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Matlab5 手册，既适合初学者，也适合高级matlab用户，有目录和每一章的pdf文件(Matlab5 manuals, both suitable for beginners, but also suitable for senior matlab users directory and each chapter has the pdf file)

the c c** compiler with arduino in the multiples excercises

This is BOC AWGN Simulation zip file. It contains one folder(build setting) and 9 files(m file, mdl files etc..) Thank you

利用MATLAB实现分水岭算法得到图像分割(Watershed algorithm)

一个仿真802.11a的matlab源程序,使用.m文件来编写,参数可以自己来设(802.11a simulation of a matlab source code, use. M documents to prepare, parameters can be set up themselves)

我们的目的是将一个分数拆分成若干个分子是1分母是正整数的分数之和的形式。例如2/3可以拆分成1/2+1/6的形式。这里不考虑顺序，比如1/6+1/2和1/2+1/6相同。 输入p, q, a, n, 其中p/q为分数，对这个分数进行拆分，要求拆分的分数个数不能多于n个，所有分母的乘积不能大于a。求有多少种拆分方法? 输入：最多200组数据。在每行中都有四个数分别表示p, q, a, n，之间以空格隔开，并且满足p,q<=800，a<=12000，n<=7，最后以四个0做为输入的结束。 输入示例: 2 3 120 3 2 3 300 3 0 0 0 0 输出示例: 4 7 (Our aim is to split a fraction into a number of elements is a denominator is a positive integer fraction of the sum of form. For example, 2/3 can be split into 1/2+1/6 form. Not considered here, the order, such as 1/6+1/2 and 1/2+1/6 of the same. Input p, q, a, n, where p/q as a fraction of this split scores require no more than the number of split points n-all the product of the denominator can not be larger than a. How many kinds of demand split method? Input: Up to 200 sets of data. In each line has four numbers indicate the p, q, a, n, separated by a space between, and meet the p, q < = 800, a < = 12000, n < = 7, the end, four 0 as the input end. Input Example: 2,312,032,330,030,000 sample output: 47)

MATLAB与VC_混合编程实现一维小波多尺度分解_汉泽西.pdf用于电网故障检测(VC_ MATLAB mixed programming with one-dimensional wavelet multiscale decomposition _ Hanze Xi. Pdf for grid fault detection)

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ASM是由Cootes和泰勒推出的多分辨率方法的一个例子。 基本思想： 在ASM模型训练，训练从手工绘制的图像轮廓。发现的ASM模型在训练使用主成分分析（PCA），使该模型自动识别数据的主要变化是，如果可能的轮廓/好的对象的轮廓。还包含了ASM模型的协方差矩阵描述行垂直纹理口岸时，在正确的位置。 (Description This is an example of the basic Active Shape Model (ASM) as introduced by Cootes and Taylor, with multi-resolution approach. Basic idea: The ASM model is trained from manually drawn contours in training images. The ASM model finds the main variations in the training data using Principal Component Analysis (PCA), which enables the model to automatically recognize if a contour is a possible/good object contour. Also the ASM models contains covariance matrices describing the texture of the lines perpendicular to the control points when in the correct positions. After creating the ASM model, an initial contour is deformed by finding the best texture match for the control points. This is an iterative process, in which the movement of the control points is limited by what the ASM model recognizes from the training data as a "normal" object contour. Literature: - Ginneken B. et al. "Active Shape Model Segmentation with Optimal Features", IEEE Transactions on Medical I)

加窗快速傅里叶变化 比常规算法精度高 (Windowed fast Fourier transform algorithm for high accuracy than conventional)