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ac-so-ro

于 2019-05-14 发布 文件大小:1229KB
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代码说明:

  MANET routing model with DSR

文件列表:

ac-so-ro\1, 0 , 2012-08-17
ac-so-ro\1\DOCUMENTATION, 0 , 2012-04-15
ac-so-ro\1\DOCUMENTATION\1.ppt, 124928 , 2008-11-25
ac-so-ro\1\DOCUMENTATION\ABSTRACT.docx, 11081 , 2012-04-15
ac-so-ro\1\DOCUMENTATION\Document.doc, 515072 , 2009-12-03
ac-so-ro\1\DOCUMENTATION\Java Doc Template.doc, 86528 , 2002-02-26
ac-so-ro\1\DOCUMENTATION\UMLS.doc, 50688 , 2002-04-06
ac-so-ro\1\HOW TO RUN, 0 , 2009-12-03
ac-so-ro\1\HOW TO RUN\readme.txt, 1115 , 2007-03-09
ac-so-ro\1\SOURCE CODE, 0 , 2009-12-03
ac-so-ro\1\SOURCE CODE\NewAdhoc, 0 , 2009-12-03
ac-so-ro\1\SOURCE CODE\NewAdhoc\adhoc_class.txt, 2162 , 2007-02-14
ac-so-ro\1\SOURCE CODE\NewAdhoc\adhoc_srs.doc, 268800 , 2007-01-24
ac-so-ro\1\SOURCE CODE\NewAdhoc\compile.bat, 23 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\compile.bat.bak, 23 , 2008-11-19
ac-so-ro\1\SOURCE CODE\NewAdhoc\Core.class, 4399 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\Core.java, 3992 , 2007-08-01
ac-so-ro\1\SOURCE CODE\NewAdhoc\Desktop.ini, 266 , 2008-01-11
ac-so-ro\1\SOURCE CODE\NewAdhoc\Destination.class, 6047 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\Destination.jad, 8697 , 2006-02-11
ac-so-ro\1\SOURCE CODE\NewAdhoc\Destination.java, 5560 , 2007-03-20
ac-so-ro\1\SOURCE CODE\NewAdhoc\ENCC.TXT, 244 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\ExecuteFirst.bat, 33 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\ExecuteFirst.bat.bak, 33 , 2008-11-19
ac-so-ro\1\SOURCE CODE\NewAdhoc\Executenext.bat, 25 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\Executenext.bat.bak, 30 , 2008-11-19
ac-so-ro\1\SOURCE CODE\NewAdhoc\F2.TXT, 9 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\FileOpen$1.class, 1728 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\FileOpen$2.class, 2449 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\FileOpen$3.class, 709 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\FileOpen$4.class, 1528 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\FileOpen.class, 3588 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\FileOpen.java, 5679 , 2007-03-23
ac-so-ro\1\SOURCE CODE\NewAdhoc\ipaddress, 0 , 2009-12-03
ac-so-ro\1\SOURCE CODE\NewAdhoc\ipaddress\ip.bat, 144 , 2007-01-19
ac-so-ro\1\SOURCE CODE\NewAdhoc\ipaddress\ip.class, 861 , 2007-01-19
ac-so-ro\1\SOURCE CODE\NewAdhoc\ipaddress\ip.java, 472 , 2007-01-17
ac-so-ro\1\SOURCE CODE\NewAdhoc\Main_Server.class, 4400 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\Main_Server.jad, 6020 , 2006-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\Main_Server.java, 4280 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\Main_Server.java.bak, 4280 , 2007-03-20
ac-so-ro\1\SOURCE CODE\NewAdhoc\Multi.JPG, 6958 , 2007-03-23
ac-so-ro\1\SOURCE CODE\NewAdhoc\net.class, 1719 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\net.java, 1200 , 2007-03-22
ac-so-ro\1\SOURCE CODE\NewAdhoc\net.txt, 243 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\New_Final_Adhoc_doc.doc, 6300672 , 2007-03-24
ac-so-ro\1\SOURCE CODE\NewAdhoc\NEXTMESH.TXT, 7 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\Pack.class, 1343 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\Pack.jad, 1292 , 2005-06-21
ac-so-ro\1\SOURCE CODE\NewAdhoc\Pack.java, 719 , 2006-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\readme.txt, 1115 , 2007-03-09
ac-so-ro\1\SOURCE CODE\NewAdhoc\ReCore.class, 2236 , 2008-11-25
ac-so-ro\1\SOURCE CODE\NewAdhoc\ReCore.java, 2204 , 2007-03-23
ac-so-ro\1\SOURCE CODE\NewAdhoc\sample, 0 , 2009-12-03
ac-so-ro\1\SOURCE CODE\NewAdhoc\sample\r.java, 347 , 2007-03-23
ac-so-ro\1\SOURCE CODE\NewAdhoc\tele.jpg, 9332 , 2007-03-23
ac-so-ro\1\SOURCE CODE\NewAdhoc\Thumbs.db, 7680 , 2002-04-07

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  • 第一次作业_基于分类算法的雷达状态识别
    第一次作业_基于分类算法的雷达状态识别 对于本数据集中的雷达状态识别,数据降维前使用朴素贝叶斯、支持向量机、神经网络的分类算法对于识别的准确率无太大影响;数据降维后使用神经网络算法最优,支持向量机算法其次,朴素贝叶斯算法较差。此外,训练样本越多,分类准确率有小幅度提高。(First Operation Radar State Recognition Based on Classification Algorithms For radar state recognition in this data set, the classification algorithm of Naive Bayesian, Support Vector Machine and Neural Network before data dimension reduction has little effect on the accuracy of recognition; Neural Network algorithm is the best after data dimension reduction, Support Vector Machine algorithm is the second, Naive Bayesian algorithm is the worst. In addition, the more training samples, the smaller the classification accuracy.)
    2019-04-03 08:54:04下载
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  • BPSK调制仿真
    说明:  bpsk调制仿真,可直接用 1、数字调制方法 数字调制就是把数字基带信号的频谱搬移到高频处,形成适合在信道中传输的带通信号。基本的数字调制方式有振幅键控(ASK)、频移键控(FSK)、绝对相移键控(PSK)、相对(差分)相移键控(DPSK)。在接收端可以采用想干解调或非相干解调还原数字基带信号。 数字信号的传输方式分为基带传输和带通传输。然而,实际中的大多数信道(如)无线信道具有丰富的低频分量。为了使数字信号在带通信道中传输,必须用数字基带信号对载波进行调制,以使信号与信道的特性相匹配。(1, digital modulation method Digital modulation is the transfer of the spectrum of a digital baseband signal to a high frequency to form a bandpass signal suitable for transmission in the channel. The basic digital modulation methods are amplitude keying (ASK), frequency shift keying (FSK), absolute phase shift keying (PSK), and relative (differential) phase shift keying (DPSK). At the receiving end, the digital baseband signal can be restored by using dry demodulation or non-coherent demodulation. Digital signal transmission methods are divided into baseband transmission and bandpass transmission. However, most channels in practice, such as wireless channels, have rich low frequency components. In order for a digital signal to be transmitted in a band channel, the carrier must be modulated with a digital baseband signal to match the signal to the characteristics of the channel.)
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