Classic_installation_diagrams_install_MATLAB_Tutor

it is a introductory book that tells u abt the fundamentals of matlab programming.......

时频原子算法建立时频原子库，从而用多个时频原子分解信号的特征(Time-frequency atoms algorithms to establish frequency atom library with multiple frequency atom decomposition signal characteristics)

直方图提取函数，用于人脸识别中，输入一幅图像，函数内部对输入图像做短时傅里叶变换，得到四幅幅度图，再求四幅图的直方图特征，合并后输出(Histogram extraction functions for face recognition, the input image, the input image inside the function to do short-time Fourier transform, the four magnitude diagram, and then seek four graph histogram feature, the combined output)

拟合、插值、遗传算法、微粒群算法实例讲解,matlab(Fitting, interpolation, genetic algorithms, particle swarm optimization examples to explain)

对某一标准图像进行二维Mallat小波的3级分解，获得小波高/低频系数，分别 用低频分量LL1、LL2、LL3的小波系数进行图像重建，分别计算这三种情况 和原始图像比较的PSNR。(A standard image 3 2D Mallat wavelet decomposition, wavelet high/low frequency coefficients , respectively Low frequency component LL1 , LL2, LL3 wavelet coefficients for image reconstruction, respectively, to calculate the three And compared to the original image PSNR. )

产生QPSK信号，根据仿真的数据估计差错概率(Produce QPSK signal and estimate the data according to the error probability)

这个市一个ns环境下的leach算法！大家可以多下在啊！我想要一个matlab环境下的概算法！(1 ns of the city under the leach algorithm! We can do more in ah! I want an environment of Matlab budget for the law!)

对AR（4）模型的经典功率谱估计，并与真实功率谱作比较。采用了welch方法，协方差法，burg方法(For AR (4) model of the classic power spectrum estimation, and with true power spectrum for comparison. Welch method using covariance method, burg method)

一种复杂性测度，用来计算两个信号之间的交叉样本熵。(A kind of complexity measure to calculate the entropy of cross sample)

说明：  关于径向基函数神经网络、两个典型的反馈神经网络Hopfield网络、Elman网络的简单示例，适合初学者掌握径向基函数网络和反馈网络的神经网络工具箱函数，为全面学习这两类网络打下基础。(The simple examples of radial basis function neural network, two typical feedback neural networks Hopfield network and Elman network are suitable for beginners to master the neural network toolbox functions of radial basis function network and feedback network, which lays a foundation for comprehensive learning of these two types of networks.)