lte-turbo

MATLAB实现霍夫曼编码对英文文本的解压缩程序(MATLAB Huffman coding for the English version of decompression procedures)

非常不错的非线性非高斯环境下的粒子滤波程序(very good nonlinear non-Gaussian environment under the particle filter process)

Color correction for tungsten,daylight,fluorescent illuminant image

it is for simple hill climbing algorithm

说明：  三次样条差值—三转角法的仿真，用于数值分析(Cubic spline difference- three corner simulation method for numerical analysis)

这是本人在学习过程中总结的matlabbode图的绘制技巧，希望对大家有用(This is summarized in the learning process, I the matlabbode map drawing skills, and hope to be useful)

Stephen J.Chapman《Matlab编程(中文版)》 Stephen, Matlab编程 本文为Stephen J.Chapman《MATLAB编程（第二版）》英文影印版的中文译本，这本书对初学者很好的入门教材。 第一章 Matlab入门 第二章 Matlab基础 第三章 分支语句和编程设计 第四章 循环结构 第五章 自定义函数 第六章 复数数据、字符串和附加画图类 第七章 稀疏矩阵、单元阵列结构 第八章 输入输出函数 第九章 句柄图形 第十章 用户图形界面 (Stephen J. Chapman " Matlab programming (English Version)" Stephen, Matlab programming paper for the Stephen J. Chapman " MATLAB Programming (Second Edition)" English copy version of the Chinese translation of the book a good primer for beginners. Chapter I Introduction Chapter II Matlab Matlab based on Chapter III branch statements and Programming Chapter IV Chapter V loop structure defined functions Chapter VI complex data, strings and additional drawing classes Chapter VII of the sparse matrix, cell array structure of the eighth input and output functions of Chapter IX Chapter X Chapter Handle Graphics GUI)

自举是一种由重采样估计，独立和（蒙特卡洛重采样）等概率设置一个单一的数据统计变化的一个途径。允许的措施估计那里的潜在分布是未知的或者样本量很小。他们的结果与这些分析方法的统计特性相一致。 在这里，我们使用非参数逼近。非参数引导更简单。它不使用该模型的结构，建造人工数据。矢量[易西]是重采样，而不是直接与replecement。这些参数是从这些对构建。 二，回归模型时，应使用在回归方程中的两个变量是随机的，会有错误的，即不是由研究者控制。模式，我用普通最小二乘回归低估了变量之间的错误时，他们都含有线性关系的斜率。据索卡尔和罗尔夫（1995），模型二回归的主题是一对哪些研究和争论仍在继续，最终建议是很难做出。 BOOTGMREGRESS模型II是一个引导程序。这需要s引导和规范样品前坡计算变量。这两个变量的每个转化为具有零均值和标准差的一个。由此产生的斜率是线性回归系数的Y在X里克创造了这个词（1973）几何平均数并给出了一个模型II回归广泛审查。它也被称为引导标准的主要轴线(The bootstrap is a way of estimating the variability of a statistic from a single data set by resampling it independently and with equal probabilities (Monte Carlo resampling). Allows the estimation of measures where the underlying distribution is unknown or where sample sizes are small. Their results are consistent with the statistical properties of those analytical methods. Here, we use the Non-parametric Bootstrap. Non-parametric bootstrap is simpler. It does not use the structure of the model to construct artificial data. The vector [yi, xi] is instead directly resampled with replecement. The parameters are constructed from these pairs. Model II regression should be used when the two variables in the regression equation are random and subject to error, i.e. not controlled by the researcher. Model I regression using ordinary least squares underestimates the slope of the linear relationship between the variables when they both contain error. According to Sokal and Rohlf (1995), t)

求解三体问题，并画动态图像。同时含有解释三体问题的pdf文件(Solving the three-body problem, and draw a dynamic image. Also contains a pdf file explaining the three-body problem)

Spectrum Analysis using FFT