SCMA调制解调matlab仿真代码

变分模态分解（Variational mode decomposition，简称VMD）方法[18]，VMD方法将信号分解转化为约束变分问题，自适应地将信号分解为若干个IMF分量之和。

这个是从github上找到的 ～可以参考参考c++代码木有找到～

一个简单的ID3决策树算法实现，但是很使用。简单易懂。

文件包含了OFDM发送端信号生成的MATLAB代码和相应的课程报告

MFC下编写的对话框程序，打开摄像头/视频文件/图像文件。基于opencv下的Mat类，避免了内存管理问题。此代码为开发图像/视频处理软件开发提供良好的参考。

MMC-HVDCmatlab仿真，模型预测控制，论文和仿真相对应

比较完善的STM32仿PLC方案，IO可以随意设置，功能较多！

站里这方面资源不多，特别是种子区域生长方面的很少，上传一个算一个吧rightnumleftnuIm percent2()Ivaluerightnumipercent2(rvalue)pm= lvalue/‖P‖,pma= rvalue/‖P‖‖Pwindows xpMATLAB (R2008aOtsa.RlenI=ll, Rlen2=60, percent1=55%c, percent270%b. Rlenl=ll, Rlen2=80, percent1=50%, percent2=94%c.Rlen1-1l, Rlen2-100 percent1-65%. percent2-80%d. Rlen1-1l, Rlen2-=80, percent1=60%. percent2-83%2cent 1percent2Percentppercent 13C1994-2012ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhtlp://www.cnki.net[J]2007,29(6):854-857[J]26(2):93-962010,45(7):76-80[JI.1963:1l5-12005,26(11):[J],2005,17(1):1-3Research on the improved algorithm and application ofseed region growth method and its sail image extrationYAN Shen-hai, HUANG Xian-tong, LIU Yang(School of Mathematics Computer Science, Gannan Normal University, Ganzhou 341000, Jiangxi, ChinaAbstract: Seeded region growing is a common method of image segmentation. Its performance depends largelyon the seleclion of seed points and growth rules. In order Lo extract snail images with such methods more effectively.The article analyzes the basic idea of the seeded region growing method(SRG) and presents an improved seededregion growing method (ISRG) which is used to extract the snail images. In ISRG, novel similarities rules and anew dynamic threshold method are adopted. For the complex and various habitat of snail, ISRG selects the region growing seed points manually. It is demonstrated by experimental results that ISRG can achieve better snailimage extraction results under the complicated real-world sceneKey words: seeded region growing (SRG; image extraction; dynamic threshold; snail(E D:X, JC1994-2012ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhtlp://www.cnki.net

基于K均值的文本聚类分析 文本聚类基于K均值的文本聚类分析 K均值 文本聚类

GM(1,1)灰色预测模型，用于长期预测使用，短期也有使用