CRC(Cyclical-Redundancy-Checking)

神经网络在手势识别中的应用及其相关的一些例子.(Neural network in gesture recognition application and its related some examples.)

利用Tikhonov正则化实现的超分辨率图像序列的重建，效果还好(Use Tikhonov regularization to achieve super-resolution image sequence reconstruction, better results)

网格编码8PSK系统中最佳码的研究与分析(Trellis Coded 8PSK system code to the best research and analysis)

matlab中用打靶法求解常微分方程组的两点边值问题(shooting method for bvps)

基于cvca模型的标准卡尔曼程序及误差分析，适用于学生(The standard model based on Kalman cvca procedures and error analysis, applies to students)

crross over and fittnes GA unit commitment

交错网格声波模拟，可展示波场快照，反映波传播的过程。(Staggered grid acoustic simulations, can show a snapshot of the wave field, reflecting the wave propagation process.)

efficient LBP Local binary patterns (LBP) is a type of feature used for classification in computer vision. LBP is the particular case of the Texture Spectrum model proposed in 1990.[1][2] LBP was first described in 1994.[3][4] It has since been found to be a powerful feature for texture classification it has further been determined that when LBP is combined with the Histogram of oriented gradients (HOG) descriptor, it improves the detection performance considerably on some dataset

互信息matlab, 混沌时间序列分析与预测工具箱 Version1.0 点这里下载: 混沌时间序列分析与预测工具箱 Version1.0 chaotic time series analysis and prediction matlab toolbox - version 1.0 1、该工具箱包括了混沌时间序列分析与预测的常用方法,有 (Mutual information matlab, chaotic time series analysis and forecast Version1.0 point toolbox here to download: Chaotic Time Series Analysis and Forecast of the toolbox Version1.0chaotic time series analysis and prediction matlab toolbox- version 1.01, the kit includes a chaotic time series analysis with the projected common method, there are)

Matlab中没有现成的Hermite插值函数，必须编写一个M文件实现插值。 设n个节点的数据以数组x0 （已知点的横坐标）， y0（函数值）， y1（导数值）输入（注意Matlat的数组下标从1开始）， m个插值点以数组x 输入，输出数组y 为m 个插值。编写一个名为hermite.m的M文件。 (Matlab is not off-the-shelf Hermite interpolation function, we must realize the preparation of a document M interpolation. N nodes for the data to an array x0 (known point of abscissa), y0 (function value), y1 (numerical derivative) input (note the subscript Matlat array starting at 1), m to the array of interpolation points x input and output array y for m months interpolation. Hermite.m the preparation of a M-file.)