IMAGE--RESTORATION

提出了一种基于 JPEG/JPEG2000相结合的医学图像感兴趣区域压缩方法。该方法对在人为选定 医学图像的感兴趣区域采用无损的 JPEG2000压缩 ,而对其他图像区域则采用高压缩比的 JPEG压缩,较好地解决了医学图像的高压缩比和高质量之间的矛盾。(Based on a combination of JPEG/JPEG2000 region of interest of medical image compression method. The method of artificially selected region of interest of medical images using lossless JPEG2000 compression, and on other image regions using high compression ratio of JPEG compression, a better solution to the medical image and high-quality high-compression ratio between the contradictions.)

MATLAB类面向对象编程学习资料，是一个很不错的文章哟！！！(MATLAB object-oriented programming class learning materials, is a very good article yo! ! !)

创建了为主成分分析所需的数据矩阵，并通过主成分分析完成对所得数据的处理。(Analysis of the main ingredients to create the required data matrix, and principal component analysis of the data to complete the deal.)

MFCC program,is written by my son,you can use it very simply!

Matlab code for record tool for speech recognition

基础篇介绍基本功能和操作；中级篇介绍编程和数值计算；高级篇介绍仿真(The basis of articles to introduce the basic functions and operations intermediate chapter to introduce programming and numerical computation High chapter introduced the simulation)

说明：  是matlab语言，对阶跃折射率分布均匀光纤（圆柱型光纤）模型中的光场分布进行了模拟。(Is the matlab language, a step refractive index distribution of the fiber (cylinder-type fiber) model of optical field distribution are simulated.)

详解MATLAB在最优化计算中的应用中第11章的源码。没有的可以下载(Detailed MATLAB source in the optimization calculation of Chapter 11. Not available for download)

射频滤波器 利用综合优化后产生的耦合矩阵 生成在hfss中自动建模的脚本文件 (The coupling matrix is produced using the optimized integrated RF filters are generated in the HFSS script file automatically modeling)

粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhart博士和kennedy博士发明。源于对鸟群捕食的行为研究 PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域(Particle Swarm Optimization (PSO) is an evolutionary computation technique (evolutionary computation), has Dr. Eberhart and Dr. kennedy invention. Deriving from the behavior of birds of prey PSO with genetic algorithm is similar to an iterative optimization-based tools. System initialization for a group of random solutions, through the iterative search for optimal values. But there is no cross-genetic algorithm used (crossover) and mutation (mutation). But the particles in the solution space of the particles to follow the optimal search. In detail the steps after the introduction sections compared with the genetic algorithm, PSO has the advantage of being simple and easy and did not realize many of the parameters need to be adjusted. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications)