GMMs

该程序涉及了一阶系统、二阶系统的阶跃响应下的系统辨识方法。(The procedure involves first-order system, second-order system step response of system identification methods.)

智能天线程序,关于天线的方向图,和方向因子,增益等仿真(Smart antenna program on the antenna pattern, and direction factor, gain, etc. Simulation)

The kernel learning approach method is producing qualitatively and quantitatively satisfactory SR results.

This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. Before getting to a description of PCA, this tutorial first introduces mathematical concepts that will be used in PCA. It covers standard deviation, covariance, eigenvectors and eigenvalues. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.

一种实用的交互式多模型跟踪算法IMM，真实好用，测试过(A practical interactive multi model tracking algorithm IMM, which is true and useful and tested)

an other test for hinfini

it is useful for my research

变步长CMA算法程序，效果比较好，其他算法可以在此基础上修改(variable step-size CMA blind equalization algorithm )

产生随机数的方法很多，很多软件自己就有产生随机数函数，本程序用的是乘同余法产生随机数，经对比，效果比matlab自带的要好。(Many methods of generating random Numbers, this procedure is by using congruence method generating random Numbers.)

Function reconstructs an estimate of a surface from its surface normals by correlating the surface normals with that those of a bank of shapelet basis functions. The correlation results are summed to produce the reconstruction. The sumation of shapelet basis functions results in an implicit integration of the surface while enforcing surface continuity. Note that the reconstruction is only valid up to a scale factor (which can be corrected for). However the reconstruction process is very robust to noise and to missing data values. Reconstructions (up to positive/negative shape ambiguity) are possible where there is an ambiguity of pi in tilt values. Low quality reconstructions are also possible with just slant, or just tilt data alone. However, if you have full gradient information you are better off with the Frankot Chellappa algorithm below.