Gaussian-beam-transmission

本书是讲用matlab工具箱处理小波分析算法的书籍，书里包含了工具箱的所有常用函数。(This book is about dealing with wavelet analysis toolbox matlab books, the book contains all the common functions of the toolbox.)

说明：  ＭＡＴＬＡＢ实现０.６１８法近似计算函数的最小值(MATLAB .618 law approximate calculation of the minimum function)

it is a toolbox for matlab_ the subject is support vector machine(svm)

基于matlab的遗传算法，程序简洁明了(Based on genetic algorithm matlab, the program clear and concise)

相干与非相干成像系统的比较。复习光学传递函数OTF的概念，以及该函数与相干传递函数CTF的关系。用OTF完成单透镜系统成像的计算。(The comparison of coherent and incoherent imaging system.Review the concept of OTF, optical transfer function and the function relations with CTF coherent transfer function.Complete the single lens imaging system with OTF calculation.)

matlab image procesing

光学传输中的Airy beam,这种由于具有独特的空间特性将广泛的应用于信号传输系统之中(Airy beam)

计算Lyapunov指数的Matlab程序，主要是二维Henon映射的Lyapunov指数的计算 (Matlab Lyapunov index calculation procedures, mainly two-dimensional Henon map of the Lyapunov index calculation)

PdfParse 用于从PDF文件中抽取文本数据的示例，支持unicode字符集编码、8进制和16进制编码。(PdfParse used to extract text from a PDF file example of data, unicode character set encoding, octal and hexadecimal encoding.)

创建一个n维的稀疏数组对象，n是任意值。 定义N可能是大于2的一类n维稀疏阵列。然而，它应该被认为是一种起动方式与普通的MATLAB稀疏矩阵和重塑它有N维度。换句话说，稀疏的数据，首先必须能够作为一个普通的2D MATLAB稀疏矩阵被前n维。事实上，如果目标数组的尺寸mxnxp……yxz，然后将它存储在内部类是一个普通的二维稀疏阵列的尺寸（M×N×P×……×Y）XZ。这导致了某些内存株时使用大量的尺寸。我发现有用的类主要用于中等尺寸像三维图像边缘检测，你经常要举行一个稀疏的3D的边缘地图。(Creates an N-dimensional sparse array object, for arbitrary N.This submission defines a class of N-dimensional sparse arrays for N possibly greater than 2. However, it should really be thought of as a way of starting with an ordinary MATLAB sparse matrix and reshaping it to have N dimensions. In other words, the sparse data must first be able to exist as an ordinary 2D MATLAB sparse matrix before being made N-dimensional. In fact, if the intended array has dimensions MxNxP...YxZ, then the class will store it internally as an ordinary 2D sparse array of dimensions (M*N*P*...*Y)xZ. This leads to certain memory strains when using large numbers of dimensions. I find the class useful mainly for moderate dimensional things like edge detection in 3D imaging, where you often want to hold a sparse 3D edge map. )