MHE

360全景相机拍摄的图片用matlab展开成普通图片(360 panoramic camera images using matlab expanded into ordinary pictures)

进行逐步线性回归，采用的是脉冲对消法，使用起来非常方便，分析了该信号的时域、频域、倒谱，循环谱等,正确率可以达到98％，有小波分析的盲信号处理，包含特征值与特征向量的提取、训练样本以及最后的识别。( Stepwise linear regression, It uses a pulse of consumer law, Very convenient to use, Analysis of the signal time domain, frequency domain, cepstrum, cyclic spectrum, etc. Accuracy can reach 98 , There Wavelet Analysis Blind Signal Processing, Contains the eigenvalue and eigenvector extraction, the training sample, and the final recognition.)

说明：  《matlab时频分析技术及其应用》该书中的源代码。matlab源码。(this is a code of the book called matlab time-frequency analesis.)

This program lets you put one image into the same voxel space as another image. E.g. you might want to extract a Brodmann area from the brodmann.img file that comes with MRIcro, and then put that set of 1x1x1mm voxels into the same voxel space as your functional data, which probably covers a smaller volume and has larger voxels. Then you can use the Brodmann area as an ROI to explore your functional results.

在认知网络下的干扰对齐算法的MATLAB代码(In the cognitive network interference alignment algorithm MATLAB code)

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 )

说明：  认知场景下的功率分配问题源码，这时要考虑次用户对主用户的干扰(Cognitive power allocation scenarios source, this time to consider the sub-user interference of the primary user)

full brididge dc dc converter

使用Matlab代码和Simulink做音频的滤波后傅里叶分析及相应滤波器分析(Using MATLAB code and Simulink to do fitlered audio file Fourier anlysis and specifications anlysis of the corresponding filter.)

电源-地同步噪声电磁仿真程序，用FDTD编写的，可以参考一下，终端匹配(applies finite difference time domain method to analyze transmission coefficient.)