sparseae_exercise

10个基本阵列，方向60 ，80，85,120,130 通用INR=5dB，用music，capon，线性预测，波束形成等四种算法画出功率谱图。(10 basic arrays, direction 60, 80,85,120,130 common INR = 5dB, with music, capon, linear prediction, beamforming and other four algorithms to draw power spectrum.)

简单的matlab gui图片文字显示程序(Simple matlab gui text image display program)

uwb无线电基础代码，第八章，超宽带信道与接收机。(based on UWB radio code, Chapter VIII, ultra-wideband channel and receiver.)

下载的，求方程组的程序，可以借鉴下，挺好的(Downloaded, find the program equations, can learn, the very good)

FC HBA API的接口实现,这个实现可以满足很多做光纤卡的app级应用开发。(FC HBAAPI implementation )

一个MATLAB 的图像处理和灰度变换的程序，这个是老师留的作业，里面有详细的讲解过程，非常的详细，对做图像的朋友非常有用(A MATLAB image processing and gray-scale transformation program, this is the teacher to stay the operation, which explain in detail the process, very detailed images of a friend is very useful to do)

采用模糊聚类算法和加核模糊聚类算法进行医学图像的分割。采用matlab编程，界面处理较好。 (Using fuzzy clustering algorithm and processing of nuclear fuzzy clustering algorithm for medical image segmentation. Using matlab programming, interface, better handling.)

此类代码产生1023位的gold码，gold码由m序列优选对(PN code acquisition and synchronization matlab)

compare fourier transform atrribute

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 )