teachyouhowtouseMatlab

fitting an elips using matalb

按照定义法产生FARIMA时间序列，经过HURST参数估计，产生的结果是具有长相关特性的时间序列(FARIMA time series, in accordance with the definition of law after HURST parameter estimates, the result of long correlation properties of time series)

时间序列根据小波熵算法进行分段，以达到降维的目的(The time series is segmented according to the wavelet entropy algorithm)

otsu的matlab程序，图像处理学中常用的操作，全局阈值图像二值化(Otsu MATLAB procedure, commonly used in image processing operations, global threshold image binarization)

说明：  实现Jakes模型的快衰落仿真，希望对大家的学习有很好的帮助，看过之后给些意见(Jakes model to achieve fast fading simulation, we hope to learn a very good help, give some advice after reading )

胡 广 书 数 字 信 号 处 理 matlab 源 码(Hu-book digital signal processing matlab source)

有关阵列天线的matlab仿真， 1、给出阵元的个数，阵元的间隔以及波长 2、计算方向图并绘制不同的方向图，有规律的排列 3、计算出主瓣宽度，3dB带宽，主瓣与第一旁瓣的幅度比（以dB作单位）(Array antenna matlab simulation, 1, to the number troupe, interval, and the wavelength of the array element 2, calculate the pattern and draw a different pattern, a regular arrangement of 3, calculate the width of the main lobe, 3-dB bandwidth of the main lobe and the first side lobe amplitude ratio (in dB as a unit))

Jakes仿真建模的基本程序，主要就是模型的构建程序，MATLAB环境下进行，有一定参考价值(Jakes simulation modeling program, model building program MATLAB environment, has a certain reference value)

灰色预测模糊PID算法实现，该算法效果显著(Grey fuzzy PID algorithm, the algorithm results are obvious)

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.)