particle

Modeling for ISAR using MATLAB

分类周期法计算功率谱，求FMCW 雷达回波中距离信息(The classification cycle method to calculate the power spectrum, seeking the FMCW radar echo in the distance information)

zemax与matlab调用程序，当您用matlab分析复杂数据时，可以利用zemax光路计算工具提供原始数据，由matlab分析，并且可以驱动zemax优化，对自由曲面的优化更为重要，此代码为matlab调用zemax的函数库。(ZEMAX and Matlab : A Powerful Pair ZEMAX features a built-in DDE (Dynamic Data Exchange) server that allows other Windows® applications access to ZEMAX functions. Programming DDE applications can be somewhat complex if you are working in a compiled language such as C++. An alternative is to use one of the scripting languages such as Tcl, Python or Matlab. Matlab is a great choice since it is much more than a scripting language. Matlab provides a wealth of functions for processing and visualizing scientific and engineering data and there are toolboxes for Matlab that extend the basic capabilities into a wide variety of more specialized areas and disciplines. )

说明：  matlab dct－lms自适应算法仿真例子，该例子做了dct－lms和lms的比较。(Matlab Extra-LMS adaptive algorithm simulation example, the example done Extra-LMS and LMS comparison.)

模糊度窄带，分为为单频信号，线性调频，双曲调频信号。通过模糊度椭圆计算时间分辨率和频率分辨率。(Narrowband ambiguity, divided into single-frequency signal, chirp, HFM signal. Computing time resolution and frequency resolution through ambiguity ellipse.)

arima代码具体实现 包括平稳性检验 差分 以及预测 数据不在压缩包中 使用者可根据自身情况使用(The specific implementation of the ARIMA code includes the stationary test difference and prediction. Data is not used in compressed packets, users can use them according to their own circumstances.)

此文章先从理论上说明了OFDM系统的实质，然后构造了OFDM系统的MATLAB仿真(This article first describes the OFDM system in theory, in real terms, and then construct a MATLAB simulation of OFDM systems)

modification for curvelet code running

HMM,DBN Function matlab tool box

EM 算法是Dempster，Laind，Rubin于1977年提出的求参数极大似然估计的一种方法，它可以从非完整数据集中对参数进行 MLE估计，是一种非常简单实用的学习算法。这种方法可以广泛地应用于处理缺损数据、截尾数据以及带有噪声等所谓的不完全数据，可以具体来说，我们可以利用EM算法来填充样本中的缺失数据、发现隐藏变量的值、估计HMM中的参数、估计有限混合分布中的参数以及可以进行无监督聚类等等。(EM algorithm is a method for maximum likelihood estimation of parameters proposed by Dempster, Laind and Rubin in 1977. It can estimate parameters from incomplete data sets by MLE. It is a very simple and practical learning algorithm. This method can be widely used in dealing with the so-called incomplete data such as defective data, censored data and noisy data. Specifically, we can use EM algorithm to fill missing data in samples, find hidden variable values, estimate parameters in HMM, estimate parameters in finite mixed distribution, and so on. Unsupervised clustering and so on.)