简介：Cyclostationary健全的地是nonstationary声音地，压力信号严重在被调制的一种特殊类型，边带在它的光谱存在。重建的健全的地不能在常规Nearfield声学的雷射摄影术考虑cyclostationary特征(不)过程。根据平面cyclostationary不，cyclostationary不基于边界元素方法被建议它能被利用与复杂表面分析散热器。用秒顺序代替Fourier的变换周期的统计，周期光谱密度(CSD)功能被用作重建的物理数量在不建议了技术，而不是光谱或力量光谱压力的密度信号。由CSD功能的解调能力的优点，重建的CSD能有效地分别地表示modulating和搬运人波浪的信息。模拟和实验说明这cyclostationary的有效性和精确性不技术满足工程的请求。

简介：Higher-orderalmostcyclostationarycomplexprocessesarecomplexrandomsignalswithalmostperiodicallytime-varyingstatistics,whichisimportanttotheresearchofnon-Gaussiansignalsininformationsystem.Intinspaper,smoothedpolyperiodogramsareproposedforrelatedtocyclicpolyspectralestimationandareshowntobeconsistentandasymptoticallycomplexnormal.Asymptoticcovarianceexpressionsarederivedalongwiththeircomputableforms.

简介：Onenearfieldacousticholography(NAH)techniqueisproposedforanalyzingcyclostationarysoundfield.Thesignalofthiskindofsoundfieldhasveryseriousmodulationphenomenongenerally,inspectrumofwhichobvioussidebandsexist.ItisdifficultforthetraditionalNAHtopossessdemodulationfunction,sovirtualpowerofsidebandsexistsinitshologram.ReplacingtheFourier'stransformwiththesecond-ordercyclicstatistics,theproposedNAHtechniqueusesthecyclicspectrumdensity(CSD)functionasreconstructedphysicalquantity,insteadofthespectrumorpowerspectrumdensityofsoundpressuresignal.TheCSDfunctioncandemodulatecyclostationarysignals,whichmakesnovirtualpowerofsidebandsinitshologram.TheresultsofsimulationandexperimentshowthattheproposedNAHcanextractmoreinformationaboutcyclostationarysoundfieldthantraditionalNAH,bywhichsoundfieldcanbeknownmoreclearly.

简介：Oneofthemainrequirementsofcognitiveradiosystemsistheabilitytodetectthepresenceoftheprimaryuserwithfastspeedandpreciseaccuracy.Toachievethat,apossibletwo-stagespectrumsensingschemeissuggestedinthispaper.Morespecifically,afastspectrumsensingalgorithmbasedontheenergydetectionisintroducedfocusingonthecoarsedetection.Acomplementaryfinespectrumsensingalgorithmadoptsone-ordercyclostationarypropertiesofprimaryuser'ssignalsintimedomain.Sincetheone-orderfeaturedetectionisperformedintimedomain,thereal-timeoperationandlow-computationalcomplexitycanbeachieved.Also,itdrasticallyreduceshardwareburdensandpowerconsumptionasopposedtotwo-orderfeaturedetection.Thesensingperformanceoftheproposedmethodisstudiedandtheanalyticalperformanceresultsaregiven.Theresultsindicatethatbetterperformancecanbeachievedinproposedtwo-stagesensingdetectioncomparedtotheconventionalenergydetector.