00466915

bone modelling for analysis

这个是KPCA的matlab工具包，台湾的一个大学开发，能够实现KPCA的基本功能(This is a matlab toolkit KPCA, Taiwan, a university development, to achieve the basic functions of KPCA)

Thi is a Binary BAT algorithm

straight line using matlab

计算多重分形非趋势波动分析matlab程序,能量谱分析计算,GSM中GMSK调制信号的产生。( Calculation multifractal detrended fluctuation analysis matlab program, Energy spectrum analysis and calculation, GSM is GMSK modulation signal generation.)

精通MATLAB综合辅导与指南的配套磁盘程序,本书介绍了MATLAB一般原理和基本运算，重点介绍了MATLAB图形用户界面的结构和使用方法，包括二维、三维图形的控制、色彩的配置、用户专用界面的创建。(Proficient in MATLAB comprehensive guide to counseling and supporting disk procedures, this book introduces MATLAB general principles and basic computing, focuses on the MATLAB graphical user interface structure and the use of methods, including two-dimensional, three-dimensional graphics control, color configuration, user-specific interface to create.)

说明：  MATLAB 模式分类算法程序，是英文版《模式分类》教程的有关资料。(MatLab Classification　)

LDA的学习资料的合计！有MATLAB的实现例子！(Total LDA learning information! MATLAB examples of realization!)

研究LDPC码的EXIT性能经典论文汇总，包括早期EXIT和PEXIT，以及部分关于LDPC编码领域的论文(Research on the performance of the LDPC code EXIT classic paper summary, including early EXIT and PEXIT, and some papers on the LDPC encoding field)

独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S + N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...( Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S+ N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...)