coilresistance

This is a Monte Carlo sampling matlab programming, hope you can use it, enjoy!

根据DFT的基二分解方法，可以发现在第L（L表示从左到右的运算级数，L＝1，2，3…M）级中，每个蝶形的两个输入数据相距B=2^(L-1)个点，同一旋转因子对应着间隔为2^L点的2^(M-L)个蝶形。从输入端开始，逐级进行，共进行M级运算。在进行L级运算时，依次求出个2^(L-1)不同的旋转因子，每求出一个旋转因子，就计算完它对应的所有的2^(M-L)个蝶形。因此我们可以用三重循环程序实现FFT变换。同一级中，每个蝶形的两个输入数据只对本蝶形有用，而且每个蝶形的输入、输出数据节点又同在一条水平线上，所以输出数据可以立即存入原输入数据所占用的存储单元。这种方法可称为原址计算，可节省大量的存储单元。附件包含算法流程图和源程序。(err)

说明：  三篇cs算法经典文章，cs算法是合成孔径雷达成像处理的常用算法之一。(3 cs algorithm classic article, cs algorithm is synthetic aperture radar imaging of the common algorithm.)

用matlab来实现各种滤波，如频域低通滤波，理想低通，巴特沃斯等。(Using matlab to achieve a variety of filters, such as frequency-domain low-pass filter,an ideal low-pass Butterworth and so on.)

说明：  决策树(Decision Tree）是在已知各种情况发生概率的基础上，通过构成决策树来求取净现值的期望值大于等于零的概率，评价项目风险，判断其可行性的决策分析方法，是直观运用概率分析的一种图解法。由于这种决策分支画成图形很像一棵树的枝干，故称决策树。在机器学习中，决策树是一个预测模型，他代表的是对象属性与对象值之间的一种映射关系。Entropy = 系统的凌乱程度，使用算法ID3, C4.5和C5.0生成树算法使用熵。这一度量是基于信息学理论中熵的概念。 决策树是一种树形结构，其中每个内部节点表示一个属性上的测试，每个分支代表一个测试输出，每个叶节点代表一种类别。 分类树（决策树）是一种十分常用的分类方法。它是一种监督学习，所谓监督学习就是给定一堆样本，每个样本都有一组属性和一个类别，这些类别是事先确定的，那么通过学习得到一个分类器，这个分类器能够对新出现的对象给出正确的分类。这样的机器学习就被称之为监督学习。(Decision tree is a decision analysis method based on the known probability of occurrence of various situations, which can calculate the probability that the expected value of net present value is greater than or equal to zero, evaluate the project risk and judge its feasibility by constructing a decision tree. It is a graphic method of intuitively using probability analysis. Because this kind of decision branch is drawn as a graph, it is very similar to the branch of a tree, so it is called decision tree. In machine learning, decision tree is a prediction model, which represents a mapping relationship between object attributes and object values. Entropy = the disorder degree of the system, using algorithms ID3, C4.5 and C5.0, spanning tree algorithm using entropy. This measure is based on the concept of entropy in information theory.)

说明：  低密度奇偶效驗碼利用硬式解碼再經過bsc通道下的錯誤率(Low-density parity codes using hard and then after bsc channel decoding error rate under the)

本书较详细地论述了信号的线性检测、假设检验理论、已知信号的检测，随机信号的检测、色高斯噪声中信号的检测、序列检测、恒虚警处理、非参量检测以及在线性处理时信号参量的估计和信号参量估计的一般理论等。(This book discusses in detail the signal linear detection, hypothesis testing theory, known signal detection, random signal detection, color gaussian noise of signal detection, sequence detection, constant false alarm processing, non parameter detection and the linear processing signal parameter estimation and signal parameter estimation of the general theory, etc.)

说明：  刘金琨所著《滑膜变结构matlab仿真》，学习滑膜经典教程(Matlab simulation of variable structure of synovium by Liu Jinkun, learning classic course of synovium)

标准粒子群优化算法1,采用MATLAB编写,本算法简单,易于实现,可调参数少,是非线性连续优化问题、组合优化问题(The standard particle swarm optimization algorithm 1,)

MUSIC算法matlab代码。文件中给出了基于MUSIC算法进行阵列信号估计的程序(MUSIC algorithm matlab code. Documents given array signal based on MUSIC algorithm for estimation procedure)