zuidas

数字信号处理matlab程序，可以做为参考。(Digital signal processing matlab program can serve as a reference.)

说明：  利用目标以前的坐标点完成目标未来未知位置的预测，分别利用一个到五个点来进行预测(Using the coordinates of points before the completion of target objectives of the forecast future unknown location, respectively, using 1-5 point forecast)

matlab经验模态分解 emd 工具包 包含emd常用函数，可用于机械设备故障诊断(Matlab empirical mode decomposition toolkit can be directly used in mechanical equipment fault diagnosis)

含有台湾大学某老师提出的果蝇优化算法文档及程序，读者可下载进行学习(Containing a fruit fly optimization algorithm document and program put forward by a teacher of National Taiwan University, readers can download and learn.)

MATLAB揭秘，是一本很好的MATLAB入门书。([MATLAB].David.McMahon)

PQ分解法求网络潮流，超详细的注解，可以仔细分析此程序，然后学着自己编写(PQ decomposition method the network trend, ultra-detailed annotations can carefully analyze this program, and then learn to write their own)

ambiguity function matlab code

arma 实现了数据从文件的输入，ar模型预测，arma模型预测，卡尔曼滤波器模型预测，利用图形用户界面编写-Realized the data from the file input, ar model predictions, arma model prediction, Kalman filter model predictions, using a graphical user interface for the preparation of(arma matlab实现了数据从文件的输入，ar模型预测，arma模型预测，卡尔曼滤波器模型预测，利用图形用户界面编写-Realized the data from the file input, ar model predictions, arma model prediction, Kalman filter model predictions, using a graphical user interface for the preparation of)

NOMA SIMUALATION in Matlab

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.)