E. Chassande-Mottin, O. Michel and P. Abry for their help at different phases of the development.)