3d-Radon-and-its-inverse

关于压缩感知的一篇经典文章，值得看看 (Introduction about compression perception of classic procedures a classic articles, worth looking at)

合成孔径雷达回波模拟器的建模仿真与系统设计，首先介绍了合成孔径雷达的高分辨力成像原理、SAR回波理论模型和几种SAR原始回波数据模拟算法。然后采用距离时域脉冲相干法对点目标的原始回波数据进行了模拟，并用经典RD算法对该回波进行处理，得到了期 望的结果，本文还用同样的方法对点阵目标的原始回波数据进行了模拟。 (Synthetic aperture radar echo simulator modeling and simulation system design, first introduced high resolution synthetic aperture radar imaging principle, the the SAR echo theoretical model and several SAR raw echo data simulation algorithm. Domain pulse coherent method and then using the distance of the original point target echo data simulation, and the echo processing using the classic RD algorithm to get the desired results, this paper also used the same way the original dot target back wave data were simulated.)

计算用于双目视觉的基本矩阵，以此进行三维重构和坐标点测量(calculate F)

用matlab编写的压缩解压缩程序，基于小波变换的是零树算法。(prepared using Matlab compression decompression procedures, based on wavelet transform is zero tree algorithm.)

说明：  这是一个改进了的模糊聚类算法程序，请放心下载，已调试过(This is an improved fuzzy clustering algorithm program, please rest assured that download has been debugged)

这是Mann－kendall趋势检验和sen斜率估计的matlab实现的源代码，与大家共享！(This is the Mann-kendall trend test and the estimated slope sen achieved Matlab source code and share!)

在matlab中，数值计算的理论和分析。(In matlab, the theoretical and numerical analysis.)

给定一个普通多项式p(x)，用列向量表示，阶次从高到低排列，此函数返回p(x)的切比雪夫多项式展开的系数，返回的的结果也是一个列向量，同样按切比雪夫多项式的阶次从高到低排列(Given a general polynomial p (x), with a column vector representation, the order highest to lowest, the function returns p (x) Chebyshev polynomial expansion coefficients of the returned result is a column vector, the same press cut the order highest to lowest Chebyshev polynomials)

说明：  BA无标度网络的算法，主要是演示无标度网络是怎么样生成新节点的(BA scale-free network algorithm, the main is to demonstrate how the scale-free network to generate a new kind of node)

说明：  matlab应用实例：基于神经网络的分类，用的是iris数据集做例子(matlab applications: classification based on neural network, using the iris data set an example)