简介：Reversibledatahidingtechniquesarecapableofreconstructingtheoriginalcoverimagefromstego-images.Recently,manyresearchershavefocusedonreversibledatahidingtoprotectintellectualpropertyrights.Inthispaper,wecombinereversibledatahidingwiththechaoticHénonmapasanencryptiontechniquetoachieveanacceptablelevelofconfidentialityincloudcomputingenvironments.And,Haardigitalwavelettransformation(HDWT)isalsoappliedtoconvertanimagefromaspatialdomainintoafrequencydomain.Andthenthedecimalofcoefficientsandintegerofhighfrequencybandaremodifiedforhidingsecretbits.Finally,themodifiedcoefficientsareinverselytransformedtostego-images.

简介：Animprovedalgorithmwhichisbasedontherecursiveclosed-formalgorithmfornon-minimumphaseFIRsystemidentificationviahigherorderstatisticsispresented.Inordertoincreasetheparametricestimationaccuracy,theimprovedalgorithmusestheoptimaliterativemethodtoseekthenonlinearleast-squaresolution.Finally,thesimulationexamplesarealsogiven.

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

简介：Themechanismofanon-polarizingbeamsplitter(NPBS)withasymmetricaltransfercoefficientscausingtherotationofpolarizationdirectionisexplainedinprinciple,andthemeasurementnonlinearerrorcausedbyNPBSisanalyzedbasedonJonesmatrixtheory.Theoreticalcalculationsshowthatthenonlinearerrorchangesperiodically,andtheerrorperiodandpeakvaluesincreasewiththedeviationbetweentransmissivitiesofp-polarizationands-polarizationstates.Whenthetransmissivityofp-polarizationis53%andthatofs-polarizationis48%,themaximumerrorreaches2.7nm.TheimperfectionofNPBSisoneofthemainerrorsourcesinsimultaneousphase-shiftingpolarizationinterferometer,anditsinfluencecannotbeneglectedinthenanoscaleultra-precisionmeasurement.

简介：ThispaperinvestigatestheuntraditionalapproachofcontentionresolutioninWavelengthDivisionMultiplexing(WDM)OpticalPacketSwitching(OPS).Themoststrikingcharacteristicsofthedevelopedswitcharchitectureare:(1)ContentionresolutionisachievedbyacombinedsharingofFiberDelay-Lines(FDLs)andTunableOpticalWavelengthConverters(TOWCs);(2)FDLsarearrangedinnon-degenerateform,i.e.,non-uniformdistributionofthedelaylines;(3)TOWCsjustcanperformwavelengthconversioninpartialcontinuouswavelengthchannels,i.e.,sparsewavelengthconversion.TheconcreteconfigurationsofFDLsandTOWCsaredescribedandanalyzedundernon-burstyandburstytrafficscenarios.Simulationresultsdemonstratethatforaprefixedpacketlossprobabilityconstraint,e.g.,10-6,thedevelopedarchitectureprovidesadifferentpointofviewinOPSdesign.Thatis,combinedsharingofFDLsandTOWCscan,effectively,obtainagoodtradeoffbetweentheswitchsizeandthecost,andTOWCswhichareachievedinsparseformcanalsodecreasetheimplementingcomplexity.

简介：Facerecognitionbasedonfewtrainingsamplesisachallengingtask.Indailyapplications,sufficienttrainingsamplesmaynotbeobtainedandmostofthegainedtrainingsamplesareinvariousilluminationsandposes.Non-sufficienttrainingsamplescouldnoteffectivelyexpressvariousfacialconditions,sotheimprovementofthefacerecognitionrateunderthenon-sufficienttrainingsamplesconditionbecomesalaboriousmission.Inourwork,thefacialposepre-recognition(FPPR)modelandthedualdictionarysparserepresentationclassification(DD-SRC)areproposedforfacerecognition.TheFPPRmodelisbasedonthefacialgeometriccharacteristicandmachinelearning,dividingatestingsampleintofull-faceandprofile.Differentposesinasingledictionaryareinfluencedbyeachother,whichleadstoalowfacerecognitionrate.TheDD-SRCcontainstwodictionaries,full-facedictionaryandprofiledictionary,andisabletoreducetheinterference.AfterFPPR,thesampleisprocessedbytheDD-SRCtofindthemostsimilaroneintrainingsamples.Theexperimentalresultsshowtheperformanceoftheproposedalgorithmonolivettiresearchlaboratory(ORL)andfacerecognitiontechnology(FERET)databases,andalsoreflectcomparisonswithSRC,linearregressionclassification(LRC),andtwo-phasetestsamplesparserepresentation(TPTSSR).