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说明：  matlab的书 其中有论文等有一个例子希望新手喜欢(matlab book has essays which have an example of hope novice like)

说明：  短时傅里叶分析，时频分析等，m文件集合，tftb(Short time Fourier analysis, time-frequency analysis)

说明：  指纹图像分割~利用分开的局部均值及方向场方法(~ Use of fingerprint image segmentation and direction of separate local mean field method)

说明：  LBG算法设计矢量量化码书的函数，需要给出训练序列x,矢量维数K和码书长度CN，返回根据训练序列给出的最小误差码书。(LBG algorithm for vector quantization codebook design function , you need to give training sequence x, vector dimension K and codebook length CN, return according to the minimum error given training sequence codebook . )

用matlab4、5阶龙格-库塔法函数求解模拟批次试验动力学，(With matlab4 order Runge- Kutta method function solving analog batch tests dynamics,)

henon lyapunov graphics for a and b

matlab的gscale函数功能是将强度图像等映射到别的值范围。(Function of matlab gscale intensity images is mapped to another range.)

Matlab环境下使用霍夫变换在二值图像上检测圆形的代码(Function uses Standard Hough Transform to detect circles in a binary image.)

Principle Component Analysis

In this session, I will focus on the gridding method for fast image reconstruction from non- Cartesian data [1-5]. While gridding is not the only game in town, it is the reconstruction of choice when reconstruction time is an issue. Other reconstruction techniques include rBURS [6] and non-Cartesian iterative methods [7,8]. Non-Cartesian iterative methods generally use gridding and inverse gridding as their core operation, so even when gridding is not the right solution for an application, an understanding of gridding can be useful. I will cover the main steps of the gridding algorithm and then discuss some implementation tips that can signicantly reduce the computation time. Finally, I will present some illustrations and viewpoints that I have found to aid in an intuitive understanding of the gridding method, which can be helpful when designing the density compensation function and judging the appropriateness of gridding as a solution for a particular application.