简介：让是四元数海森堡组，并且让P是仿射的自守组。我们上经由P的单一的代表在四元数海森堡组上开发连续小浪变换的理论。光线的小浪的一个班被构造。反的小浪变换被使用光线的小浪简化。然后，我们上调查氡变换。一个Semyanistyi-Lizorkin空格被介绍，在哪个上氡变换是bijection。我们由欧几里德几何学的Fourier变换和组Fourier变换在两个上处理氡变换。这二个处理是实质上相等的。如果小浪是光滑的，我们也由使用小浪给一个倒置公式，它不要求功能的光滑。另外，我们获得上与亚拉普拉斯算符联系的氡变换的一个倒置公式。

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简介：ThisworkpresentsacomputationalmatrixframeworkintermsoftensorsignalalgebrafortheformulationofdiscretechirpFouriertransformalgorithms.Thesealgorithmsareusedinthisworktoestimatethepointtargetfunctions(impulseresponsefunctions)ofmultiple-inputmultiple-output(MIMO)syntheticapertureradar(SAR)systems.Thisestimationtechniqueisbeingstudiedasanalternativetotheestimationofpointtargetfunctionsusingthediscretecross-ambiguityfunctionforcertaintypesofenvironmentalsurveillanceapplications.Thetensorsignalalgebraispresentedasamathematicsenvironmentcomposedofsignalspaces,finitedimensionallinearoperators,andspecialmatriceswherealgebraicmethodsareusedtogeneratethesesignaltransformsascomputationalestimators.Also,thetensorsignalalgebracontributestoanalysis,design,andimplementationofparallelalgorithms.AninstantiationoftheframeworkwasperformedbyusingtheMATLABParallelComputingToolbox,whereallthealgorithmspresentedinthispaperwereimplemented.

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简介：Homologousfeaturepointextractionisakeyproblemintheopticalandsyntheticapertureradar(SAR)imageregistrationforislands.Anewfeaturepointextractionmethodusingathresholdshrinkoperatorandnon-subsampledwavelettransform(TSO-NSWT)foropticalandSARimageregistrationwasproposed.Moreover,thematchingforthisproposedfeaturewasdifferentfromthetraditionalfeaturematchingstrategiesandwasperformedusingasimilaritymeasurecomputedfromneighborhoodcirclesinlow-frequencybands.Then,anumberofreliablymatchedcoupleswithevendistributionswereobtained,whichassuredtheaccuracyoftheregistration.ApplicationoftheproposedalgorithmtoSPOT-5(multi-spectral)andYG-1(SAR)imagesshowedthatalargenumberofaccuratelymatchedcouplescouldbeidentified.Additionally,bothoftherootmeansquareerror(RMSE)patternsoftheregistrationparameterscomputedbasedontheTSO-NSWTalgorithmandtraditionalNSWTalgorithmwereanalyzedandcompared,whichfurtherdemonstratedtheeffectivenessoftheproposedalgorithm.Thealgorithmcansupplythecrucialstepforislandimageregistrationandislandrecognition.

简介：Thecoherencecubetechnologyhasbecomeanimportanttechnologyfortheseismicattributeinterpretation,whichextractsthediscontinuitiesoftheeventsthroughanalyzingthesimilaritiesofadjacentseismicchannelstoidentifythefaultform.Thecoherencecubetechnologywhichusesconstanttimewindowlengthscannotbalancetheshallowlayersandthedeeplayers,becausethefrequencybandofseismicdatavarieswithtime.Whenanalyzingtheshallowlayers,thetimewindowwillcrossoveralotofevents,whichwillleadtoweakfocusingabilityandfailuretodelineatethedetails.Whilethetimewindowwillnotbelongenoughforanalyzingdeeplayers,whichwillleadtolowaccuracybecausethecoherencesnearthezeropointsoftheeventsareheavilyinfluencedbynoise.Forsolvingtheproblem,weshouldmakearesearchonthecoherencecubetechnologywithself-adaptivetimewindow.Thispaperdeterminesthesamplepoints’timewindowlengthsinrealtimebycomputingtheinstantaneousfrequencybandswithWaveletTransformation,whichgivesacoherencecomputingmethodwiththeself-adaptivetimewindowlengths.Theresultshowsthatthecoherencecubetechnologywithself-adaptivetimewindowbasedonWaveletTransformationimprovestheaccuracyoffaultidentification,andsupressesthenoiseeffectively.Themethodcombinestheadvantagesoflongtimewindowmethodandshorttimewindowmethod.