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stats 用于检验回归模型的统计量，有三个数值：相关系数r2、F值、与F对应的概率p.相关系数r2越接近1，说明回归方程越显著；F > F1-α（k，n-k-1）时拒绝H0，F越大，说明回归方程越显著；与F对应的概率p 时拒绝H0，回归模型成立.(stats for the test statistic of the regression model, there are three values: the correlation coefficient r2, F value, and F corresponds to the probability p. The correlation coefficient r2 more close to 1, note the more significant regression equation F> F1-α (k, nk-1) refused to H0, F the bigger shows the more significant regression equation F corresponds with the probability p refused H0, set up a regression model.)

hht变换源码，里面讲的很详细希望对大家有所帮助(hht transform source Inside and very comprehensive description of the procedures，For beginners helpful learning materials，I hope everyone likes)

通信原理的好书，一步步matlab讲解，由浅入深，值得看~(Communication theory books, matlab explain step by step, progressive approach, it is worth watching ~)

产生一均匀分布的白噪声信产生均匀分布的白噪信号，使均值为0，功率为p号u(n),画出其波形，并检验其分布情况，产生零均值功率0.1且服从高斯分布的白噪声，sinc信号，chirp信号，线性卷积(Generate uniformly distributed one letter of the white noise generated uniformly distributed white noise signal, so that the mean is 0, the power for the p number u (n), draw their waveforms, and test its distribution, resulting in zero average power of 0.1 and subject to Gaussian distribution of white noise, sinc signals, chirp signals, the linear convolution)

matlab ofdm自适应算法程序(matlab ofdm adaptive algorithm)

根据角速率计算 挠曲 该代码只是个计算的例子,提供方法.传递对准中的挠曲和柑臂计算,(Deflection angle rate calculated according to the code is just a calculation example, provide a way. Transfer alignment and orange arm deflection calculation)

说明：  关联规则分析问题的一种方法 apriori在MATLAB下的编写 (Association Rules in a way the problem apriori prepared under the MATLAB)

16QAM盲均衡,恒模算法，400次仿真，做出星座图，误差曲线(blind equalizaton for 16QAM,constant modulus algorithm，realization 400times ,constellation ,mmse curve)

SIFT代码，包括detector & descriptor.Matlab版本，内有详细的说明文档。值得一看(SIFT code, including detector )

RELIABLE automatic recognition of persons has long been an attractive goal. As in all pattern recognition problems, the key issue is the relation between inter-class and intra-class variability: