lid_driven_flowLBMMatlab

说明：  利用凯泽窗产生FIR滤波器，可以直接使用，不用其它头文件。(err)

水果图像识别，利用MATLAB进行图形的特征提取，边缘检测，阈值分割等(Fruit and image recognition, the use of MATLAB for graphics feature extraction, edge detection, thresholding, etc.)

电子工业出版社2005=小波分析理论与MATLAB7实现.-[483p](Wavelet analysis MATLAB7)

这是世界名曲“卡农”的matlab版本，采用m编程语言，歌曲优美动听。在学习之余不防静静聆听洗涤内心的浮躁。(This is the world famous " Canon" matlab version, the m programming language, beautiful sounds of the song. In the study anti quietly listening to the washing inner impetuous.)

将蚁群算法应用到无人机中,实现无人机的航迹任务规划(The ant colony algorithm is applied to UAV, UAV flight path planning task)

自己写的matlab小程序，用于解线性方程的根。可以参考下(Their own small programs written in matlab for solving linear equations of the root. Can refer to)

演示龙格库塔方法的绝对稳定区域，从1阶到4阶，且阶数可以调整。另外，区域的左右边界可以计算出来(Absolutely stable region demonstrates Runge-Kutta methods, from an order to the fourth order, and the order can be adjusted. Further, around the boundary region can be calculated)

YALMIP是一种定义和求解高级优化问题的模化语言，能够解决所有标准优化问题和高级主题，完全可以替代Matlab中的Optim和LMI工具箱(YALMIP is a high-level optimization problem definition and solving the modeling language, to solve the optimization problem for all standard and advanced topics, can replace Matlab and the LMI toolbox in the Optim)

matlab programming for engineers(2nd)书籍源码,这是一本很好的学习MATLAB编程书(matlab programming for engineers(2nd))

基于奇异值分解的人脸识别方法 梁毅雄 龚卫国 潘英俊 李伟红 刘嘉敏 张红梅 提出了一种将傅里叶变换和奇异值分解相结合的人脸自动识别方法.首先对人脸图像进行傅里叶变换,得到其具有位移不变特性的振幅谱表征.其次,从所有训练图像样本的振幅谱表征中给定标准脸并对其进行奇异值分解,求出标准特征矩阵,再将人脸的振幅谱表征投影到标准特征矩阵后得到的投影系数作为该人脸的模式特征.然后,对经典的最近邻分类器算法进行了改进,并采用模式特征之间的欧式距离作为相似性度量,从而完成对未知人脸的识别.采用ORL (Olivetti Research Laboratory)人脸库对本文提出的人脸识别方法进行验证,获得了100.00 的识别率.实验结果表明,本方法优于现有的基于奇异值分解的人脸识别方法,且对表情、姿态变换等具有一定的鲁棒性. (Face recognition based on singular value decomposition method Deliberate simultaneously Gong Weiguo Li Wei Hung Stephen Lau, Hong-Mei Zhang Ying-Jun Pan Paper, a Fourier transform and singular value decomposition of the combination of automatic face recognition. First of all, the face image by Fourier transformation, it has the same characteristics of the displacement amplitude spectra. Secondly, all training The amplitude spectrum of the sample images given in standard face representation and its singular value decomposition, find the standard characteristic matrix, then the amplitude of spectral characterization of human faces projected onto the standard characteristic matrix of projection coefficients obtained as the face of the model features . Then, the classical nearest neighbor classifier is improved, and the use of Euclidean distance between pattern features as the similarity measure, thus completing the identification of unknown human faces. using ORL (Olivetti Research La)