各种光网络解决方案 相关技术的研究和探索Word下载.docx
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GPeterNet,agraphtheoreticframework,andFraNtiC,afractalgeometricarchitecture,forarbitraryaccessnetworkdeployments.Theperformanceofthesetopologiesisanalyzedintermsofdifferentsystemmetrics–topologicalrobustnessandreliability,systemcostsandnetworkexposureduetofailureconditions.Ouranalysisshowsthatacombinationofdifferentmesh-basedmulti-hopaccesstopologies,coupledwithemergingwirelessbackhaultechnologies,cancatercarrier-classservicesfornextgenerationradioaccessnetworks,providingsignificantadvantagesoverexistingaccesstechnologies.
ArticleOutline
1.Introduction
1.1.Motivationandpreviouswork
1.2.Ourcontributions
2.Opticalwirelesstechnology
3.ThePeterNetandGPeterNetarchitectures
3.1.ThegeneralizedPeterNet
4.TheFraNtiCarchitecture
4.1.Flexibilityandscalability
5.Robustness,reliabilityandnetworkexposure
5.1.Robustness
5.1.1.Centralityanditsroleinaccesstopology
5.2.Reliabilityanalysis
5.2.1.ReliabilityanalysisofFraNtiC
5.2.2.ReliabilityanalysisofGPeterNet
5.3.Networkexposure
6.Performanceevaluationframework
6.1.Systemparameters
6.2.Evaluationplatform
7.Conclusion
Acknowledgements
AppendixA.
AppendixB.
References
Vitae
硬件人工智能系统二十年的发展历程及启示和经验
Artificialneuralnetworksinhardware:
Asurveyoftwodecadesofprogress
Neurocomputing神经网络计算学报
Thisarticlepresentsacomprehensiveoverviewofthehardwarerealizationsofartificialneuralnetwork(ANN)models,knownashardwareneuralnetworks(HNN),appearinginacademicstudiesasprototypesaswellasincommercialuse.HNNresearchhaswitnessedasteadyprogressformorethanlasttwodecades,thoughcommercialadoptionofthetechnologyhasbeenrelativelyslower.WestudytheoverallprogressinthefieldacrossallmajorANNmodels,hardwaredesignapproaches,andapplications.WeoutlineunderlyingdesignapproachesformappinganANNmodelontoacompact,reliable,andenergyefficienthardwareentailingcomputationandcommunicationandsurveyawiderangeofillustrativeexamples.Chipdesignapproaches(digital,analog,hybrid,andFPGAbased)atneuronallevelandasneurochipsrealizingcompleteANNmodelsarestudied.Wespecificallydiscuss,indetail,neuromorphicdesignsincludingspikingneuralnetworkhardware,cellularneuralnetworkimplementations,reconfigurableFPGAbasedimplementations,inparticular,forstochasticANNmodels,andopticalimplementations.Paralleldigitalimplementationsemployingbit-slice,systolic,andSIMDarchitectures,implementationsforassociativeneuralmemories,andRAMbasedimplementationsarealsooutlined.Wetracetherecenttrendsandexplorepotentialfutureresearchdirections.
1.Introduction
2.Evaluationparametersandclassification
2.1.Hardwareneuralnetworkclassification
3.Hardwareapproachestoneuronaldesign
3.1.Digitalneuron
3.2.Analogneuron
3.3.Siliconimplementationofspikingneuronanditssynapticdynamics
4.Hardwareneuralnetworkchips
4.1.Digitalneurochips
4.2.Analogneurochips
4.3.Hybridneurochips
4.4.FPGAbasedimplementations
4.5.Otherimplementations
4.5.1.Associativeneuralmemories
4.5.2.RAMbasedimplementations
5.CNNimplementations
6.NeuromorphicHNNs
6.1.Spikingneuralnetworkhardware
7.Opticalneuralnetworks
8.Conclusionsanddiscussion
Databasearchitectures:
Currenttrendsandtheirrelationshipstoenvironmentaldatamanagement
数据结构:
当前发展趋势及其与数据管理环境的关系
EnvironmentalModelling&
Software
AnMPLS-basedarchitectureforscalableQoSandtrafficengineeringinconvergedmultiservicemobileIPnetworks
Impactofnetworkstructureonthecapacityofwirelessmultihopadhoccommunication
PhysicaA:
StatisticalMechanicsanditsApplication
网络结构设置与网络通信能力的关系及相互影响无线网络通讯多路由连接与布局
Reconfigurableturbodecodingfor3Gapplications
SignalProcessing
3G应用软件中,本蓝C语言代码调试与程序编写
Softwareradioandreconfigurablesystemsrepresentreconfigurablefunctionalitiesoftheradiointerface.Consideringturbodecodingfunctioninbattery-powereddeviceslike3GPPmobileterminals,itwouldbedesirabletochoosetheoptimumdecodingalgorithm:
SOVAintermsoflatency,andlog-MAPintermsofperformance.Inthispaperitisshownthatthetwoalgorithmssharecommonoperations,makingfeasibleareconfigurableSOVA/log-MAPturbodecoderwithincreasedefficiency.Moreover,animprovementintheperformanceofthereconfigurablearchitectureisalsopossibleatminimumcost,byscalingtheextrinsicinformationwithacommonfactor.Theimplementationoftheimprovedreconfigurabledecoderwithinthe3GPPstandardisalsodiscussed,consideringdifferentscenarios.Ineachscenariovariousframelengthsareevaluated,whilethefourpossibleserviceclassesareapplied.InthecaseofAWGNchannels,theoptimumalgorithmisproposedaccordingtothedesiredqualityofserviceofeachclass,whichisdeterminedfromlatencyandperformanceconstraints.Ouranalysisshowsthepotentialutilityofthereconfigurabledecoder,sincethereisanoptimumalgorithmformostscenarios.
2.WhyreconfigurationonlybetweenSOVAandlog-MAP?
3.Mathematicalanalysisofthealgorithms
3.1.SOVAanalysis
3.2.Log-MAPanalysis
3.3.ReconfigurableoperationsbetweenSOVAandlog-MAP
3.3.1.BMCblock
3.3.2.FSMCandRSMCblocks
3.3.2.1.FPMC,RPMCsub-blocks
3.3.2.2.FAC,RACsub-blocks
3.3.2.3.SPC,FACsub-blocks
4.Datatransferin3GPP
4.1.Qualityofservicearchitecture
5.Simulationmodel
6.ImprovingtheSOVA/log-MAPreconfigurabledecoder
7.Latencycalculation
8.Simulationresultsandimplementationscenariosin3GPP
8.1.Scenario1:
28.8
kbpsradiobearerservice
8.2.Scenario2:
57.6
kbpsradiobearerservice
8.2.1.Streamingserviceclass
8.3.Scenario3:
64
8.3.1.Streamingserviceclass
8.4.Scenario4:
128
8.4.1.Streamingserviceclass
8.5.Scenario5:
144
8.