PB-OLE

应用MATlAB语言处理数字信号与数字图像，从书中提取出來的不分，对初学者来说很不国！(Application of MATLAB language processing digital signals and digital images, extracted from the book, with no distinction, for beginners is not the country!)

digital signal prossing

相空间重构,以及绘制相空间图. 程序并不复杂,但运行效果良好.(phase reconstitution, and phase graphic mapping.)

一个五阶的自回归短期预测模型的MATLAB程序(A AUTOREGRESSion model used to prediction)

相关分析过程的matlab方法，针对EMD方法的不足,自己编的5种调制信号。( Correlation analysis process matlab method, For lack of EMD, Own five modulation signal.)

采用经典FFT算法对dqpsk信号进行频率估计，并在高斯信道下绘制估计结果图。(The classical FFT algorithm is used to estimate the frequency of DQPSK signal, and the estimation result graph is drawn in the Gaussian channel.)

motion退化函数的matlab实现 脚本，自己定参数(Implementation of motion degradation function)

说明：  FQPSK与简单重复码的迭代译码,简单重复码做为外码(serial concatenation of repetition code and FQPSK ，in which repetition code is the out code)

说明：  《精通Matlab与C/C++混合程序设计》（ 第二章） 刘维编著("proficient Matlab and C/C mixed Program Design" (Chapter II) Liu compilation)

T=[1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] 输入向量的最大值和最小值 threshold=[0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net=newff(threshold,[31 3],{ tansig , logsig }, trainlm ) 训练次数为1000，训练目标为0.01，学习速率为0.1 net.trainParam.epochs=1000 net.trainParam.goal=0.01 LP.lr=0.1 net = train(net,P,T) 测试数据，和训练数据不一致 P_test=[0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 (T = [1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] ' of the maximum and minimum input vector threshold = [0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net = newff (threshold, [31 3], {' tansig' , ' logsig' }, ' trainlm' ) training times for the 1000 target of 0.01 training, learning rate of 0.1 net.trainParam.epochs = 1000 net. trainParam.goal = 0.01 LP.lr = 0.1 net = train (net, P, T) test data, and training data inconsistencies P_test = [0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 )