基于单片机的电子换号牌的设计

毕业设计（论文）报告题目基于单片机的电子换号牌的设计系别专业班级学生姓名学号指导教师2013年4月THEAPPLICATIONOFELECTRONICNOSEABSTRACTPOSITIVEHUMANOLFACTORYORGANISACOMPLEXPHYSIOLOGICALREACTIONNATUREKINDOFODORTENSOFTHOUSANDS,EVENPROFESSIONALSSPECIALIZINGINODORIDENTIFICATIONWORKISOFTENRECOGNITIONERRORSKEYWORDSELECTRONICNOSECOMPUTERTECHNOLOGYDEVELOPEDOLFACTORYORGANSPHYSIOLOGICALREACTION“CUSTOMS“SECURITYMONITORINGNEURALNETWORKSODORSENSORIDENTIFICATIONINFRAREDSENSORINTRODUCTIONTHEELECTRONICNOSEDEVELOPEDAHIGHTECHPRODUCTSISSIMULATEDANIMALOLFACTORYORGAN,SCIENTISTSARESTILLNOTALLCLEARPRINCIPLEOFTHEANIMALSSENSEOFSMELLHOWEVER,WITHTHEDEVELOPMENTOFSCIENCEANDTECHNOLOGY,MOREAUTHORITATIVESOMEUNIVERSITIESINTHEWORLDHAVEDEVELOPEDELECTRONICNOSEHASAWIDERANGEOFAPPLICATIONS,MOSTNOTABLYTOTHENUMBEROFTHEUNIVERSITYOFHAMBURG,GERMANY,HASABSOLUTEAUTHORITYINTHESENSORFIELDINTHEWORLDTODAYELECTRONICNOSEISTHERESPONSEPATTERNTOIDENTIFYTHEODOROFTHEGASSENSORARRAYELECTRONICSYSTEMS,ITCANBEINAFEWHOURS,DAYSOREVENAFEWMONTHSTIMEWITHINACONTINUOUS,REALTIMEMONITORINGOFTHESPECIFICLOCATIONOFTHEODORCONDITIONIDENTIFYINGODORMAINMECHANISMOFTHEELECTRONICNOSEISTHATEACHSENSORINTHEARRAYHASADIFFERENTSENSITIVITYOFTHEMEASUREDGASTHECOREOFTHEDEVICEOFTHEELECTRONICNOSEGASSENSORGASSENSORSBASEDONTHEPRINCIPLEOFDIFFERENTTYPEOFMETALOXIDE,ELECTROCHEMICAL,ANDCONDUCTIVEPOLYMERTYPE,QUALITY,PHOTOIONIZATIONTYPECANBEDIVIDEDINTOMANYTYPESCURRENTLYTHEMOSTWIDELYUSEDISAMETALOXIDEHOWITWORKSELECTRONICNOSEISMAINLYCOMPOSEDOFTHREEPARTSOFAGASSENSORARRAY,SIGNALPREPROCESSINGANDPATTERNRECOGNITIONPRESENTEDINFRONTOFANACTIVEMATERIALOFTHESENSOR,ANODORSENSORCHEMICALINPUTISCONVERTEDINTOANELECTRICALSIGNALBYAPLURALITYOFSENSORRESPONSETOANODORTHEYCONSTITUTETHESENSORARRAYTOTHEODOROFTHERESPONSESPECTRUMOBVIOUSLY,THEVARIOUSCHEMICALCOMPONENTSINTHEODORSENSITIVEMATERIALSPLAYAROLEINTHISRESPONSESPECTRUMFORABROADSPECTRUMOFODORRESPONSESPECTRUMTOACHIEVETHEODORQUALITATIVEORQUANTITATIVEANALYSIS,THESENSORSIGNALMUSTBEAPPROPRIATEPRETREATMENTTOELIMINATENOISE,FEATUREEXTRACTION,SIGNALAMPLIFICATION,ETC,USINGASUITABLEPATTERNRECOGNITIONANALYSISMETHODFORPROCESSINGTHEREOFTHEORETICALLY,EACHODORWILLHAVEITSCHARACTERISTICRESPONSESPECTRUM,ACCORDINGTOITSCHARACTERISTICRESPONSESPECTRUMCANDISTINGUISHBETWEENSMALLSAMEODORWHILEGASSENSORSCONSTITUTINGTHEARRAYTOMEASURETHECROSSSENSITIVITYOFAVARIETYOFGASES,BYASUITABLEANALYTICALMETHOD,THEMIXEDGASANALYSISTHEELECTRONICNOSEISTHEUSEOFVARIOUSGASSENSINGDEVICEHASARESPONSETOTHISCHARACTERISTICBUTDIFFERENTFROMEACHOTHER,WITHTHEDATAPROCESSINGMETHODSTOIDENTIFYAVARIETYOFODOR,ODORQUALITYANALYSISANDEVALUATIONOFCOMPLEXCOMPONENTGASESTHEMAINMECHANISMOFTHEELECTRONICNOSEIDENTIFYEACHSENSORINTHEARRAYHASADIFFERENTSENSITIVITYOFTHEMEASUREDGAS,EG,HIGHRESPONSEONEGASMAYBEGENERATEDONASENSOR,WHILETHEOTHERSENSORISALOWRESPONSESIMILARLY,THE2NDGASGENERATINGHIGHRESPONSEOFTHESENSORISNOTSENSITIVEONTHE1STGASAND,ULTIMATELY,THEENTIRESENSORARRAYRESPONSEPATTERNISDIFFERENTFORDIFFERENTGASES,ITISTHISDIFFERENCE,TOENABLETHESYSTEMTOIDENTIFYAGASACCORDINGTOTHERESPONSEOFTHESENSORPATTERNELECTRONICNOSECANBESUMMARIZEDASFOLLOWSTHESENSORARRAYTHESIGNALPREPROCESSINGNEURALNETWORKSANDAVARIETYOFALGORITHMSCOMPUTERIDENTIFICATIONGASQUALITATIVEANDQUANTITATIVEANALYSISFUNCTIONALLYSPEAKING,THEGASSENSORARRAYISEQUIVALENTTOTHEBIOLOGICALOLFACTORYSYSTEMINALARGENUMBEROFOLFACTORYRECEPTORCELLS,NEURALNETWORKANDTHECOMPUTERTORECOGNIZETHEBIOLOGICALEQUIVALENTOFTHEBRAIN,THERESTISTHEEQUIVALENTOFTHEOLFACTORYNERVESIGNALTRANSDUCTIONSYSTEMFOREXAMPLE,THEMEASUREMENTOFTHEMETALOXIDESEMICONDUCTORMOSGASSENSORINRESPONSETOTHESCHEMATICDIAGRAMOFTHEVOLTAGESIGNALMOSGASSENSORISUSUALLYBEFORETHETESTSHALLBEHEATEDTO2500ORHIGHERINORDERTOWORKPROPERLYAFTERTHECHEMICALREACTIONOCCURSINTHETESTINGPROCESS,THEMOSGASSENSORANDTHESAMPLEGAS/ODOR,WILLCHANGEITSOWNGASSENSITIVEFILMCONDUCTIVITYANDRESISTANCEVALUES,LEADINGTOATERMINALVOLTAGEOFASAMPLINGRESISTORINSERIESTHERETOISCHANGEDDUETOSAMPLINGRESISTORISFIXED,SOTHEIMMEDIATEEXTRACTIONOFTHEENDOFTHESAMPLINGRESISTORVOLTAGESIGNALVOLTAGEMOSGASSENSORRESPONSECURVE图1FEATURESELECTRONICNOSERESPONSETIME,SPEEDDETECTION,UNLIKEOTHERINSTRUMENTS,SUCHASTHEGASCHROMATOGRAPHYSENSORS,HIGHPERFORMANCELIQUIDCHROMATOGRAPHYSENSORNEEDCOMPLEXPRETREATMENTPROCESSMEASURINGAWIDERANGEOFASSESSMENT,ITCANDETECTAVARIETYOFDIFFERENTTYPESOFFOODANDTOAVOIDHUMANERROR,GOODREPEATABILITYCANDETECTSOMEOFTHEHUMANNOSECANNOTDETECTTHEGAS,SUCHASPOISONGASORSOMEIRRITANTGASES,WHICHINMANYAREAS,ESPECIALLYINTHEFOODINDUSTRYPLAYSANINCREASINGLYIMPORTANTROLEANDGRAPHICALCOGNITIVEEQUIPMENTTOHELPITSSPECIFICITYGREATLYENHANCETHEDEVELOPMENTOFSENSORMATERIALSALSOCONTRIBUTEDTOTHEIMPROVEMENTOFITSREPETITIVE,ANDWITHTHEIMPROVEMENTOFBIOCHIPS,BIOTECHNOLOGYDEVELOPMENTANDINTEGRATIONTECHNOLOGY,ANDSOMENANOMATERIALSTHEAPPLICATIONOFELECTRONICNOSEWILLHAVEBROADAPPLICATIONPROSPECTSMANYDIFFERENTTYPESOFTHEELECTRONICNOSE,THETYPICALWORKINGPROGRAMTHATIS1SENSORINITIALIZATIONUSINGAVACUUMPUMPTOTHEAIRSAMPLINGLESSONSTOSMALLCONTAINERSFITTEDWITHTHEELECTRONICSENSORARRAYCHAMBER2DETERMINATIONOFTHESAMPLEANDDATAANALYSISSAMPLINGOPERATIONUNITTHEINITIALIZATIONOFTHESENSORARRAYISEXPOSEDTOTHEODORBODY,WHENTHECONTACTWITHTHESURFACEOFTHEACTIVEMATERIALOFTHEVOLATILECOMPOUNDSVOCANDTHESENSORULTRASONICSENSOR,TOPRODUCETHETRANSIENTRESPONSESUCHARESPONSEISRECORDEDANDTRANSMITTEDTOTHESIGNALPROCESSINGUNITFORANALYSIS,ANDSTOREDINTHEDATABASEOFALARGENUMBEROFVOCPATTERNCOMPARISON,IDENTIFICATION,ANDTODETERMINETHETYPEOFODOR3CLEANINGTHESENSORMEASUREDAFTERTHESAMPLEISFINISHED,USEOFALCOHOLVAPOR“FLUSH“THESENSORSURFACEOFTHEACTIVEMATERIAL,THEODORREMOVALMEASUREDBIMIXTUREINENTERNEWMEASUREMENTPRIORTOTHENEXTROUND,THESENSORSTILLIMPLEMENTINITIALIZATIONTHATIS,BETWEENTHEWORKAGAIN,EACHSENSORAREREQUIREDDRYAIRORSOMEOTHERREFERENCEGASCLEANINGTOMEETTHEBENCHMARKSTHEMEASUREDODOREFFECTOFTIMEISCALLEDTHE“RESPONSETIME“ANDTHEPURGEPROCESSANDTHEREFERENCEGASROLEOFTHESENSORARRAYUSEDINTHEINITIALIZATIONPROCESSTIMEISCALLEDTHE“RECOVERYTIME“THEELECTRONICNOSESYSTEMCONSISTSOFTWOPARTSINFORMATIONACQUISITIONTHETERMINALACQUISITIONTERMINALANDINFORMATIONPROCESSINGTERMINALPROCESSINGTERMINAL1COLLECTIONTERMINALISRESPONSIBLEFORCOLLECTINGTHESIGNALVOLTAGEOFTHEMOSGASSENSORARRAYPROCESSINGTERMINALVIAAHANDHELDCOMPUTERPDATOCOMPLETETHEDATAANALYSISANDPROCESSINGTWOTERMINALDATAEXCHANGEDBYTHETWOWIRELESSMODULECOLLECTIONTERMINALCONSISTSOFTHREEMAJORCOMPONENTSSAMPLINGSYSTEMS,ELECTRICALSYSTEMS,ASWELLASTHESENSORARRAYTHESAMPLINGSYSTEMCONSISTSOFTHETHEGASSENSORCAVITY,MICROPLOWPUMPANDTHREEWAYSOLENOIDVALVECIRCUITSYSTEMCONSISTSOFTHEFOLLOWINGFUNCTIONALMODULESANDPOWERTHEMICROCONTROLLER,ADCMODULE,DACCONDITIONINGMODULES,MEMORYMODULES,WIRELESSMODULESANDLITHIUMBATTERYTHESENSORCAVITYISPLACEDWITHTHESENSORARRAYCONSISTINGOFEIGHTCOMMERCIALTGSGASSENSORTHEWORKOFENERGYCONSUMPTIONOFTHEENTIRECOLLECTIONTERMINALIN6WABOUT,INTHECASEOFTHELITHIUMBATTERYWITHACAPACITYOF3800AMHPOWERED,ITISPOSSIBLETOWORKCONTINUOUSLY32HCOLLECTIONSOFTWAREONTHETERMINALISUNDERINKEILUVISION2DEVELOPMENTENVIRONMENTWITHCLANGUAGE,WRITINGGOODSOURCETHROUGHTHERS232SERIALBURNTOSTC89C516KDMICROCONTROLLER2PROCESSINGTERMINALBYAWIRELESSMODULE,MICROCONTROLLER,RS232SERIALPORTTOUSBINTERFACEMODULERS232TOUSB,ANDPDAWIRELESSMODULEWITHMICROCONTROLLERINTERFACEDESIGNCIRCUITWITHTHECOLLECTIONTERMINALBECAUSEONPDAUSBINTERFACE,ITISNECESSARYTODESIGNRS232SERIALTOUSBINTERFACECIRCUITTOCOMPLETETHEEXCHANGEOFDATABETWEENTHEMCUANDPDASTHEELECTRONICNOSESYSTEMSOFTWAREISDIVIDEDINTOTWOPARTSTHESOFTWAREISBASEDONPROCESSINGTERMINALLABVIEW71NATIONALINSTRUMENTATION,USAPLATFORMDEVELOPEDONAPDAWINDOWSXPSYSTEMELECTRONICNOSEINACOMPLETETESTPROCESS,HAVETOGOTHROUGHFOURSTAGESGROUNDSTATEPHASESAMPLINGSTAGE,HOLDINGPHASEANDTHERECOVERYPHASESTAGEINTHEBASESTATE,THETHREEWAYSOLENOIDVALVEISSWITCHEDTOTHEAIRPASSAGE,ANDAVOLTAGESIGNALOFTHESENSORARRAYTOAHORIZONTALBASELINETOBEATHREEWAYSOLENOIDVALVEISSWITCHEDTOTHESAMPLEGASCHANNELINTOTHESAMPLINGSTAGE,THESENSORARRAYRESPONSE,AFEWSECONDSAFTERTHEVOLTAGESIGNALVALUEROSETOAPEAKANDTHENSTABILIZED,TOBECLOSETOEQUILIBRIUM,THESYSTEMENTERSTHEHOLDINGPHASE,THREEWAYSOLENOIDVALVEREMAINSCONSTANTSAMPLEGASCHANNELTHELASTTOENTERTHERECOVERYPHASE,THREEWAYSOLENOIDVALVEISSWITCHEDBACKTOTHEAIRCHANNEL,RAPIDDECLINEINTHERESPONSECURVEOFTHESENSORARRAY,UNTILITISRESTOREDTOTHEBASELINEPOSITIONTHETIMEVALUEOFTHEFOURSTAGESOFTHETESTINGPROCESSBEFORETHESTARTOFTHETEST,YOUMUSTFIRSTSETONAPDATHEGROUNDSTATETIME,SAMPLINGTIME,HOLDTIMEANDRECOVERYTIMETHEN“START“COMMAND,THESYSTEMBEGANTOTESTISSUEDBYTHEPDACOLLECTIONTERMINALAFTERRECEIVINGTHEORDER,THEINTERNALMICROCONTROLLERINACCORDANCEWITHTHEINSTRUCTIONS,TIMERTOCONTROLTHETIMEOFTHEFOURSTAGESFIRST,THEMICROCONTROLLERWILLCONTROLTHEHEATINGVOLTAGEOFDACCONDITIONINGMODULEOUTPUTISUSEDTOHEATTHESENSORARRAY,ANDTHENCONTROLTHESIGNALVOLTAGEOFTHEADCMODULEACQUISITIONSENSORARRAYANDCHOOSETOGETTHEDATASAVEDTOTHEMEMORYMODULE,ORDIRECTLYTOTHERECEIVERBYTHEWIRELESSMODULEFEEDBACKPDATODOTHEANALYSISANDPROCESSINGAFTERTHEDATAACQUISITIONISCOMPLETED,PDASENDS“END“COMMAND,THESYSTEMSTOPSWORKINGDURINGTHENEXTROUNDOFTESTINGISREQUIREDBEFORETHEPDAISSUEA“RESET“COMMANDTOMAKETHESYSTEMCLEAREDBEFOREANEWROUNDOFTESTSINADDITION,INTHEPROCESSOFTESTING,YOUCANISSUECOMMANDSTHROUGHTHEPDAMINIATUREAIRPUMPANDTHREEWAYELECTRONICVALVECONTROLSWITCHAFTERTHETESTISSTOPPED,THEDATAOFTHESENSORARRAYCANBEREADTHROUGHTHE“OPEN“INSTRUCTIONTOOBTAINBEFOREANDRESPONSECURVESSTATISTICALMETHODSPRINCIPALCOMPONENTANALYSISPCAPRINCIPALCOMPONENTANALYSISREFERSTOSEVERALVARIABLESBYLINEARTRANSFORMATIONTOELECTAFEWERNUMBEROFIMPORTANTVARIABLESOFMULTIVARIATESTATISTICALANALYSISMETHOD,ALSOKNOWNASPRINCIPALCOMPONENTANALYSISITISASTATISTICALANALYSISMETHODTOGRASPTHEPRINCIPALCONTRADICTIONOFTHINGS,YOUCANPARSEOUTTHEMAININFLUENCINGFACTORSFROMDIVERSETHINGS,TOREVEALTHENATUREOFTHINGS,SIMPLIFYCOMPLEXPROBLEMSPCAASALINEARFEATUREEXTRACTIONTECHNOLOGY,DESIGNEDTOEXPLOITDATADIMENSIONALITYREDUCTIONIDEATHATTHECALCULATIONOFTHEMAINCOMPONENTSANDHIGHDIMENSIONALDATAISPROJECTEDONTOALOWERDIMENSIONALSPACE,MULTIINDICATORSINTOAFEWINDICATORS,SOASMUCHASPOSSIBLETOSHOWTHEINFORMATIONCONTAINEDINTHEORIGINALDATAPCAINELECTRONICNOSESFORANOBJECTIVEANALYSISOFTHEDIFFERENCESBETWEENTHESAMPLESCLUSTERANALYSISCACLUSTERANALYSISISAKINDOFSUBJECTSWEREDIVIDEDINTORELATIVELYHOMOGENEOUSGROUPSOFSTATISTICALANALYSISTECHNIQUESFROMASTATISTICALPOINTOFVIEW,THECLUSTERANALYSISISAWAYTOSIMPLIFYTHEDATATHROUGHDATAMODELINGCAISCLASSIFIEDBASEDONTHERELATIONSHIPBETWEENTHENUMBEROFTHEINDIVIDUALORVARIABLETHEOBJECTIVITYSTRONG,BUTVARIOUSCLUSTERINGMETHODSCANONLYBEACHIEVEDUNDERCERTAINCONDITIONSTHELOCALOPTIMUMWHETHERTHEFINALRESULTOFTHECLUSTERINGTOSETUP,TAKEIDENTIFICATIONOFEXPERTSDISCRIMINANTFACTORANALYSISDFATHEJUDGMENTFACTORANALYSISISASTATISTICALMETHODTODETERMINETHEINDIVIDUALCATEGORYOBSERVATIONSACCORDINGTOTHEKNOWNCLASSESOFTWOORMORESAMPLESTODETERMINEONEORMORELINEARDISCRIMINANTFUNCTIONDISCRIMINANTINDEX,THENANOTHERBODYTODETERMINEWHICHCATEGORYTHEDISCRIMINANTFUNCTIONBASEDONDISCRIMINATIONINDEXDFAACCORDINGTOTHERESULTSOFTHESTANDARDSAMPLEUSEDFORELECTRONICNOSESTOIDENTIFYBLINDBYRECOMBINATIONOFSENSORDATATOOPTIMIZETHEDIFFERENTIATED,ORTHROUGHTHESENSOROPTIMIZEDSELECTION,IEREMOVALOFTHESENSOR,NOCONTRIBUTIONORASMALLCONTRIBUTIONTOIMPROVETHERECOGNITIONCAPABILITY,ITSPURPOSEISTOBRINGTHEGROUPDISTANCEBETWEENTHEDIFFERENCEWITHINAMAXIMUMWHILEENSURINGGROUPMINIMUMARTIFICIALNEURALNETWORKANNTHEARTIFICIALNEURALNETWORKISABYIMITATEHUMANORANIMALNEURALNETWORKBEHAVIORALCHARACTERISTICS,MATHEMATICALMODELOFDISTRIBUTEDPARALLELINFORMATIONPROCESSINGTHISNETWORKRELIESONASYSTEMOFCOMPLEXPROCEDURES,BYADJUSTINGTHETHEINTERNALLARGENUMBEROFINTERCONNECTEDRELATIONSHIPSBETWEENNODES,SOASTOACHIEVETHEPURPOSEOFPROCESSINGINFORMATIONANNPROVIDEDINADVANCEANUMBEROFMUTUALLYCORRESPONDINGINPUTDATAOUTPUTDATA,ANALYSIS,GRASPTHEPOTENTIALBETWEENTHELAW,ANDULTIMATELYWITHANEW“INPUTDATA“ACCORDINGTOTHESELAWS,TODERIVEOUTPUT,THISTHELEARNINGPROCESSOFANALYSISISCALLED“TRAINING“THEAFOREMENTIONEDMETHODS,ANNISUSUALLYCONSIDEREDTOBEAPROMISINGAPPROACH,ANDITSFEATURESANDBENEFITSMAINLYINTHREEASPECTSWITHSELFLEARNINGANDADAPTIVEFUNCTIONASSOCIATIVEMEMORYFUNCTIONWITHHIGHSPEEDTOFINDTHEOPTIMALSOLUTIONCAPACITYINADDITION,ITISABLETOSOLVENONLINEARPROBLEMSBETTERTHANTHETRADITIONALSTATISTICALMETHODSINDEALINGWITHNOISEANDDRIFTCURRENTLY,MANYARTIFICIALNEURALNETWORKISUSEDFORPROCESSINGTHESIGNALOFTHESENSORARRAY,SUCHASBPNEURALNETWORK,RADIALBASISNEURALNETWORKS,FUZZYNEURALNETWORKS,SELFORGANIZINGNETWORK图2RESEARCHPROGRESSIN1964,WILKENSANDHATMANUSEOFGASONTHEELECTRODE,THEOXIDATIONREDUCTIONREACTIONOLFACTORYPROCESSELECTRONICANALOG,WHICHISTHEEARLIESTREPORTSONTHEELECTRONICNOSE1965,BUCKETALTHEUSEOFCHANGESINTHECONDUCTANCEOFAMETALANDASEMICONDUCTORGASMEASUREMENT,DRAVIEKSTHENUSINGTHECHANGEINCONTACTPOTENTIALMEASUREMENTSOFTHEGASHOWEVER,ASTHEGASCLASSIFICATIONWITHTHECONCEPTOFINTELLIGENTCHEMICALSENSORARRAYSUNTIL1982BYTHEUNIVERSITYOFWARWICK,UKPERSUADETAL,THEIRELECTRONICNOSESYSTEMCONSISTSOFTWOPARTSOFTHEGASSENSORARRAYANDPATTERNRECOGNITIONSYSTEMTHESENSORARRAYPARTOFTHEMOUNTAINTHREESEMICONDUCTORGASSENSORTHISSIMPLESYSTEMCANDISTINGUISHBETWEENTHEBRAIN,ACCORDINGTOTHETREEROSEOIL,CLOVEOIL,VOLATILECHEMICALSUBSTANCESFORDENTALODORINTHENEXTFIVEYEARS,THEELECTRONICNOSERESEARCHANDDIDNOTCAUSEEXTENSIVEATTENTIONINTHEINTERNATIONALACADEMICCOMMUNITY1987,HELDATTHEUNIVERSITYOFWARWICK,UK8THANNUALMEETINGOFTHEEUROPEANCHEMICALSENSINGRESEARCHORGANIZATIONISTHETURNINGPOINTOFTHESTUDYOFTHEELECTRONICNOSEATTHISMEETING,GARDNERLEDBYTHEUNIVERSITYOFWARWICKINGASSENSINGSENSINGRESEARCHTEAMPUBLISHEDAPAPERSENSORINGASMEASUREMENTPARTIESANDAPPLICATIONS,FOCUSINGONTHECONCEPTOFPATTERNRECOGNITION,CAUSEDACADEMIAEXTENSIVEINTERESTIN1989,THENORTHATLANTICTREATYORGANIZATIONHELDASPECIALCHEMICALSENSORINFORMATIONPROCESSINGSYMPOSIUM,DEDICATEDTOARTIFICIALOLFACTORYSYSTEMDESIGNOFTHESETWOTOPICSINAUGUST1991,THENORTHATLANTICTREATYORGANIZATIONWASHELDINICELANDTHEMATICSESSIONSOFTHEFIRSTELECTRONICNOSETHEELECTRONICNOSESINCETHENRAPIDDEVELOPMENT1994,GARDNEAREVIEWARTICLEONTHEELECTRONICNOSE,FORMALLYPROPOSEDTHECONCEPTOF“ELECTRONICNOSE“,MARKINGTHEELECTRONICNOSETECHNOLOGYINTOTHEMATURESTAGEOFDEVELOPMENTSINCE1994,AFTERMORETHANTENYEARSOFDEVELOPMENT,THEELECTRONICNOSERESEARCHHASMADERAPIDPROGRESSFORTHESTUDYOFTHEELECTRONICNOSEMAINLYINTHESIDEOFTHESENSORANDELECTRONICNOSEHARDWAREDESIGN,PATTERNRECOGNITIONTHEORY,THEELECTRONICNOSEINFOOD,AGRICULTURE,PHARMACEUTICAL,BIOFIELDAPPLICATIONS,ELECTRONICNOSE,ANDBIOLOGICALSYSTEMSWHILETHEHARDWAREDESIGNOFTHESENSORANDTHEELECTRONICNOSEANDELECTRONICNOSEAPPLICATIONSINTHEFIELDOFFOODANDAGRICULTUREISAHOTSPOTINTHESTUDYOFTHEELECTRONICNOSECREWETOWNINCHESHIRE,ENGLANDCOMPANYSUCCESSFULLYDEVELOPEDANELECTRONICNOSE,THETESTSSHOWEDTHATTHEPATIENTSKINWOUNDBACTERIACAN“SNIFF“OUTTHEEROSIONREMINDDOCTORSTOTAKEAPPROPRIATEMEASURESINATIMELYMANNERHISSTUDYOFTHEELECTRONICNOSEISAMATRIXCOMPOSEDOF32DIFFERENTORGANICPOLYMERSENSOR,VERYSENSITIVETOTHESMELLOFVARIOUSVOLATILECOMPOUND,THECOMPOUNDISDIFFERENT,THEREACTIONISDIFFERENTTYPICALLY,BACTERIALGROWTHWILLBEISSUEDCHEMICALODORTHEELECTRONICNOSECONTACTWITHTHEODOR,EACHSENSORRESISTANCEWILLCHANGE“FORMAT“SINCEEACHSENSORCORRESPONDINGTOADIFFERENTKINDOFCHEMICALSUBSTANCES,COMPOSEDOF32KINDSOFTHESAMECHANGEINRESISTANCEWOULDRESPECTIVELYREPRESENTTHE“FINGERPRINT“OFTHEDIFFERENTODORSTHETESTSSHOWEDTHATTHEELECTRONICNOSECANBEFOUNDIFTHEREISTHEPRESENCEOFBACTERIAONLYNEEDAFEWHOURSINTHEPAST,USINGTHEMETHODSOFTHELABORATORYTESTS,USUALLY13DAYSTOGETTHERESULTSTHEGASSENSORSINTHESYSTEMOFEL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