litifangzhen

PSO_DynamicEnvironment是用粒子群算法（particle swarm algorithm）解决动态环境寻优（最优值不断变化）的问题。文件打开后运行PSO_DynamicEnvironment.m文件即可得到结果，代码中有详细注释，方便修改。运行示例已经保存为图片附在压缩包中。(PSO_DynamicEnvironment solve dynamic environment particle swarm optimization algorithm (particle swarm algorithm) (optimal value changing) problems. Run PSO_DynamicEnvironment.m file to get the file to open the result, the code has detailed notes, easy to modify. Run the sample has been saved as a picture attached to the compressed package.)

　　瑞利信道频率曲线和频谱图。 　　和相关瑞利信道的信息。 (Rayleigh channel frequency curve and spectrum diagram. And associated Rayleigh channel of information. )

通过应用高斯混合模型来实现语音识别，语音样本为短时语音(By applying a Gaussian mixture model to achieve speech recognition, voice samples of short-time speech)

说明：  Matlab支持向量机导论（中文版） 作者：【英】 Nello Cristianini ， John Shawe-Taylor 译者：李国正，王猛，曾华军 出版社：电子工业出版社 版次：2004年3月第1次印刷(Introduction to Matlab Support Vector Machine (Chinese Edition) Author: n. Nello Cristianini, John Shawe-Taylor Translator: Li Guo-Zheng, Wang Meng, who Huajun Publisher: Electronic Industry Press Revision: March 2004 1st Printing)

用Hausdorff距离对两角点集进行配准，得到点集间的仿射变换，从而实现图像的自动配准。此算法以角点作为Hausdorff距离的配准特征，与直接选用边缘来配准的方法相比较，大大减小计算量。(Hausdorff distance on the corners with a point set registration, be affine transformation between sets in order to achieve automatic image registration. This algorithm is based corner as the Hausdorff distance matching characteristics, and use Edge to direct registration method of comparison, greatly reduced computation.)

给出一些参考有关图像融合的参考文章，两片英文文献，两篇中文文献(Give some information about the article reference image fusion, two pieces of English literature, two Chinese literature)

Arquivo do Fastreport para implementaç ã o

16qam的例程仿真，并对理论的误码率进行对比。星座图，误码率曲线等(The routine simulation of 16QAM is carried out, and the theoretical BER is compared. Constellation diagram, BER curve, etc.)

海洋科学导论的书籍，对于搞SAR和海洋的人有帮助(Introduction to marine science books, for people who engage in SAR and ocean help)

用Matlab写的高斯混合模型的贝叶斯EM估计，(Variational Bayesian EM for Gaussian mixture models)