ARMA_predict

covolutional turbo coides

MATLAB 图像处理命令：对matlab图像处理命令进行详细的全面阐述，并举事例说明。对初学者极为好的帮助。(MATLAB image processing orders : Matlab image processing orders for a detailed comprehensive exposition both examples. Very good for beginners to help.)

高铁信道模型Qianliu模型仿真,自相关函数与clarke模型自相关函数的比较(Qianliu channel model for high-speed railway compared with Clarke s correlated function)

基础的BP神经网络应用。附有注释，适合初学，简单易懂。(basic utilities of BP neural networks.)

利用单电容进行主动均衡,实现各个电池单体电压的一致(active balance)

An implementation of Lucas-Kanade optical flow method for 3-D images(This is an implementation of Lucas-Kanade optical flow method for three dimensional images like NIFTI, DICOM etc. A demo with test dataset is given.)

用MATLAB实现图像的直方图均衡化的处理，使图像更分明。(MATLAB image histogram equalization processing, make the image more clearly.)

孙志忠编写的《偏微分方程数值解法》一书中例题1.1.3的程序。用紧差分格式解常微分方程边值问题。(SUN Zhi-zhong prepared, )

ＭＡＴＬＡＢ仿真Ｖ形振子的相关数据　电流　方向性图　(MATLAB simulation of V-shaped vibrator relevant data current directional map)

In statistics, an expectation-maximization (EM) algorithm is a method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM is an iterative method which alternates between performing an expectation (E) step, which computes the expectation of the log-likelihood evaluated using the current estimate for the latent variables, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.