xiaobofenxitezhengtiqu

实现地震叠前三参数反演（纵波速度、横波速度、密度）(prestack seismic inversion (compressional wave velocity, shear wave velocity, density))

这是parzen窗估计的典型例子，大家可以直接学习，是个不可多得的好材料。(This is a typical example of the estimated parzen window, we can learn, is a rare good material.)

说明：  在matlab7中可以运行。其功能是实现用CV模型对灰色图像进行分割。(It Can be run in matlab7. function: It can make the gray image segmentation using the CV model .)

说明：  MATLAB是一个非常好的用于测量，分析和可视化的数据，控制仪器，并建立测试系统。安捷伦信号和频谱分析仪可让您分析失真，虚假的，相位噪声，用于2G到4G无线通信测量。这种Matlab下开发的高级数据可视化（ADV）的应用软件，扩展了安捷伦信号和频谱分析仪的可视化功能，提供了四个不同的显示格式：Analog Advanced, Analog Plus, Waterfall,和Spectrogram。这些显示使得更加容易捕获间歇性信号。(MATLAB is a very good for the measurement, analysis and visualization of data, control apparatus, and to establish test systems. Agilent signal and spectrum analyzers lets you analyze distortion, false, phase noise, for 2G to 4G wireless communication measurements. This developed under Matlab Advanced Data Visualization (ADV) of the application software, the expansion of the Agilent signal and spectrum analyzer visualization capabilities, providing four different display formats: Analog Advanced, Analog Plus, Waterfall, and Spectrogram. These shows make it easier to capture intermittent signals.)

scatter plot in matlan advance

this file is m-file for design of ZCS/ZVS converter

this is can apply to run simulink

说明：  珍藏版，可实现，新K均值聚类算法，分为如下几个步骤： 一、初始化聚类中心 1、根据具体问题，凭经验从样本集中选出C个比较合适的样本作为初始聚类中心。 2、用前C个样本作为初始聚类中心。 3、将全部样本随机地分成C类，计算每类的样本均值，将样本均值作为初始聚类中心。 二、初始聚类 1、按就近原则将样本归入各聚类中心所代表的类中。 2、取一样本，将其归入与其最近的聚类中心的那一类中，重新计算样本均值，更新聚类中心。然后取下一样本，重复操作，直至所有样本归入相应类中。 三、判断聚类是否合理 采用误差平方和准则函数判断聚类是否合理，不合理则修改分类。循环进行判断、修改直至达到算法终止条件。(NewK-means clustering algorithm ，Divided into the following several steps: A, initialize clustering center 1, according to the specific problems, from samples with experience selected C a more appropriate focus the sample as the initial clustering center. 2, with former C a sample as the initial clustering center. 3, will all samples randomly divided into C, calculate the sample mean, each the sample mean as the initial clustering center. Second, initial clustering 1, according to the sample into the nearest principle clustering center represents the class. 2, as this, take the its recent as clustering center of that category, recount the sample mean, update clustering center. And then taking off, as this, repeated operation until all samples into the corresponding class. Three, judge clustering is reasonable Adopt error squares principles function cluster analysis.after clustering whether reasonable, no reasonable criterion revisio)

INTHIS WE ARE ABLE TO SUMMARIZE fourier analysis application in fingerprint

模板法实现图像锐化，《计算机图像处理与识别技术》源代码 (model image sharpen)