简介:Inthispaper,anovelmethodforThreedimensionalactivesoundfieldcancellationisproposed.Theoptimumsoundcancellationsindifferentsituationsmaybeobtained.Anarraywhoseelementslieonasetofringsplacedonthesurfaceofascatteringobjectisconsideredasthedevice.Itisquitepossibletocancelthescatteredsoundfieldtoanyarbitrarylevelovereitherthewholespaceorapartialareaofinterest,aslongasthenumberofthearrayelementsissufficient.
简介:Thisreportdescribesanewmethod,theself-searchingmethod,tofindeigenraysinanoceanwherethereisathree-dimensionalsoundspeedperturbationblobonauniformsoundspeedbackground.Comparedwiththetraditionalshootingmethod,thismethodcanreducethenumberofraycalcula-tionsbyabouttwoordersofmagnitude,andaneigenraycanbefoundbycom-puterprogramwithoutmanualintervention.
简介:Ultrasoundsimulationforcarotidarteriesishelpfultotheperformanceassessmentsofvesselwalldetectionandsignalprocessingmethodsbyusingultrasoundtechniques.Anultrasoundsimulationmethodofcarotidarterywallwithathree-membranestructureisproposedinpresentstudy.Accordingtotheultrasoundspeckledistributionsvaryingwiththeshapesanddensitiesofscattererdistributions,aswellasthestatisticresultsoftheclinicalimages,theparametersofdistributions,densitiesandintensitiesofscatterersfordifferentkindsoftissuesinthecarotidarteryphantomsaredetermined.EachregionisacousticallycharacterizedusingFIELDIIsoftwaretoproducetheradiofrequencyechosignals,fromwhichultrasoundimagesarederived.Theresultsbasedon30simulationsshowthattheechodistributionsoftheintimae,mediae,adventitiasandbloodareconsistentwiththeclinicalones.Moreover,comparedwiththeresultsfromthecentralfrequencyof8MHz,themeanmeasurementsforthicknessesoftheintima,mediaandadventitiamembranes,aswellasthelumendiameterfromthesimulationimagesbasedon12MHzarethesameasthepresetones,andthemaximumrelativeerrorsarethe4.01%,1.25%,0.04%and0.15%,respectively.Thesimulationunderthisconditionismorerealistic.
简介:Toimprovetheperformanceofsoundsourcelocalizationbasedondistributedmicrophonearraysinnoisyandreverberantenvironments,asoundsourcelocalizationmethodwasproposed.Thismethodexploitedtheinherentspatialsparsitytoconvertthelocalizationproblemintoasparserecoveryproblembasedonthecompressivesensing(CS)theory.Inthismethodtwo-stepdiscretecosinetransform(DCT)-basedfeatureextractionwasutilizedtocoverbothshort-timeandlong-timepropertiesofthesignalandreducethedimensionsofthesparsemodel.Moreover,anonlinedictionarylearning(DL)methodwasusedtodynamicallyadjustthedictionaryformatchingthechangesofaudiosignals,andthenthesparsesolutioncouldbetterrepresentlocationestimations.Inaddition,weproposedanimprovedapproximatel_0normminimizationalgorithmtoenhancereconstructionperformanceforsparsesignalsinlowsignal-noiseratio(SNR).Theeffectivenessoftheproposedschemeisdemonstratedbysimulationresultswherethelocationsofmultiplesourcescanbeobtainedinthenoisyandreverberantconditions.
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