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This is a MATLAB code for segmentation of any image. Initially the image is converted from RGB to greyscale image and then segmantation is done.

在Matlab的编程环境下实现了卷积编码和译码的功能，简单明了(In the Matlab programming environment to achieve a convolution coding and decoding functions, simple)

Copula函数的预测，预测相关系数和尾部相关系数值(Predictive correlation coefficient and tail correlation coefficient value)

purebasic的入门书籍，虽然用purebasic的人很少，甚至分类都没他。不过这个语言还是不错的。(purebasic entry books, although very few people with purebasic, or even no his classification. However, the language is good.)

这是一个卷积码的编码的源程序，适用于实现卷积码编码的MATLAB仿真(This is a convolution code coding the source program, suitable to achieve convolution code coding of MATLAB simulation.)

卡尔曼滤波器的c语言，已既matlab的实现(Kalman filter c language, has been the realization of both matlab)

基于字典学习的超分辨率例程，matlab编辑，对于理解超分辨率有一定帮助。(Dictionary-based super-resolution routine learning, matlab editor, for understanding the super-resolution of some help.)

说明：  近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。 多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的(In recent years, heuristic-based multi-objective optimization technology has been greatly developed, and research shows that this technology is more practical and efficient than classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, SPEA, SPEA2, PAES and PESA. Particle Swarm Optimization (PSO) algorithm is an evolutionary technology based on swarm intelligence that simulates social behavior. With its unique search mechanism, excellent convergence performance, and convenient computer implementation, it has been widely used in the field of engineering optimization. The objective PSO (MOPSO) algorithm is applied to different optimization fields [9~11], but it has shortcomings such as high computational complexity, low versatility, and poor convergence. The multi-objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos A. Coello Coello et al. in 2004)

Hapke2002年模型仿真，以及模型参数的反演。(Hapke model implementation)

一个matlab的程序用于，消除趋势项，平滑处理，和时域与频域积分的处理.(A matlab program is used to eliminate the tendency item, smoothing, and deal with the time domain and frequency domain integral.)