matlab

用相位相关方法做的图象拼接程序,可进行图象拼接(Phase correlation method used to do image stitching process, images can be spliced)

实现Nakagami信道仿真,用在无线传输信道中(Achieve Nakagami channel simulation, using the wireless transmission channel)

本代码通过对基因库中人类和家牛基因编码部分的对比和统计,分析了不同属生物间的基因遗传的异同和特性(This part of the code by comparing the gene encoding the human and cattle library chain genes were compared and statistics, and then to analyze the similarities and differences between different genera of biological and genetic characteristics of genetic)

浙大蔡登，何晓飞写的降维，特征选择等机器学习的源码 包括：谱回归，降维，特征选择，主题模型，矩阵分解，稀疏编码，哈希，聚类，主动学习，矩阵学习。 是一个很好的机器学习源码资料。(cCaideng s code for Machine learning,include Spectral regression : (a regression framework for efficient dimensionality reduction) Dimensionality reduction (Subspace learning) Feature selection Topic modeling and GMM Matrix factorization Sparse coding Hashing Clustering Active learning Ranking and Metric learning )

说明：  本科毕业设计，基于FDTD法研究移动通信终端电磁辐射对人体的影响，内含毕业论文，论文对程序有详细的解释说明，包含人体受电磁辐射的SAR值二维渲染图，供参考。(Undergraduate graduation design, based on FDTD method to study the impact of electromagnetic radiation on the human body of mobile communication terminals, including graduation thesis, the paper has a detailed explanation of the program, including the two-dimensional rendering of the SAR value of the human body by electromagnetic radiation, for reference.)

说明：  利用高斯回归进行数据预测，有实例和注释，可供参考学习(Using Gaussian regression to predict data, there are examples)

finite element source code using matlab (beam, plates, shells)

现有的代数特征的抽取方法绝大多数采用一维的方法，即首先将图像转换为一维向量，再用主分量分析（PCA）,Fisher线性鉴别分析（LDA）,Fisherfaces式核主分量分析（KPCA）等方法抽取特征，然后用适合的分类器分类。针对一维方法维数过高，计算量大，协方差矩阵常常是奇异矩阵等不足，提出了二维的图像特征抽取方法，计算量小，协方差矩阵一般是可逆的，且识别率较高。(existing algebra feature extraction method using a majority of the peacekeepers, First images will be converted into one-dimensional vector, and then principal component analysis (PCA), Fisher Linear Discriminant Analysis (LDA), Fisherfaces audits principal component analysis (KPCA), and other selected characteristics, then use the appropriate classification for classification. Victoria against an excessive dimension method, calculation, covariance matrix is often inadequate singular matrix, a two-dimensional image feature extraction method, a small amount of covariance matrix is usually reversible, and the recognition rate higher.)

Ordinary and Partial Differential Equation Routines in C, C++, Fortran, Java, Maple, and MATLAB

Fuzzy c means + hmf implenentation