简介:Two-dimensional(2D)multiple-inputmultiple-output(MIMO)iscurrentlyconcentratedonpropagationinhorizontalplane,buttheimpactofelevationangleisnotconsidered.However,duetothethree-dimensional(3D)characteroftherealMIMOchannel,2DMIMOcannotachievetheoptimalsystemthroughput.Amultiple-userMIMO(MU-MIMO)userpairingschemewasproposed,inwhichtheverticaldimensionwastakenintoconsideration.Intheproposedscheme,a3DcodebookbasedonfulldimensionMIMOchannelwasdesigned;thentwo3DMU-MIMOuser'spairingschemesareproposedcombiningtheproposedjointandseparate3Dcodebook.Simulationevaluatestheproposed3Dcodebookaideduserpairingschemeandcompareswiththeprevious2DMU-MIMOuserpairingtechnology.Owingtotheadditionalspatialdegreeoffreedominverticaldimension,theproposed3DMU-MIMOuserpairingschemescaneffectivelyimprovetheoverallsystemperformance.
简介:Single-cameramobile-visioncoordinatemeasurementisoneoftheprimarymethodsof3D-coordinatevisionmeasurement,andcodedtargetplaysanimportantroleinthissystem.Amultifunctionalcodedtargetanditsrecognitionalgorithmisdeveloped,whichcanrealizeautomaticmatchoffeaturepoints,calculationofcamerainitialexteriororientationandspacescalefactorconstraintinmeasurementsystem.Theuniquenessandscalabilityofcodingareguaranteedbytherationalarrangementofcodebits.Therecognitionofcodedtargetsisrealizedbycross-ratioinvariancerestriction,spacecoordinatestransformoffeaturepointsbasedonspacialposeestimationalgorithm,recognitionofcodebitsandcomputationofcodingvalues.Theexperimentresultsdemonstratetheuniquenessofthecodingformandthereliabilityofrecognition.
简介:从工信部获悉,工信部副部长辛国斌对《国家智能制造标准体系建设指南》进行了解读,他表示,智能带IJ造是《中国制造2025》的主攻方向,该指南明确了建设智能制造标准体系的总体要求、建设思路、建设内容和组织实施方式,从生命周期、系统层级、智能功能等3个维度建立了智能制造标准体系参考模型,并由此提出了智能制造标准体系框架,框架包括“基础”、“安全”、“管理”、“检测评价”、“可靠性”等5类基础共性标准,和“智能装备”、“智能工厂”、“智能服务”、“工业软件和大数据”、“工业互联网”等5类关键技术标准,以及包括《中国制造2025》中10大应用领域在内的不同行业的应用标准。辛国斌表示,计划每2-3年对该指南进行修订。
简介:Thispapercombinescompressedsensing(CS)imagingtheoryandrangemigrationalgorithm(RMA),andthenproposesanear-fieldthree-dimensional(3-D)imagingapproachforjointhigh-resolutionimagingandphaseerrorcorrection.Firstly,asparsemeasurementmatrixconstructionmethodbasedonalogisticsequenceisproposed,whichconductsnonlineartransformationforthedeterminedlogisticsequence,makingitobeyuniformdistribution,thenconductssignfunctionmapping,andgeneratesthepseudorandomsequencewithBernoullidistribution,thusleadingtogoodsignalrecoveryunderdown-samplingandeasyavailabilityforengineeringrealization.Secondly,incombinationwiththeRMAimagingapproach,thedictionarywithallsceneinformationandphaseerrorcorrectionisconstructedforCSsignalrecoveryanderrorcorrection.Finally,thenon-quadraticsolutionmodeljointingimagingandphaseerrorcorrectionbasedonregularizationisbuilt,anditissolvedbytwosteps—theseparablesurrogatefunctionals(SSF)iterativeshrinkagealgorithmisadoptedtorealizetargetscatteringestimate;theiterationmodeisadoptedforthecorrectionofthedictionarymodel,soastoachievethegoaloferrorcorrectionandhighly-focusedimaging.Theproposedapproachprovestobeeffectivethroughnumericalsimulationandrealmeasurementinanechoicchamber.Theresultsshowthat,theproposedapproachcanrealizehigh-resolutionimaginginthecaseoflessdata;thedesignedmeasurementmatrixhasbetternon-coherenceandeasyavailabilityforengineeringrealization.Theproposedapproachcaneffectivelycorrectthephaseerror,andachievehighly-focusedtargetimage.