GhostPoints

mpsk的matlab仿真实现 ,供初学者使用参考(MPSK Simulation of matlab for beginners to use reference)

自己做的一个项目的处理程序： 实现大量数据的格式读取，分割处理，并按要求的行以一定的函数进行运算，把要求的行的数据与时间画出变化曲线(A project of their own procedures for handling: achieving a large amount of data format to read, be dealt with separately in accordance with the requirements of the line to a certain degree of function computation, the data requirements and time line to draw curve)

一个混沌信号产生器MATLAB源代码。主要用于产生混沌信号序列。()

Coldfire processor Assembly instructions

Energey Detection ROC plot. Probablity of detection vs Probablity of false alarm.

example to matlab/simulink power system model

LPC(linear predictive coding)线性预测编码及改进型voice excited LPC源代码(LPC (linear predictive coding) linear predictive coding and improved voice excited LPC source code)

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)

2-D–3-D Frequency Registration Using a Low-Dose Radiographic System for Knee Motion Estimation

实现了区域合并的图像分割的经典方法，是Interactive Image Segmentation by Maximal Similarity based Region Merging 论文的源码，可以好好研究(Regional integration to achieve the classic method of image segmentation is the Interactive Image Segmentation by Maximal Similarity based Region Merging paper source, you can carefully study)