Mindlin

级联积分梳妆滤波器的补偿滤波器，altera公司IP(altera cic compensate filter)

说明：  特征检测，Loy and Zelinski s fast radial feature detector.(Loy and Zelinski s fast radial feature detector.)

尤拉法解ode,張鵬永老師傳授之數值方法(Euler method for solving ode, Zhang Pengyong teacher)

亚波长孔阵列色散方程亚波长孔阵列色散方程亚波长孔阵列色散方程(Subwavelength hole arrays dispersion equation subwavelength long hole array dispersion equation subwavelength array of long hole dispersion equation)

波尔兹曼格子法计算流体分析的matlab实例(Lattice Boltzmann method matlab examples)

前后向平滑MUSIC算法。通过平滑，提高了分辨率(Before and after smoothing MUSIC algorithm. By smoothing to improve the resolution)

说明：  本程序是一个相对完整的matlab simulink 光伏发电系统的仿真，MPPT运行了扰动法，可以为有相关需要的同学提供一定的借鉴。(This program is a relatively complete simulation of MATLAB Simulink photovoltaic power generation system. MPPT runs the perturbation method, which can provide some reference for students who need it.)

说明：  信号带宽30MHz,输入采样率96MHz,输出采样率32MHz，抽取倍数为3的数字下变频DDC的Matlab仿真程序(Signal bandwidth of 30MHz, the input sample rate of 96MHz, the output sampling rate of 32MHz, extracted 3 times DDC DDC in the Matlab simulation program)

matlab节点电压法算电路电压，很简单的学习资料(matlab calculating circuit voltage of the node voltage method, a very simple learning materials)

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 )