IEEE14

Chinese text categorization and classification of the document indexing mechanism model of

实现9/7小波正反变换 注1： 采用标准正交方法，对行列采用不同矩阵（和matlab里不同） 注2： 为了保证正交，所有边界处理，全部采用循环处理 注3： 正交性验证，将单位阵带入函数，输出仍是单位阵（matlab不具有此性质） 注4： 此程序是矩阵实现，所以图像水平分量和垂直分量估计被交换位置 注5： 此程序实现的是类小波（wavelet-like）变换，是介于小波包变换与小波变换之间的变换 注6： 此程序每层变换相对原图像矩阵，产生的矩阵都是正交阵，这和小波包一致 注7： 但小波变换每层产生的矩阵，是相对每个待分解子块的正交矩阵，而不是原图像的正交矩阵 注8： 且小波变换产生的正交矩阵维数，随分解层数2分减少 注9: 提升系数可以在MATLAB7.0以上版本，用liftwave( 9.7 )获取，这里直接给出，考虑兼容性 注10：由于MATLAB数组下标从1开始，所以注意奇偶序列的变化 注11：d为对偶上升，即预测；p为原上升，即更新(Note 1: The standard orthogonal method, the ranks of the different matrices (and matlab where different) Note 2: In order to ensure orthogonality, all boundary treatment, all with recycling Note 3: Orthogonal validation, the identity matrix into the function, the output is still the unit matrix (matlab does not have this property) Note 4: This program is a matrix to achieve, so the image horizontal and vertical components is estimated to be swapped Note 5: This class implements the wavelet (wavelet-like) transformation, is between the wavelet packet transform and wavelet transform between Note 6: This procedure transforms each image relative to the original matrix, the resulting matrix are orthogonal matrix, consistent with this and wavelet packet Note 7: But each generation wavelet transform matrix, is to be broken down for each sub-block relative to the orthogonal matrix, rather than the original image of the orthogonal matrix Note 8: Wavelet transf)

Amorphous定位算法的matlab例程，下载解压后可以直接用(Amorphous localization algorithm)

用于粒子滤波的，讲解详细！初学者可以很容易地看懂！(For the particle filter, and explain in detail! Beginners can easily understand!)

这个文件中整理了大家常用的一些Matlab的函数及画图命令，需要的时候可以自由查找(This document compiled we used some of the Matlab functions and drawing commands, you need to find free time)

用matlab编写的牛顿迭代方法求解非线性方程组：数值分析中很经典的代码(a matlab code of newton method solve system of nonlinear equations)

this is speech recognition file

this is matlab code cyclic 1511

generating wave form

奇异值分解（SVD）的MATLAB仿真性能分析(Singular value decomposition (SVD) of the MATLAB simulation performance analysis)