development of success measures for CHKMS.docx

development of success measures for CHKMS.docx

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developmentofsuccessmeasuresforCHKMS

AnorganiccatalyticCVD:

Principle,apparatusandapplications  OriginalResearchArticle

ThinSolidFilms,Volume516,Issue5,15January2008,Pages558-563

TsuyoshiHata,HiroshiNakayama

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731

AudiosteganalysiswithHausdorffdistancehigherorderstatisticsusingarulebaseddecisiontreeparadigm  OriginalResearchArticle

ExpertSystemswithApplications,Volume37,Issue12,December2010,Pages7469-7482

S.Geetha,N.Ishwarya,N.Kamaraj

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AbstractAbstract|Figures/TablesFigures/Tables|ReferencesReferences

Abstract

Theaimofthispaperistoconstructapracticalforensicsteganalysistoolforaudiosignalsthatcanproperlyanalyzethestatisticsdisturbedbystegoembeddingandclassifythemtoselectedcurrentsteganographicmethods.Theobjectiveofthispaperistoprovethatthechoiceofeffectivestegosensitivefeaturesandaproficientmachinelearningparadigmenhancesthedetectionaccuracyofthesteganalyser.Inthispaperarulebasedapproachwithafamilyofsixdecisiontreeclassifiersviz.,AlternatingDecisionTree,DecisionStump,J48,LogicalModelTree,NaïveBaye’sTreeandFastDecisionTreelearner,toperformthedetectionofaudiosubliminalchannelisintroduced.InparticularthehigherorderstatisticsextractedfromtheHausdorffdistanceareinvestigatedforanimprovementofthedetectionperformance,ascompetentaudiosteganalyticfeatures.Theevaluationoftheenhancedfeaturespaceandthedecisiontreeparadigm,onadatabasecontaining4800cleanandstegoaudiofilesisperformedforclassicalsteganographicaswellasforwatermarkingalgorithms.WiththisstrategyitisshownhowgeneralforensicapproachcandetectinformationhidingtechniquesinthefieldofcovertcommunicationaswellasforDRMapplications.Forthelattercase,thedetectionofthepresenceofapotentialwatermarkinaspecificfeaturespacecanleadtonewattacksortoabetterdesignofthewatermarkingpattern.

ArticleOutline

1.Motivationandtheapplicationscenarioofaudiosteganalysis

2.Literaturereview

3.Proposedsteganalyser

3.1.Preprocessingoftheaudiosignal

3.2.Featureextraction

3.3.Post-processingoftheresultingfeaturevectors

3.4.Machinelearningparadigm

4.Testscenario

4.1.Testsetsandtestset-up

4.1.1.Informationhidingalgorithmsused

4.1.2.Testfiles

4.1.3.Machinelearningparadigmused

4.2.Testprocedure

4.2.1.Pre-processingoftheaudiodata

4.2.2.Featureextractionformthesignal

4.2.3.Post-processingoftheresultingfeaturevectors

4.2.4.Analysis

4.3.Resultsanddiscussion

4.4.Influenceofembeddingrate

5.Summaryandconclusions

Acknowledgements

References

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732

Controloflinearmotormachinetoolfeeddrivesforendmilling:

RobustMIMOapproach  OriginalResearchArticle

Mechatronics,Volume15,Issue10,December2005,Pages1207-1224

ChintaeChoi,Tsu-ChinTsao

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Abstract

Linearmotorsaregettingpromisingforuseashighspeed,highaccuracymachinetoolfeeddrives.Thecuttingforceinthemachiningprocessaredirectlyreflectedtothelinearmotorduetonogearingmechanism.Toachievehighaccuracymachining,thecontrollerforthelinearmotorsystemshouldbedesignedtocompensateforthecuttingforce.

Inthiswork,aMIMOH∞controllerforthelinearmotormachinetoolfeeddriveshasbeendesignedtoreducetrackingerrorsinducedbycuttingforcesforendmilling.Thecontrollerisdesignedusingnormalizedcoprimefactorizationmethodforthedynamicmodelofthelinearmotorsystem.Themodelincludesconstantin-lineandcrosscouplingforcegain,sincethefeedbackcuttingforcecanbeconsideredastheproductoftheconstantgainandthemovingvelocityofeachaxis.

Analysisofthestructuredsingularvalueshowsthatthedesignedcontrollerhasgoodrobustperformancedespitewidevariationsofthecuttingforceandphysicalparameters.ItisdirectlyimplementedonalinearmotorX–YtablewhichismountedonamillingmachinetohavecuttingexperimentsviaaDSPboard.Experimentalresultsverifiedeffectivenessoftheproposedschemetosuppresstheeffectsofthecuttingforceinthehighfeedrate.

ArticleOutline

1.Introduction

2.Modelingofthelinearmotorsystem

3.H∞loopshapingcontrollerdesign

4.Analysisofrobustness

5.Experimentalresults

6.Conclusions

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733

Capacitivesensor-basedfluidlevelmeasurementinadynamicenvironmentusingneuralnetwork  

EngineeringApplicationsofArtificialIntelligence,Volume23,Issue4,June2010,Pages614-619

EdinTerzic,C.R.Nagarajah,MuhammadAlamgir

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AbstractAbstract|Figures/TablesFigures/Tables|ReferencesReferences

Abstract

Ameasurementsystemhasbeendevelopedusingasingletubecapacitivesensortoaccuratelydeterminethefluidlevelinnon-stationarytanks,namelyautomotivefueltanks.Thesystemdeterminesthefluidlevelinthepresenceofdynamicslosh.Aneuralnetwork-basedapproachisusedtoprocessthesensorsignalandachievesubstantialaccuracycomparedwiththeaveragingmethod,whichisnormallyusedundersuchconditions.Thesensorreadingswereobtainedbyexperimentationcarriedoutundervariousdynamicconditions.Thesensorresponsewasrecordedatvarioussloshfrequenciesandfuelvolumes;whichwasthenusedtotrainthreedifferentneuralnetworktopologies.FieldtrialswerecarriedouttoobtaintheactualdrivingdataforthepurposeoftestingtheneuralnetworksusingMATLABsoftware.Onestaticneuralnetworktopology,namelyFeed-forwardBackpropagationNeuralNetwork,andtwodynamicneuralnetworktopologies,namelyDistributedTimeDelayNeuralNetworkandNARXNeuralNetwork,havebeeninvestigatedinthiswork.Thedevelopedfluidlevelmeasurementsystemiscapableofdeterminingthefluidlevelinadynamicenvironmentwithamaximumerrorof8.7%byusingthetwodynamicneuralnetworks,and0.11%usingthestaticfeed-forwardbackpropagationneuralnetwork.

ArticleOutline

1.Introduction

2.Neuralnetwork-basedapproach

3.Networktopologies

3.1.Staticfeed-forwardnetwork

3.2.Dynamicneuralnetwork

4.Experimentalsetup

4.1.Trainingdata

4.2.Testdata

5.Simulationresults

6.Summary

7.Futurework

References

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734

Standardizationofisletisolationoutcome–Anewautomaticsystemtodeterminepancreaticisletviability  OriginalResearchArticle

ExpertSystemswithApplications,Volume38,Issue4,April2011,Pages3461-3466

GiadaLaScalia,GiuseppeAiello,CristianaRastellini

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Abstract

Pancreaticislettransplantationisemergingasatherapeuticapproachforpatientsaffectedbydiabetes.Thisapproachconsistsofaminimallyinvasiveprocedurereplacinginsulin-producingcells（pancreaticislets）.Thetechniquehasbeenprovensuccessful,butlimitationshavebeenidentified.Oneofthemajorchallengesoftheprocedureisthecountingoftheisolatedpancreaticislets,whichiscurrentlyjeopardizedbysubjectivityandinaccuracy.Determinationoftheaccurateisletnumberisacrucialfactorindeterminingthecorrelationbetweentheisolationproductandclinicaloutcome.Intheproposedstudy,wehavedevelopedsoftwarecapableofobjectivelyevaluatingisletnumbersandotherviabilityvariablesbyimageanalysis.Thissoftwareisbasedonimageprocessingandfeatureextractionalgorithmsforrecognitionoftheareaofinterest.Thisisthefirststeptowardstandardizationoftheisolationoutcomeandpotentialclinicalsuccesspredictability.

ArticleOutline

1.Introduction

2.Theory

3.Methods

3.1.Imageanalysis

3.2.Imageacquisition

3.3.Imageprocessing

4.Featureextraction

5.Resultsanddiscussion

6.Conclusion

Acknowledgements

References

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Researchhighlights

►AutomaticallydeterminetheamountandthesizeofisletsofLangerhans.►Standardizationofthecountingoftheisolatedpancreaticislets.►Implementationofadditionalindicators.

735

Apixellatedγ-camerabasedonCdTedetectorsclinicalinterestsandperformances  OriginalResearchArticle

NuclearInstrumentsandMethodsinPhysicsResearchSectionA:

Accelerators,Spectrometers,DetectorsandAssociatedEquipment,Volume448,Issue3,1July2000,Pages537-549

J.Chambron,Y.Arntz,B.Eclancher,Ch.Scheiber,P.Siffert,M.HageHali,R.Regal,A.Kazandjian,V.Prat,S.Thomas,S.Warren,R.Matz,A.Jahnke,M.Karman,A.Pszota,L.Nemeth

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Abstract

Amobilegammacameradedicatedtonuclearcardiology,basedona15 cm×15 cmdetectionmatrixof2304CdTedetectorelements,2.83 mm×2.83 mm×2 mm,hasbeendevelopedwithaEuropeanCommunitysupporttoacademicandindustrialresearchcentres.Theintrinsicpropertiesofthesemiconductorcrystals–low-ionisationenergy,high-energyresolution,highattenuationcoefficient–arepotentiallyattractivetoimprovetheγ-cameraperformances.Buttheiruseasγdetectorsformedicalimag