简介：这份报纸在无线传感器网络为随机的地评价学习传感器选择问题。作者首先证明选择在D最佳的标准下面最小化评价错误的一套l传感器是NP完全的。作者建议一个反复的算法追求一个非最优的答案。而且，联网以便改进带宽和无线传感器的精力效率，作者与使量子化的大小和研究为一个Gaussian随机的领域建议一个最好的线性不偏的评估者相应传感器选择问题。在未知协变性矩阵的情况中，作者用大小为协变性矩阵建议一个评估者并且也分析这个评估者的敏感。模拟结果显示出建议算法的好表演。

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简介：由于连接增加，小世界现象在基于树的无线传感器网络存在。基于树的网络的小世界上的流行病被学习，并且流行病的爆发在发生的流行阀值是计算的。当感染概率比过滤阀值大时，流行病学的过程被分析。尽管不同流行病学的过程发生在内在的树拓扑学上，当感染传播，感染的节点的数字指数地增加。一致免疫过程在同类的小世界的网络被进行。尽管免疫有效地减少流行速度，感染仍然指数地延长。

简介：Wirelesssensornetworkspromiseanewparadigmforgatheringdataviacollaborationamongsensorsspreadingoveralargegeometricalregion.Manyapplicationsimposedelayrequirementsfordatagatheringandaskfortime-efficientschedulesforaggregatingsenseddataandsendingtothedatasink.Inthispaper,theauthorsstudytheminimumdataaggregationtimeproblemundercollision-freetransmissionmodel.Ineachtimeround,datasentbyasensorreachesallsensorswithinitstransmissionrange,butasensorcanreceivedataonlywhenitistheonlydatathatreachesthesensor.Thegoalistofindthemethodthatschedulesdatatransmissionandaggregationatsensorssothatthetimeforallrequesteddatatobesenttothedatasinkisminimal.TheauthorsproposeanewapproximationalgorithmforthisNP-hardproblemwithguaranteedperformanceratio(7Δ)/(Iog_2|S|)+c,whichsignificantlyreducesthecurrentbestratioofΔ-1,whereSisthesetofsensorscontainingsourcedata,Δisthemaximalnumberofsensorswithinthetransmissionrangeofanysensor,andcisaconstant.Theauthorsalsoconductextensivesimulation,theobtainedresultsjustifytheimprovementofproposedalgorithmovertheexistingone.

简介：Asanadvancedprocessdetectiontechnology,electricalimpedancetomography(EIT)haswideapplicationprospectsandadvantagesinmedicalimagingdiagnosis.However,aseriesofissuesneedtobeaddressedbeforeapplyingEITforbedsidemonitoring.Medicaldiagnosisandbedsidemonitoringaredynamicmeasuringprocess,wherethepositionsofmeasuringelectrodesandtheshapeofthedetectedfieldarechangingdynamical.Duetotheinabilitytocopewiththechangeableelectrodepositionsandvariousdynamicfields,existingEITsystemsaremainlyusedforindustrialdetectioninconditionofstaticmeasurementandvisualization.Inthispaper,weinvestigatethedynamicmeasurementandvisualizationofhumanbreastinEITfield,describethedesignofthemeasuringsensorsystem,andexpoundthemeasuringprinciple.Themaincomponentofthehardwaresystemisabuiltinservoelectricalresistancetomographysensorwithcapacitiveslidingrod,whichcanadapttothecrowdofdifferentchestcontourandthechangeofchestshapeinthedynamicprocessofbreathing.Thecorrespondingmeasuringprincipleisextractingallreal-timepositionsofmeasuringelectrodes,thenobtainingthedynamicboundary,finallydividingthedetectionfieldrapidly.Experimentalresultsconfirmedthattheproposedsystemcanobtainreal-timelocationofboundarysensoranddynamicallysolvetheproblemofarbitrary-shapeboundarymeasurement.Theimagingresultsvalidatetheavailabilityofdesignedsensorsystemandtheeffectivenessofthecorrespondingmeasuringprinciple.

简介：即时监视传感器的运作的状态并且差错的精确诊断是必要的。这份报纸为传感器建议一个改进差错诊断计划，它包括差错察觉和差错鉴定。第一，趋势分析与最少的广场结合了支持向量机器(TA-LSSVM)方法被建议并且实现了检测差错。第二，改正产量代码支持向量机器(ECOC-SVM)的一个改进错误基于差错鉴定方法被建议区分不同传感器失败模式。表明建议计划的有效性，实验与一个MTi系列传感器，和比较与另外的差错鉴定方法被做的一些被进行。试验性的结果证明建议差错诊断计划与察觉提供必要改进即时性质和更好的鉴定精确性。

简介：Thispaperaddressestheproblemonsensorfaultestimationandfault-tolerantcontrolforaclassofTakagi-SugenoMarkovianjumpsystems,whicharesubjectedtosensorfaultsandpartiallyunknowntransitionrates.First,theoriginalplantisextendedtoadescriptorsystem,wheretheoriginalstatesandthesensorfaultsareassembledintothenewstatevector.Then,anovelreduced-orderobserverisdesignedfortheextendedsystemtosimultaneouslyestimatetheimmeasurablestatesandsensorfaults.Second,byusingtheestimatedstatesobtainedfromthedesignedobserver,astate-feedbackfault-tolerantcontrolstrategyisdevelopedtomaketheresultingclosed-loopcontrolsystemstochasticallystable.Basedonlinearmatrixinequalitytechnique,algorithmsarepresentedtocomputetheobservergainsandcontrolgains.Theeffectivenessoftheproposedobserverandcontrollerarevalidatedbyanumericalexampleandacomparedstudy,respectively,andthesimulationresultsrevealthattheproposedmethodcansuccessfullyestimatethesensorfaultsandguaranteethestochasticstabilityoftheresultingclosed-loopsystem.