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DateConvert：简单而快速的日期转换 DateConvert转换DATESTR（0）格式的日期向量或序列号和日期向后。 Matlab的DATENUM，DATEVEC和DATESTR接受各种不同意见，但结果他们是缓慢的。如果输入格式正是众所周知，转换可以更快： DATENUM（ 17 - 10 - 2009 11时08分23秒 ）：2.97秒 DateConvert（ 17 - 10 - 2009 11时08分23秒 ， 号码 ）：0.039秒 =>“1.3％（1000环，1.5GHz的奔腾- M的，利用Matlab 7.7） DateConvert不*不*验证作为Matlab的日期函数输入日期： DATEVEC（ 32 - 2 - 2008 25:62:63 ）的答复[2008 3 4 2 2 7] DateConvert（ 32 - 2 - 2008 25:61:67 ， 载体 ）的答复[2008 2 32 25 61 67]！ 因此DateConvert几乎微不足道，但对速度进行了优化。我需要时间排序200 000邮票，并从217个减少到11秒的时间。 请运行单元测试TestDateVec检查的有效性和速度。 测试：利用Matlab 6.5，7.7，7.8，winxp的，32位。 纯Matlab代码，所以兼容性的MacOS，Linux操作系统，64位预期。 英语月份名称是必要的。(DateConvert: Simple but fast date conversion DateConvert converts the DATESTR(0) format to a date vector or serial date number and backwards. Matlab s DATENUM, DATEVEC and DATESTR accept a variety of inputs, but in consequence they are slow. If the input format is exactly known, the conversion can be much faster: DATENUM( 17-Oct-2009 11:08:23 ): 2.97 sec DateConvert( 17-Oct-2009 11:08:23 , number ): 0.039 sec => 1.3 (1000 loops, 1.5GHz Pentium-M, Matlab 7.7) DateConvert does*not* validate the input date as Matlab s date functions: DATEVEC( 32-Feb-2008 25:62:63 ) replies [2008 3 4 2 2 7], DateConvert( 32-Feb-2008 25:61:67 , vector ) replies [2008 2 32 25 61 67] ! So DateConvert is nearly trivial, but optimized for speed. I needed it for sorting 200 000 time stamps and reduced the time from 217 to 11 sec. Please run the unit-test TestDateVec to check validity and speed. Tested: Matlab 6.5, 7.7, 7.8, WinXP, 32bit. Pure Matlab code, so compat)

npc 3-level inverter spwm matlab

kalman 滤波器，初学者的参考,最好先学习一下其推到过程(kalman filter kalman filterkalman filterkalman filterkalman filterkalman filter)

电力电子三相桥式整流matlab仿真电路(Three-phase bridge rectifier power electronic circuit simulation matlab)

zxing相关的二维码开发类库的编译工具，最新版本的apache-ant-1.9.3(zxing related to the development of libraries of the two-dimensional code compilation tools, the latest version of apache-ant-1.9.3)

基于动态矩阵控制算法的模型预测控制， 预测控制(Dynamic Matrix Control Algorithm Based on the model predictive control, predictive control)

matlab matlab 的锐形(matlab matlab anatase form)

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

IMAGE STEGNOGRAPHY LKE THIS CHEC IT ADVANTAGE USING ATLAB.VERY USEFULL

图像的点运算：各种直方图的实现，直方图规定化，直方图均衡化。(Image point operation: all kinds of the realization of the histogram, histogram specification, histogram equalization.)