ml

matlab编写滤波的源代码，小弟花了几天时间编写的，希望对大家有所帮助(matlab source code for the preparation of filtering, younger brother spent a few days to prepare, and I hope all of you to help)

一种用于图像超分辨率分析的混合高斯模型算法。可实现图像方法，同时保持很好的图像边缘。(For image super-resolution algorithm for Gaussian mixture model analysis. Image method can be realized while maintaining a good edge.)

MATLAB自带的积分函数，采用m文件编写。通用性好，方便新手分析其编程思路。(MATLAB built-in integration function, the use of m document preparation. Common good, to facilitate analysis of its new programming ideas.)

Sensorless Speed Control of Independent Dual-PMSM Drives Fed by a Five-Leg Inverter

基于奇异值分解的图像压缩。分别给出了灰度及RGB真彩图的压缩代码。　　 (Based on singular value decomposition of the image compression. Are given respectively grayscale and RGB true color pictures of the compression code.)

m现代通信系统－使用matlab,该书的所有程序(m modern communication systems- using matlab, the book all the procedures)

一种基于区域活动轮廓模型，通过引入局部的图像拟合能量来提取图像的局部信息，能够有效的额分割强度不均一的图片。较传统的水平集方法有更少的计算量(By introducing the local image fitting(LIFenergy to extract the local image information,our model is able to segment image with intensity inhomogeneities.)

Arnold置换图像置乱，用于数字水印；(Arnold Replacement Image Scrambling for Digital Watermarking )

迭代学习控制系统仿真及其PID参数整定-华东石油大学本科毕业论文 (Iterative Learning Control System Simulation and PID parameter tuning- East China Petroleum University undergraduate thesis)

半监督学习(Semi-Supervised Learning，SSL)是模式识别和机器学习领域研究的重点问题，是监督学习与无监督学习相结合的一种学习方法。半监督学习使用大量的未标记数据，以及同时使用标记数据，来进行模式识别工作。当使用半监督学习时，将会要求尽量少的人员来从事工作，同时，又能够带来比较高的准确性，因此，半监督学习目前正越来越受到人们的重视。(Semi-Supervised Learning (SSL) is a key issue in the field of pattern recognition and machine learning. It is a learning method combining supervised learning with unsupervised learning. Semi-supervised learning uses a large number of unlabeled data, as well as labeled data, for pattern recognition. When using semi-supervised learning, it will require as few people as possible to work, and at the same time, it can bring relatively high accuracy. Therefore, semi-supervised learning is receiving more and more attention.)