StaMPS_v3.3b1.tar

这是进行自适应均衡的关键一步 很重要哦!!!还有一部分内容没有上载(This is a key step adaptive equalizer is very important Oh!!! There are not a part of the content uploaded)

Shelliemers equation implementaion.

正则化切割图像，matlab代码实现，有样例可以直接运行。 (Regular cutting of the image, matlab code, and sample can be directly run.)

这份源代码是用粒子群算法解决0/1背包问题，可以正常运行。(The source code is the particle swarm algorithm to solve 0/1 knapsack problem, it works.)

说明：  双音频按键识别 背景： 打电话拨号时，每次按键会发出不同的声音，这些声音实际上是由一些固定频率的正弦信号合成的。因为每个按键声音包含两个不同频率的正弦信号，因此也被称为“双音频”信号，简记为DTMF。这相当于是对不同键位用‘音频’进行了编码，如同计算机领域中用数字0和1对处理对象（包括键盘）进行编码。(Two-tone buttons identify Background: When you call dial, each key emits different sounds, which is actually made up of some fixed frequency sinusoidal signal synthesis. Because the sound of each key contains two sinusoidal signals of different frequencies, it is also known as &quot two-tone&quot signal, abbreviated as DTMF. This is equivalent to different keys using the ' audio' encoded, as the computer industry with the numbers 0 and 1 pair of processing object (including the keyboard) to be encoded.)

大学课程数值计算引论中的matlab程序(Introduction to the numerical calculation matlab program)

通过matlab仿真分析了QAM 调制方式在AWGN信道下的误比特性能(By matlab simulation analysis of QAM modulation in AWGN channel BER performance)

Backward Step FDM is used to solve Steram Vorticity Function for a backward step problem

application of compression image

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 )