LDA

共有5个程序，分别是从文件提取散点画球（椭球），散点画压力云图，散点画散点位置及速度分布图，散点画密度分布图，以及从数据文件中提取特定散点的程序，这是一套程序，可对大量散点数据进行处理。(There are five programs, which are extracted from the file powder stippling ball (ellipsoid), scattered cloud pressure stipple, stipple scatter scattered distribution of position and velocity, bulk density stipple maps and data files extracted from the scatter-specific procedures This is a process, a large number of discrete data can be processed.)

说明：  CMMB基带发射端MATLAB仿真。有助于理解CMMB物理层结构(CMMB baseband transmitter data simulation,)

在XP中应用WEBBRORSER的程序,实现XP的风格.很实用的(in XP WEBBRORSER the application procedures, XP style. Very useful)

包含了MATLAB编写的关于自适应算法的MATLAB程序，运行正确(Includes adaptive algorithms written in MATLAB on the MATLAB program, run right)

过程控制与simulink仿真的一本较好的书籍，欢迎下载(A better process control simulink simulation books are welcome to download)

计算亚波长光栅衍射效率,PBS,即偏振光分离(calculate subwave grating diffraction efficiency)

说明：  卷积编码程序。用matlab编写，希望对大家有用。(Convolutional coding and decoding process. Prepared using matlab, we want to be useful.)

获取图象并直接在GUI的界面上显示，Matlab代码实现了一个调制的GUI界面，实现BPSK调制算法的源代码。(To acquire images directly in the GUI interface, Matlab code implements a GUI interface modulation BPSK modulation algorithm source code.)

应用于地震勘探领域的资料处理，第一代相干算法处理。(Used in seismic exploration in the field of data processing, the first generation of coherent algorithm processing.)

这是用身高体重数据进行性别分类的实验。 用最小错误率贝叶斯分类器决策时，首先通过比较概率大小判断一个体重身高二维向量代表的人是男是女，然后再逐一与已知性别的数据比较，就可以得到错误率的统计。然后改变先验概率，重复上面的过程，观察数据结果的变化。 用最小风险贝叶斯分类器决策时，首先求出用最小错误率贝叶斯分类器得到的条件概率；然后根据人为给定的决策表，根据公式算出条件风险；然后逐一比较条件风险，找出使条件风险最小的决策（也就是分类）。最后用分类得到的结果逐一比较已经知道的原始数据，统计处错误率。 (This is the height and weight data for gender classification experiment. With the minimum error rate Bayesian classifier decisions , first by comparing the probability of the size and weight to height to determine a person represented by two-dimensional vector is male or female , and then one by one with known gender data comparison, the statistical error rate can be . Then change the prior probability , repeat the above process , the results of the changes observed data . Bayesian classifier with the minimum risk decision-making , first find the minimum error rate using Bayesian classifier to get the conditional probability then artificially given decision table , according to the formula to calculate conditional risk and then one by one more conditional risk , to find ambassador to the conditions of minimum risk decision making (ie classification) . Finally, the results obtained with the classification by-side comparison of the raw data have been aware of SD error rate . )