简介：InordertoimprovetheperformanceofdeceptiondetectionbasedonChinesespeechsignals,amethodofsparsedecompositiononspectralfeatureisproposed.First,thewaveletpackettransformisappliedtodividethespeechsignalintomultiplesub-bands.Bandcepstralfeaturesofwaveletpacketsareobtainedbyoperatingthediscretecosinetransformonlogarithmicenergyofeachsub-band.ThecepstralfeatureisgeneratedbycombingMelFrequencyCepstralCoefficientandWaveletPacketBandCepstralCoefficient.Second,K-singularvaluedecompositionalgorithmisemployedtoachievethetrainingofanover-completemixturedictionarybasedonboththetruthanddeceptivefeaturesets,andanorthogonalmatchingpursuitalgorithmisusedforsparsecodingaccordingtothemixturedictionarytogetsparsefeature.Finally,recognitionexperimentsareperformedwithvariousclassifiedmodules.Experimentalresultsshowthatthesparsedecompositionmethodhasbetterperformancecompariedwithconventionaldimensionreducedmethods.Therecognitionaccuracyofthemethodproposedinthispaperis78.34%,whichishigherthanmethodsusingotherfeatures,improvingtherecognitionabilityofdeceptiondetectionsystemsignificantly.

简介：Overthepastdecades,cellsurfacecharge,althoughexperimentallyobserved,hasnotbeenwellunderstoodparticularlyfromtheviewpointofbiophysics.Ourrecentstudieshaveshownthatallcancercellsexhib让negativesurfacechargesthataredirectlyproportionaltothesecretedlacticacid,auniquecancermetaboliccharacteristic:highrateofglycolysis.Wehavethereforedesignedanddevelopedasetofelectrically-charged,fluorescent,andsuper-paramagneticnanoprobes,capableofsensitivedetectionofcancercellsbasedonthesurfacecharges.Theseprobesareutilizedtobindontocellsviaelectrostaticreactionforcaptureandmagneticseparation.Inthisfashion,weareabletocharacterizecellsurfacechargesthatareregulatedbydifferentmetabolicpatterns,thereforeeffectivelydistinguishingthecancercellsfromthenormalcells.All22cancercellsofdifferentorgansarefoundtobenegativelychargedthereforeboundstronglybythepositively-chargednanoprobes,whereasthenormalcellsshowinsignificantbindingtothenanoprobesofeitherchargesigns(positiveornegative).Thisfindingsuggeststhatalltestedcancercellsarenegatively-chargedandnormalcellsareeithercharge-neutralorslightlypositive.Fordiagnosis,cancercellscanbedetected,electrostaticallybound,andmagneticallyseparatedinbloodbychargedandsuper-paramagneticnanoprobes.Intherapeutics,circulatingcancercells(CTCs)canbefilteredandremovedinacontinuousfashiontoreducetheriskofcancermetastasis.Ifsuccessful,thisnewnanotechnologywillrevolutionizeearlycancerdiagnosisandpotentiallyenablenewtherapeuticsinclinicalsettings.

简介：Platestructuresareemployedasimportantstructuralcomponentsinmanyengineeringapplications.Hence,assessingthestructuralconditionsofin-serviceplatestructuresiscriticaltomonitoringglobalstructuralhealth.Modalcurvature-baseddamagedetectiontechniqueshaverecentlygarneredconsiderableattentionfromtheresearchcommunity,andhavebecomeapromisingvibration-basedstructuralhealthmonitoringsolution.However,computingerrorsarisewhencalculatingmodalcurvaturesfromlateralmodeshapes,whichresultfromunavoidablemeasurementerrorsinthemodeshapesasidentifiedfromlateralvibrationsignals;thismakescurvature-basedalgorithmsthatusealateralmeasurementonlytheoreticallyfeasible,butpracticallyinfeasible.Therefore,inthisstudy,long-gaugefiberBragggratingstrainsensorsareemployedtoobtainamodalcurvaturewithoutanumericaldifferentiationprocedureinordertocircumventthecomputingerrors.Severaldamageindicesbasedonmodalcurvaturesthatweredevelopedtolocatebeamdamageareemployed.Bothnumericalandexperimentalstudiesareperformedtovalidatetheproposedapproach.However,althoughpreviousstudieshavereportedrelativesuccesswiththeapplicationofthesedamageindicesonasimplebeam,onlyonedamageindexdemonstratedthecapabilitytolocatedamagewhenthestiffnessofthelocalregionchangednearthesensor.

简介：Alargenumberofdebrisflowdisasters(calledSeismicdebrisflows)wouldoccurafteranearthquake,whichcancauseagreatamountofdamage.UAVlow-altituderemotesensingtechnologyhasbecomeameansofquicklyobtainingdisasterinformationasithastheadvantageofconvenienceandtimeliness,butthespectralinformationoftheimageissoscarce,makingitdifficulttoaccuratelydetecttheinformationofearthquakedebrisflowdisasters.Basedontheaboveproblems,aseismicdebrisflowdetectionmethodbasedontransferlearning(TL)mechanismisproposed.Onthebasisoftheconstructedseismicdebrisflowdisasterdatabase,thefeaturesacquiredfromthetrainingoftheconvolutionalneuralnetwork(CNN)aretransferredtothedisasterinformationdetectionoftheseismicdebrisflow.Theautomaticdetectionofearthquakedebrisflowdisasterinformationisthencompleted,andtheresultsofobject-orientedseismicdebrisflowdisasterinformationdetectionarecomparedandanalyzedwiththedetectionresultssupportedbytransferlearning.

简介：Laser-inducedbreakdownspectroscopy(LIBS)wasexaminedtodetectatracesubstanceadheredontoAlalloysforthesurfaceinspectionofmaterialstobeadhesivelybonded.AsanexampleofSicontamination,siliconeoilwasemployedandsprayedontosubstrateswithacontrolledsurfaceconcentration.LIBSmeasurementsemployingnanosecondUVpulses(λ=266nm)andanoff-axisemissioncollectionsystemwithdifferentdetectingheightswereperformed.Becausesurfacecontaminantsareinvolvedintheplasmaformedbylaserablationofthesubstrates,therelativecontributionofthesurfacecontaminantsandthesubstratestotheplasmaemissioncouldbechangeddependingontheconditionsforplasmaformation.Thelimitofdetection(LOD)wasevaluatedunderseveraldetectingconditionsforinvestigatingthefactorsthataffectedtheLOD.Asignificantfactorwasthestandarddeviationvaluesofsignalintensitiesobtainedforthecleansubstrates.Thisvaluevarieddependingonthemeasurementconditions.FortheAlalloy(A6061),thesmallestLODobtainedwas0.529μg·cm^-2.Furthermore,animprovedLOD(0.299μg·cm^-2)wasobtainedfortheAlalloywithalowerSicontent.

简介：Laser-inducedbreakdownspectroscopy(LIBS)hasattractedextensiveattentionasanewtechniqueforin-situmarineapplication.Inthiswork,theinfluenceofdeep-seahighpressureenvironmentonLIBSsignalswasinvestigatedbyusingacompactLIBS-seasystemdevelopedbyOceanUniversityofChinaforthein-situchemicalanalysisofseawater.TheresultsfromthefieldmeasurementsshowthattheliquidpressurehasasignificanteffectontheLIBSsignals.Higherpeakintensityandlargerlinebroadeningwereobtainedasthepressureincreases.BycomparingthevariationsofthetemperatureandsalinitywiththeLIBSsignals,aweakcorrelationbetweenthemcanbeobserved.Underhighpressureconditions,theoptimallaserenergywashigherthanthatinairenvironment.Whenthelaserenergyexceeded17mJ,theeffectoflaserenergyonthesignalintensityweakened.Thesignalintensitydecreasesgraduallyatlargerdelays.TheobtainedresultsverifiedthefeasibilityoftheLIBStechniqueforthedeep-seain-situdetection,andwehopethistechnologycancontributetosurveyingmoredeep-seaenvironmentssuchasthehydrothermalventregions.

简介：Oneofthetechnicalbottlenecksoftraditionallaser-inducedbreakdownspectroscopy(LIBS)isthedifficultyinquantitativedetectioncausedbythematrixeffect.Totroubleshootthisproblem,thispaperinvestigatedacombinationoftime-resolvedLIBSandconvolutionalneuralnetworks(CNNs)toimproveKdeterminationinsoil.Thetime-resolvedLIBScontainedtheinformationofbothwavelengthandtimedimension.Thespectraofwavelengthdimensionshowedthecharacteristicemissionlinesofelements,andthoseoftimedimensionpresentedtheplasmadecaytrend.Theone-dimensionaldataofLIBSintensityfromtheemissionlineat766.49nmwereextractedandcorrelatedwiththeKconcentration,showingapoorcorrelationofR^2c=0.0967,whichiscausedbythematrixeffectofheterogeneoussoil.Forthewavelengthdimension,thetwo-dimensionaldataoftraditionalintegratedLIBSwereextractedandanalyzedbyanartificialneuralnetwork(ANN),showingR^2v=0.6318andtherootmeansquareerrorofvalidation(RMSEV)=0.6234.Forthetimedimension,thetwo-dimensionaldataoftime-decayLIBSwereextractedandanalyzedbyANN,showingR^2v=0.7366andRMSEV=0.7855.Thesehigherdeterminationcoefficientsrevealthatboththenon-KemissionlinesofwavelengthdimensionandthespectraldecayoftimedimensioncouldassistinquantitativedetectionofK.However,duetolimitedcalibrationsamples,thetwo-dimensionalmodelspresentedover-fitting.Thethree-dimensionaldataoftime-resolvedLIBSwereanalyzedbyCNNs,whichextractedandintegratedtheinformationofboththewavelengthandtimedimension,showingtheR^2v=0.9968andRMSEV=0.0785.CNNanalysisoftime-resolvedLIBSiscapableofimprovingthedeterminationofKinsoil.

简介：Photonics-basedradarwithaphotonicde-chirpreceiverhastheadvantagesofbroadbandoperationandreal-timesignalprocessing,butitsuffersfrominterferencefromimagefrequenciesandotherundesiredfrequency-mixingcomponents,duetosingle-channelreal-valuedphotonicfrequencymixing.Inthispaper,weproposeaphotonicsbasedradarwithaphotonicfrequency-doublingtransmitterandabalancedin-phaseandquadrature(I/Q)de-chirpreceiver.Thisradartransmitsbroadbandlinearlyfrequency-modulatedsignalsgeneratedbyphotonicfrequencydoublingandperformsI/Qde-chirpingoftheradarechoesbasedonabalancedphotonicI/Qfrequencymixer,whichisrealizedbyapplyinga90°opticalhybridfollowedbybalancedphotodetectors.Theproposedradarhasahighrangeresolutionbecauseofthelargeoperationbandwidthandachievesinterference-freedetectionbysuppressingtheimagefrequenciesandotherundesiredfrequency-mixingcomponents.Intheexperiment,aphotonics-basedK-bandradarwithabandwidthof8GHzisdemonstrated.ThebalancedI/Qde-chirpingreceiverachievesanimage-rejectionratioofover30dBandsuccessfullyeliminatestheinterferenceduetothebasebandenvelopeandthefrequencymixingbetweenradarechoesofdifferenttargets.Inaddition,thedesireddechirpedsignalpowerisalsoenhancedwithbalanceddetection.Basedontheestablishedphotonics-basedradar,inversesyntheticapertureradarimagingisalsoimplemented,throughwhichtheadvantagesoftheproposedradarareverified.