简介：<正>InthispaperitisprovedthatsumfromN=N+1toN+Hx（n）ψ（n）（?）εH1-（1/r）q(（1/4（r-）1）+ε,wherer=4,qisaprimepower,χandψaremultiplicativeandadditivecharactersmoduloqrespectively,withχnontrivial.

简介：ThetraditionalGaussianMixtureModel(GMM)forpatternrecognitionisanunsupervisedlearningmethod.Theparametersinthemodelarederivedonlybythetrainingsamplesinoneclasswithouttakingintoaccounttheeffectofsampledistributionsofotherclasses,hence,itsrecognitionaccuracyisnotidealsometimes.ThispaperintroducesanapproachforestimatingtheparametersinGMMinasupervisingway.TheSupervisedLearningGaussianMixtureModel(SLGMM)improvestherecognitionaccuracyoftheGMM.Anexperimentalexamplehasshownitseffectiveness.TheexperimentalresultshaveshownthattherecognitionaccuracyderivedbytheapproachishigherthanthoseobtainedbytheVectorQuantization(VQ)approach,theRadialBasisFunction(RBF)networkmodel,theLearningVectorQuantization(LVQ)approachandtheGMM.Inaddition,thetrainingtimeoftheapproachislessthanthatofMultilayerPerceptrom(MLP).

简介：有弹性的移植被采用multicomponent处理地震数据的向量广泛地付了注意。光线基于的有弹性的Kirchhoff移植有象高灵活性和高效率的如此的性质。然而，它没能解决multipath引起的许多问题。在另一方面，有弹性的反向时间的移植(请读使用手册)基于双向波浪方程被知道能够处理这些问题，但是当在3D情况和速度模型大楼中适用时，它是极其昂贵的。基于有弹性的Kirchhoff-Helmholtz积分，我们计算decoupled由介绍有弹性的格林的向后继续的wavefields为P-waves,和S波浪工作，它被elastodynamicGaussian横梁的求和表示。PP和改正极性的PS图象被计算关联在之间获得向下并且decoupled向后继续的向量wavefields，在极性修正被分析在极化之间的关系执行的地方，变换PS的方向飘动并且接口上的事件角度。到大程度，我们的方法把基于光线的移植的高效率与波浪方程的高精确性相结合基于的反向时间的移植。到从差错模型和Marmousi2的合成数据集建模的multicomponent的这个方法的申请表明新方法的有效性，灵活性和精确性。

简介：Detailedmathematicaldeductionispresentedtoimprovethegraphicsqualityofaircraftdashboards.Generationofrhumblineswithsuchkindofalgorithmshowsthatthenewapproachiseffective.

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简介：Recentextensivemeasurementsofreal-lifetrafficdemonstratethattheprobabilitydensityfunctionofthetrafficinnon-Gaussian.Ifatrafficmodeldoesnotcapturethischaracteristics,anyanalyticalorsimulationresultswillnotbeaccurate.Inthiswork,westudytheimpactofnon-Gaussiantrafficonnetworkperformance,andpresentanapproachthatcanaccuratelymodelthemarginaldistributionofreal-lifetraffic.Boththelong-andshort-rangeautocorrelationsarealsoaccounted.Weshowthattheremovalofnon-Gaussiancomponentsoftheprocessdoesnotchangeitscorrelationstructure,andwevalidateourpromisingprocedurebysimulations.

简介：ThemainpurposeofthispaperistoshowthatthereisanisometricembeddingfromMoebiusbandtoR4withpositiveGaussiancurvature.

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简介：Opticalspinsplittinghasattractedsignificantattentionowingtoitspotentialapplicationsinquantuminformationandprecisionmetrology.However,itistypicallysmallandcannotbecontrolledefficiently.Here,weenhancethespinsplittingbytransmittinghigher-orderLaguerre–Gaussian(LG)beamsthroughgraphenemetamaterialslabs.TheinteractionbetweenLGbeamsandmetamaterialresultsinanorbital-angularmomentum-(OAM)dependentspinsplitting.TheupperboundoftheOAM-dependentspinsplittingisfound,whichvarieswiththeincidentOAMandbeamwaist.Moreover,thespinsplittingcanbeflexiblytunedbymodulatingtheFermienergyofthegraphenesheets.Thistunablespinsplittinghaspotentialapplicationsinthedevelopmentofspin-basedapplicationsandthemanipulationofmid-infraredwaves.

简介：ＡＮｅｗＭｅｔｈｏｄｏｆＡｐｅｒｔｕｒｅＡｎａｌｙｓｉｓＢａｓｅｄｎＧａｕｓｓｉａｎＢｅａｍＥｘｐａｎｓｉｏｎTXＡＮｅｗＭｅｔｈｏｄｏｆＡｐｅｒｔｕｒｅＡｎａｌｙｓｉｓＢａｓｅｄｏｎＧａｕｓｉａｎＢｅａｍＥｘｐａｎｓｉｏｎＺｈｏｕＨａｉｊｉｎｇ＆Ｒ...

简介：Basedonthesecond-ordermoments,thispaperderivesananalyticalexpressionoftheM2factoroffour-petalGaussianbeam.TheresultsshowthattheM2factorisonlydeterminedbythebeamordern.Thecorrespondingnumericalcalculationsarealsogiven.Asthebeamorderincreases,theaugmentofM2factorisdisciplinary.AstheexpressionofM2factorisexpressedinseriesformandbecomesmorecomplicated,anewconciseformulaofM2factorisalsopresentedbyusingcurvefittingofnumericalcalculations.When3≤n≤200,themaximumerrorrateoffittingformulawillnotexceed2.6%andtheaverageerrorrateis0.28%.Thisresearchishelpfultotheapplicationsoffour-petalGaussianbeam.

简介：Withthevigorousexpansionofnonlinearadaptivefilteringwithreal-valuedkernelfunctions,itscounterpartcomplexkerneladaptivefilteringalgorithmswerealsosequentiallyproposedtosolvethecomplex-valuednonlinearproblemsarisinginalmostallreal-worldapplications.ThispaperfirstlypresentstwoschemesofthecomplexGaussiankernel-basedadaptivefilteringalgorithmstoillustratetheirrespectivecharacteristics.ThenthetheoreticalconvergencebehaviorofthecomplexGaussiankernelleastmeansquare(LMS)algorithmisstudiedbyusingthefixeddictionarystrategy.ThesimulationresultsdemonstratethatthetheoreticalcurvespredictedbythederivedanalyticalmodelsconsistentlycoincidewiththeMonteCarlosimulationresultsinbothtransientandsteady-statestagesfortwointroducedcomplexGaussiankernelLMSalgonthmsusingnon-circularcomplexdata.Theanalyticalmodelsareabletoberegardasatheoreticaltoolevaluatingabilityandallowtocomparewithmeansquareerror(MSE)performanceamongofcomplexkernelLMS(KLMS)methodsaccordingtothespecifiedkernelbandwidthandthelengthofdictionary.

简介：Thephenomenonofstochasticresonance(SR)inabistablenonlinearsystemisstudiedwhenthesystemisdrivenbytheasymmetricpotentialandadditiveGaussiancolorednoise.Usingtheunifiedcolorednoiseapproximationmethod,theadditiveGaussiancolorednoisecanbesimplifiedtoadditiveGaussianwhitenoise.Thesignal-to-noiseratio(SNR)iscalculatedaccordingtothegeneralizedtwo-statetheory(shownin[H.S.WioandS.Bouzat,BrazilianJ.Phys.29(1999)136]).WefindthattheSNRincreaseswiththeproximityofatozero.Inaddition,thecorrelationtimeτbetweentheadditiveGaussiancolorednoiseisalsoaningredienttoimproveSR.TheshorterthecorrelationtimeτbetweentheGaussianadditivecolorednoiseis,thehigherofthepeakvalueofSNR.

简介：Let{Xk(t),t≥0},k=1,2,...,beasequenceofindependentGaussianprocesseswithaσ^2k(h)=E(Xk(t+h)--Xk(t))^2.Putσ(p,h)=(∞/∑/k=1σ^pk(h))^1/p,p≥1.Theauthorestablishesthelargeincrementresultsforboundedσ(p,h).

简介：Let{fX(t),t≧0}beacenteredstationaryGaussianprocesswithcorrelationr(t)suchthat1-r(t)isasymptotictoaregularlyvaryingfunction.WithTbeinganonnegativerandomvariableandindependentofX(t),theexactasymptoticsofP(supt∈[0;T]X(t)>x)isconsidered,asx→∞.

简介：AnewmethodologyofvoiceconversionincepstrumeigenspacebasedonstructuredGaussianmixturemodelisproposedfornon-parallelcorporawithoutjointtraining.Foreachspeaker,thecepstrumfeaturesofspeechareextracted,andmappedtotheeigenspacewhichisformedbyeigenvectorsofitsscattermatrix,therebytheStructuredGaussianMixtureModelintheEigenSpace(SGMM-ES)istrained.Thesourceandtargetspeaker’sSGMM-ESarematchedbasedonAcousticUniversalStructure(AUS)principletoachievespectrumtransformfunction.Experimentalresultsshowthespeakeridentificationrateofconversionspeechachieves95.25%,andthevalueofaveragecepstrumdistortionis1.25whichis0.8%and7.3%higherthantheperformanceofSGMMmethodrespectively.ABXandMOSevaluationsindicatetheconversionperformanceisquiteclosetothetraditionalmethodundertheparallelcorporacondition.TheresultsshowtheeigenspacebasedstructuredGaussianmixturemodelforvoiceconversionunderthenon-parallelcorporaiseffective.

简介：公共天气服务是向向用户提供概率的天气预报的trending，代替传统的确定的预报。概率的预报技术不断地正在被改进优化可得到的预报信息。预报(BPF)的贝叶斯的处理器，为概率的预报的一个新统计方法，能根据在那个预报系统产生的观察和预报之间的历史的统计关系把一张确定的预报转变成一张概率的预报。这种技术在确定说明一个确定的预报系统的典型预报性能预报无常。meta-Gaussian可能性的模型对有单调可能性的比率的许多随机的依赖结构合适。收养这种可能性的模特儿的meta-GaussianBPF能因此越过许多地被使用，包括气象学和水文学。有二个连续随机的变量和正常线性的BPF的Bayes定理简短被介绍。为用一个单个预言者的连续predictand的meta-GaussianBPF然后被介绍并且讨论。meta-GaussianBPF的表演在一个初步的实验被测试。在在长沙和武汉车站的0000UTC的每日的表面温度的控制预报被用作确定的预报数据。这些控制预报从整体预言被拿，一96-h铅时间由中国气象学的管理的国家气象学的中心产生了，中等范围的天气的欧洲中心预报，并且US公民为在2008年1月期间的环境预言集中。实验的结果证明meta-GaussianBPF能从三整体预言中的任何一个把表面温度的一张确定的控制预报转变成表面温度的一张有用概率的预报。这些概率的预报确定控制预报的无常；因此，概率的预报的表演基于内在的确定的控制预报的来源不同。

简介：这篇论文研究平板波导然后匹配的效率在之间的结束衍射远地并且它的Gaussian近似的地被分析导致分叉一半角度的一个新定义。最后，远地的罐头为什么被Gaussian功能接近，根据横梁繁殖因素的特征被介绍。

简介：InthispaperthepropagationofLorentz–GaussianbeamsinstronglynonlinearnonlocalmediaisinvestigatedbytheABCDmatrixmethod.Forthispurpose,anexpressionforfielddistributionduringpropagationisderivedandbasedonit,thepropagationofLorentz–Gaussianbeamsissimulatedinthismedia.Then,theevolutionsofbeamwidthandcurvatureradiusduringpropagationarediscussed.

简介：为Madych和尼尔森在1992提出的Gaussians有很强大的错误界限，是著名的，，它具有form|/(jc)-在哪儿的s(x)\<(Cd)i||/||AC，c是常数，h是Gaussian功能，s是插入内推的功能，并且d被叫充满哪个的距离，粗略地说，测量插值在发生的点的间距，。这错误界限作为d->很快变得小0。常数C和c是很敏感的，他们的一个细微变化将导致错误界限的一个巨大的变化，。数字c能是出现在被计算[9]。然而，C不能被计算，或甚至接近了。这是在光线的基础功能的理论的一个著名问题。这篇论文的目的是回答这个问题，。