yichangsuanfa

CMMB中校验矩阵的生成和相应的比特映射向量表的生成代码(CMMB in the check matrix generation and the corresponding bit-map generation code to Scale)

周期图法估计平稳随机信号功率谱的MATLAB实现 周期图法估计平稳随机信号功率谱的MATLAB实现(Periodogram method to estimate the power spectrum of the stationary random signal MATLAB implementation)

本程序实现轨迹生成。基于的原理是飞行器位置和姿态解耦，对其进行独立设计，通过反解出惯性器件信息，从而用于导航。(The program trajectory generation. Based on the principle of the aircraft position and attitude decoupling its independent design, the inverse solution inertial devices used for navigation.)

C语言编写的傅立叶正逆变换，经过测试与matlab里面的.m算法结果一致。还有数据测试可用。适用于工程信号处理中。(C language of the Fourier transform, After test and Matlab inside m algorithm results. There are data test available. Suitable for engineering signal processing.)

说明：  运用各种机器学习算法，检测人脸是否为照片(Various machine learning algorithms are used to detect whether a face is a photo)

Optilux是一个设计，模拟和分析光通信系统的开发工具包(Optilux is an collection of tools that provide advanced techniques to design, simulate, and analyze optical communication systems)

converter control pwm simuling

matlab deneme dosya sfunction

这是模式识别中最小错误率Bayes分类器设计方案。 自行完善了在不同先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。 全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。 调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。 调用最小错误率贝叶斯分类器决策子函数时，根据先验概率数组，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。 主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小错误率贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到不同先验概率条件下错误率的统计。 (This is the minimum error rate pattern recognition Bayes classifier design. Self- improvement prior probability in different conditions , male , female and total error rate error rate statistics , into which each array . All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions . Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector third step is to application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function . Call the minimum error rate decision Functions Bayesian)

calculate the air properties(input the parameters are relative humidity and dry bulb, if you input the above parameter that you can get another air properties)