简介：Severalfeaturesofretinalvesselscanbeusedtomonitortheprogressionofdiseases.Changesinvascularstructures,forexample,vesselcaliber,branchingangle,andtortuosity,areportentsofmanydiseasessuchasdiabeticretinopathyandarterialhypertension.Thispaperproposesanautomaticretinalvesselsegmentationmethodbasedonmorphologicalclosingandmulti-scalelinedetection.First,anilluminationcorrectionisperformedonthegreenbandretinalimage.Next,themorphologicalclosingandsubtractionprocessingareappliedtoobtainthecruderetinalvesselimage.Then,themulti-scalelinedetectionisusedtofinethevesselimage.Finally,thebinaryvasculatureisextractedbytheOtsualgorithm.Inthispaper,forimprovingthedrawbacksofmulti-scalelinedetection,onlythelinedetectorsat4scalesareused.Theexperimentalresultsshowthattheaccuracyis0.939forDRIVE(digitalretinalimagesforvesselextraction)retinaldatabase,whichismuchbetterthanothermethods.

简介：Inordertoslovethelarge-scalenonlinearprogramming(NLP)problemsefficiently,anefficientoptimizationalgorithmbasedonreducedsequentialquadraticprogramming(rSQP)andautomaticdifferentiation(AD)ispresentedinthispaper.Withthecharacteristicsofsparseness,relativelylowdegreesoffreedomandequalityconstraintsutilized,thenonlinearprogrammingproblemissolvedbyimprovedrSQPsolver.Inthesolvingprocess,ADtechnologyisusedtoobtainaccurategradientinformation.Thenumericalresultsshowthatthecombinedalgorithm,whichissuitableforlarge-scaleprocessoptimizationproblems,cancalculatemoreefficientlythanrSQPitself.

简介：ThispaperpresentstwodifferentalgorithmsthatderivethecohesionstructureintheformoflexicalchainsfromtwokindsoflanguageresourcesHowNetandTongYiCiCiLin.There-searchthatconnectsthecohesionstructureofatexttothederivationofitssummaryisdisplayed.Anovelmodelofautomatictextsummarizationisdevised,basedonthedataprovidedbylexicalchainsfromoriginaltexts.Moreover,theconstructionrulesoflexicalchainsaremodifiedaccord-ingtocharacteristicsoftheknowledgedatabaseinordertobemoresuitableforChinesesumma-rization.EvaluationresultsshowthathighqualityindicativesummariesareproducedfromChi-nesetexts.

简介：ThepaperpresentsanalgorithmofautomatictargetdetectioninSyntheticApertureRadar(SAR)imagesbasedonMaximumAPosteriori(MAP).Thealgorithmisdividedintothreesteps.First,itemploysGaussianmixturedistributiontoapproximateandestimatemulti-modalhistogramofSARimage.Then,basedontheprincipleofMAP,whenaprioriprobabilityisbothunknownandlearnedrespectively,thesamplepixelsareclassifiedintodifferentclassesc={target,shadow,background}.Last,itcomparestheresultsoftwodifferenttargetdetections.Simulationresultspreferablyindicatethatthepresentedalgorithmisfastandrobust,withthelearnedaprioriprobability,anapproachtotargetdetectionisreliableandpromising.

简介：Beingdirectedagainettwokinksofnoiseinopticalfibersensors,asim-pleandeffectivemethodofautomaticcompensationforopticalfibersensorsispresented.Notonlytheunstabilityeffectoflightsource,butalsozerodriftofphotoelectronicde-vices,canbeeliminatedorenormouslyrestranedwiththeaidofthismethod.Inanotherway,byusingsingle-chipmrcrocomputer,theopticalfibersensorsystemfabricatediscaonnetedtoacopmuternetworkstorealizeanautomaticcompensation.

简介：ThepaperaddressestheproblemoftargetrecognitionusingHigh-resolutionRadarRangeProfiles(HRRP).Anovelapproachoffeatureextractionanddimensionreductionbasedonextendedhighordercentralmomentsisproposedinordertoreducethedimensionofrangeprofiles.FeaturesextractedfromradarHRRPsarenormalizedandsmoothed,andthencomparativeanalysisofthesimilarapproachesisdone.Therangeprofilesareobtainedbystepfrequencytechniqueusingthetwo-dimensionalbackscattersdistributiondataoffourdifferentaircraftmodels.Thetemplatematchingmethodbynearestneighborrules,whichisbasedonthetheoryofkernelmethodsforpatternanalysis,isusedtoclassifyandidentifytherangeprofilesfromfourdifferentaircrafts.Numericalsimulationresultsshowthattheproposedapproachcanachievegoodperformanceofstability,shiftindependenceandhigherrecognitionrate.Itishelpfulforreal-timeidentificationandtheengineeringimplementsofautomatictargetrecognitionusingHRRP.Thenumberofrequiredtemplatescouldbereducedcon-siderablywhilemaintaininganequivalentrecognitionrate.

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简介：Theneedforwide-bandclockanddatarecovery(CDR)circuitsisdiscussed.A2Gbpsto12Gbpscontinuous-rateCDRcircuitemployingamulti-modevoltage-controloscillator(VCO),afrequencydetector,andaphasedetector(FD&PD)isdescribed.Anewautomaticfrequencybandselection(FBS)withoutexternalreferenceclockisproposedtoselecttheappropriatemodeandalsosolvetheinstabilityproblemwhenthecircuitispoweringon.Themulti-modeVCOandFD/PDcircuitswhichcanoperateatfull-rateandhalf-ratemodesfacilitateCDRwithsixoperationmodes.TheproposedCDRstructurehasbeenmodeledwithMATLABandthesimulatedresultsvalidateitsfeasibility.

简介：ThispaperproposesaPCAandKPCAself-fusionbasedMSTARSARautomatictargetrecognitionalgorithm.Thisalgorithmcombinesthelinearfeatureextractedfromprincipalcomponentanalysis(PCA)andnonlinearfeatureextractedfromkernelprincipalcomponentanalysis(KPCA)respectively,andthenutilizestheadaptivefeaturefusionalgorithmwhichisbasedontheweightedmaximummargincriterion(WMMC)tofusethefeaturesinordertoachievebetterperformance.Thelinearregressionclassifierisusedintheexperiments.Theexperimentalresultsindicatethattheproposedself-fusionalgorithmachieveshigherrecognitionratecomparedwiththetraditionalPCAandKPCAfeaturefusionalgorithms.

简介：AmeasurementsystemwiththeCCDmatrixandcomputersystemisdesignedtotestthe2Dsizeofanyshapeworkpiecesautomatically.Inaddition,thesystemadoptsthemethodoftherelativemeasurementwhichincreasestheprecisionandthevelocity.Moreimportantly,theprecisioncan'tbechangedwiththeconditionsofthetemperatureandairpressure.Theexperimentsshowthattherelativeprecisionof0.0029andtheabsoluteprecisionof2.97μmareobtained.Theinstrumentmaybeusedintheproductlineandmakethetestingonlinepossible.

简介：Thisletterintroducesa4thorderactiveRCcomplexfilterwith1.5MHzcenterfrequencyand1MHzbandwidth.Thetotalharmonicdistortionofthefilterislessthan–60dBandtheimagerejectionratioisgreaterthan60dB.Anoveltechniqueisalsoproposedinthislettertoautomaticallyadjustthevariationofthetimeconstant.Theadvantagesoftheproposedmethodareitshighprecisionandsimplicity.Using5bitscontrolwords,thetuningerrorislessthan±1.6%.