简介：Supportvectormachines(SVMs)haveshownremarkablesuccessinmanyapplications.However,thenon-smoothfeatureofobjectivefunctionisalimitationinpracticalapplicationofSVMs.Toovercomethisdisadvantage,atwicecontinuouslydifferentiablepiecewise-smoothfunctionisconstructedtosmooththeobjectivefunctionofunconstrainedsupportvectormachine(SVM),anditissuesapiecewise-smoothsupportvectormachine(PWESSVM).Comparingtotheothersmoothapproximationfunctions,thesmoothprecisionhasanobviousimprovement.ThetheoreticalanalysisshowsPWESSVMisgloballyconvergent.Numericalresultsandcomparisonsdemonstratetheclassificationperformanceofouralgorithmisbetterthanothercompetitivebaselines.

简介：Supportvectormachines(SVMs)havebeenintensivelyappliedinthedomainsofspeechrecognition,textcategorization,andfaultsdetection.However,thepracticalapplicationofSVMsislimitedbythenon-smoothfeatureofobjectivefunction.Toovercomethisproblem,anovelsmoothfunctionbasedonthegeometryofcircletangentisconstructed.Itsmoothesthenon-differentiabletermofunconstrainedSVM,andalsoproposesacircletangentsmoothSVM(CTSSVM).Comparedwithothersmoothapproachingfunctions,itssmoothprecisionhadanobviousimprovement.TheoreticalanalysisprovedtheglobalconvergenceofCTSSVM.NumericalexperimentsandcomparisonsshowedCTSSVMhadbetterclassificationandlearningefficiencythancompetitivebaselines.

简介：Thesum-ratecapacityofCDMAandTDMAinvectorbroadcastchannelisresearched.Anupperboundofsum-ratecapacityinCDMAsystemandalowerboundofsum-ratecapacityinTDMAsystemarederivedrespectively.Accordingtothesetwobounds,theratioofthetwoboundsatmostcanachievemin(M,K).HereMistheantennanumberofthetransmitterandKisthenumberoftheuserinthisvectorbroadcastchannel.

简介：Asthesignificantbranchofintelligentvehiclenetworkingtechnology,theintelligentfatiguedrivingdetectiontechnologyhasbeenintroducedintothepaperinordertorecognizethefatiguestateofthevehicledriverandavoidthetrafficaccident.Thedisadvantagesofthetraditionalfatiguedrivingdetectionmethodhavebeenpointedoutwhenwestudyonthetraditionaleyetrackingtechnologyandtraditionalartificialneuralnetworks.Onthebasisoftheimagetopologicalanalysistechnology,Haarlikefeaturesandextremelearningmachinealgorithm,anewdetectionmethodoftheintelligentfatiguedrivinghasbeenproposedinthepaper.Besides,thedetailedalgorithmandrealizationschemeoftheintelligentfatiguedrivingdetectionhavebeenputforwardaswell.Finally,bycomparingtheresultsofthesimulationexperiments,thenewmethodhasbeenverifiedtohaveabetterrobustness,efficiencyandaccuracyinmonitoringandtrackingthedrivers’fatiguedrivingbyusingthehumaneyetrackingtechnology.

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简介：Withthewideapplicationofvirtualizationtechnologyinclouddatacenters,howtoeffectivelyplacevirtualmachine(VM)isbecomingamajorissueforcloudproviders.Theexistingvirtualmachineplacement(VMP)solutionsaremainlytooptimizeserverresources.However,theypaylittleconsiderationonnetworkresourcesoptimization,andtheydonotconcerntheimpactofthenetworktopologyandthecurrentnetworktraffic.Amulti-resourceconstraintsVMPschemeisproposed.Firstly,theauthorsattempttoreducethetotalcommunicationtrafficinthedatacenternetwork,whichisabstractedasaquadraticassignmentproblem;andthenaimatoptimizingnetworkmaximumlinkutilization(MLU).Ontheconditionofslightvariationofthetotaltraffic,minimizingMLUcanbalancenetworktrafficdistributionandreducenetworkcongestionhotspots,aclassiccombinatorialoptimizationproblemaswellasNP-hardproblem.Antcolonyoptimizationand2-optlocalsearcharecombinedtosolvetheproblem.SimulationshowsthatMLUisdecreasedby20%,andthenumberofhotlinksisdecreasedby37%.

简介：Asthesystemperformanceisobviouslyimprovedbyintroducingtheconceptofrelayintothetraditionalorthogonalfrequencydivisionmultipleaccess(OFDMA)systems,resourceschedulinginrelay-enhancedOFDMAsystemsisworthyofbeingstudiedcarefully.Tosolvetheoptimizationproblemofachievingthemaximumthroughputwhilesatisfyingthequalityofservice(QoS)andguaranteeingthefairnessofusers,anovelresourceschedulingschemewithQoSsupportforthedownlinkoftwo-hoprelay-enhancedOFDMAsystemsisproposed.Theproposedscheme,whichisconsideredbothinthefirsttimesub-slotbetweendirectlinkusersandrelaystations,andthesecondtimesub-slotamongrelaylinkusers,takesQoSsupportintoconsideration,aswellasthesystemthroughputandthefairnessforusers.Simulationresultsshowthattheproposedschemehasgoodperformanceinmaximizingsystemthroughputandguaranteeingtheperformanceintheservicedelayandthedatalossrate.

简介：罗德与施瓦茨同汽车电子测试专家Vector公司合作发布业界最先进的蜂窝车联网应用层测试方案。该方案结合了罗德与施瓦茨的基站模拟器R&SCMW500、卫星模拟器R&SSMBV100A/B和Vector公司的CANoe.Car2X软件,并于巴塞罗那举办的2019年世界移动通信大会上首次展示,其中CANoe.Car2X软件被广泛的应用于仿真,开发和测试车联网相关应用。