code.tar

基于支持向量机分类器的活动形状模型。学术论文。(Support vector machine classifier based on the activities of the shape model. Papers.)

在大多数中研究伯肃叶流动用格子boltzmann方法去扩展值得实践新的研究方法(In most studies of leaf movement of Peter Stephen lattice boltzmann method used should be to expand the practice of new research methods)

用于说话人识别初始化样本的聚类算法 调试成功(Speaker Recognition for initialization of the clustering algorithm samples)

本代码主要讲述了抛物型偏微分方程的向前Euler格式的差分解法。代码简单，高效。(This code mainly tells the story of parabolic partial differential equations of Euler forward format of finite difference method. The code is simple, efficient.)

说明：  使用蒙特卡罗方法计算n维球体的体积，很有趣的程序(An interesting program for calculating the volume of n-dimensional spheres by Monte Carlo method)

这是我从一本基于MATLAB实现的数值分析算法的书中自带的光盘程序，都是可以套用的算法。都是用MATLAB实现的算法(I take it from a MATLAB-based algorithm to achieve the numerical analysis of the book comes with a CD-ROM program, the algorithm can be applied. Are realized by MATLAB algorithms)

说明：  DSP入门：应用傅里叶变换对信号进行频谱分析(DSP basis: Implement of Fourier Transfer to analysis signals spectrum)

为作业最终的仿真结果与分析 PL_free.m：free space path loss model PL_logdist.m: log-distance path loss model PL_lognorm.m: log-normal shadowing path loss model plot_PL_general.m: plot the figures itum.m: ITU M.1225 vehicular-B(120km/hr) channel model AWGN.m: create AWGN channel object(The final for the job simulation results and analysis PL_free.m: free space path loss model PL_logdist.m: log-distance path loss model PL_lognorm.m: log-normal shadowing path loss model plot_PL_general.m: plot the figures itum.m: ITU M.1225 vehicular-B (120km/hr) channel model AWGN.m: create AWGN channel object)

包括主成分分析、因子分析、贝叶斯分析，包括 MUSIC算法，ESPRIT算法 ROOT-MUSIC算法，是机器学习的例程，基于chebyshev的水声信号分析，在MATLAB中求图像纹理特征，阐述了负荷预测的应用研究,是学习PCA特征提取的很好的学习资料，使用混沌与分形分析的例程。( Including principal component analysis, factor analysis, Bayesian analysis, Including the MUSIC algorithm, ESPRIT algorithm ROOT-MUSIC algorithm, Machine learning routines, Based chebyshev underwater acoustic signal analysis, In the MATLAB image texture feature, It describes the application of load forecasting, Is a good learning materials to learn PCA feature extraction, Use Chaos and fractal analysis routines.)

Image classification project