gongcheng4fsk

胡广书 数字信号处理 理论算法与实现 第二版(HU-book theory of digital signal processing algorithms with the second version)

在一段语音信号上加上高斯白噪声，产生任意信噪比的程序(A voice signal with white Gaussian noise to produce any signal to noise ratio of the program)

Matlab conding for image Processing

获取图片，算灰度，比较，UV-VIS，自动生成表格(UV-VIS picture catch)

主要用于样本的划分，有K-S和R-S两种方法(Mainly used for sample classification, there are two ways to KS and RS)

多带KALMAN滤波，是不错的程序，有兴趣的可以下了(Multi-band Kalman filtering, is a good procedure, interested parties can have a)

This tutorial is the next of before, you will learn a few of the basic functions of Matlab. First we will start working with basic mathematical functions, setting variables, and generating time series.

NormFitGA:用动态线性标定适应值的遗传算法求解一维无约束优化问题 (NormFitGA: dynamic linear calibration with genetic algorithms for solving one-dimensional fitness unconstrained optimization problems)

· 2PSK仿真系统编程。调制、信道、检测。 · 误码率仿真。 (2PSK 仿真)

这是模式识别中最小风险Bayes分类器的设计方案。在参考例程的情况下，自行完善了在一定先验概率的条件下，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。 调用最小风险贝叶斯分类器决策子函数时，根据先验概率，再根据自行给出的5*5的决策表，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，放入决策数组中。 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”，调用最小风险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，就可以得到在一定先验概率条件下，决策表中不同决策的错误率的统计。 (This is a pattern recognition classifier minimum risk Bayes design .In reference to the case of routine , self- improvement in a certain a priori probability 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 The third step is the 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 . Bayesian classifier )