multiple organ retrieval and exchange MORE in Canada.docx

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multipleorganretrievalandexchangeMOREinCanada

236

Wavelet-basedproceduresforproteomicmassspectrometrydataprocessing  OriginalResearchArticle

ComputationalStatistics&DataAnalysis,Volume52,Issue1,15September2007,Pages211-220

ShuoChen,DonHong,YuShyr

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AbstractAbstract|Figures/TablesFigures/Tables|ReferencesReferences

Abstract

Proteomicsaimsatdeterminingthestructure,functionandexpressionofproteins.High-throughputmassspectrometry（MS）isemergingasaleadingtechniqueintheproteomicsrevolution.Thoughitcanbeusedtofinddisease-relatedproteinpatternsinmixturesofproteinsderivedfromeasilyobtainedsamples,keychallengesremainintheprocessingofproteomicMSdata.Multiscalemathematicaltoolssuchaswaveletsplayanimportantroleinsignalprocessingandstatisticaldataanalysis.Awavelet-basedalgorithmforproteomicdataprocessingisdeveloped.AMATLABimplementationofthesoftwarepackage,calledWaveSpect0,ispresentedincludingprocessingproceduresofstep-intervalunification,adaptivestationarydiscretewaveletdenoising,baselinecorrectionusingsplines,normalization,peakdetection,andanewlydesignedpeakalignmentmethodusingclusteringtechniques.ApplicationstorealMSdatasetsfordifferentcancerresearchprojectsinVanderbiltIngramCancerCentershowthatthealgorithmisefficientandsatisfactoryinMSdatamining.

ArticleOutline

1.Introduction

2.WaveletsandapplicationsinproteomicMSdataanalysis

2.1.WaveletsforMALDI-TOFMSData

2.2.Waveletdenoisingstrategy

2.3.Analysisonwaveletdomain

3.Method:

processingprocedures

3.1.Step-intervalunificationanddenoising

3.2.Baselinecorrectionandnormalization

3.3.Peakdetectionandalignment

4.Results

4.1.Peakselectionbasedmethodresults

4.2.Waveletcoefficientsanalysisresults

Acknowledgements

References

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237

Networkdesigntechniquesusingadaptedgeneticalgorithms  OriginalResearchArticle

AdvancesinEngineeringSoftware,Volume32,Issue9,September2001,Pages731-744

MitsuoGen,RunweiCheng,ShumuelS.Oren

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Abstract

Inrecentyearswehaveevidencedanextensiveeffortinthedevelopmentofcomputercommunicationnetworks,whichhavedeeplyintegratedinhumanbeing'severydaylife.Oneofimportantaspectsofthenetworkdesignprocessisthetopologicaldesignprobleminvolvedinestablishingacommunicationnetwork.However,withtheincreaseoftheproblemscale,theconventionaltechniquesarefacingthechallengetoeffectivelyandefficientlysolvethosecomplicatednetworkdesignproblems.Inthisarticle,wesummarizedrecentresearchworksonnetworkdesignproblemsbyusinggeneticalgorithms（GAs）,includingmultistageprocessplanning（MPP）problem,fixedchargetransportationproblem（fc-TP）,minimumspanningtreeproblem,centralizednetworkdesign,localareanetwork（LAN）designandshortestpathproblem.Alltheseproblemsareillustratedfromthepointofgeneticrepresentationencodingskillandthegeneticoperatorswithhybridstrategies.LargequantitiesofnumericalexperimentsshowtheeffectivenessandefficiencyofsuchkindofGA-basedapproach.

ArticleOutline

1.Introduction

2.AdaptationofGAs

3.Multistageprocessplanningproblems

3.1.Representation

3.2.Geneticoperators

3.3.Evaluation

3.4.Example

4.Fixedchargetransportationproblem

4.1.Representation

4.2.Geneticoperators

4.3.Evaluationandselection

4.4.Examples

5.Minimumspanningtreeproblem

5.1.Representation

5.2.Feasibilitycondition

5.3.Geneticoperators

6.Centralizednetworkdesignproblem

7.Localareanetworkdesignproblem

8.Shortestpathproblem

8.1.Representation

8.2.Pathgrowthprocedure

8.3.Geneticoperators

8.4.Compromiseapproach

8.5.Evaluationandselection

8.6.OverallprocedureofGA

8.7.Examples

9.Conclusions

Acknowledgements

References

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238

Areconfigurablecomputingframeworkformulti-scalecellularimageprocessing  OriginalResearchArticle

MicroprocessorsandMicrosystems,Volume31,Issue8,3December2007,Pages546-563

ReidPorter,JanFrigo,AlConti,NealHarvey,GarrettKenyon,MayaGokhale

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Abstract

Cellularcomputingarchitecturesrepresentanimportantclassofcomputationthatarecharacterizedbysimpleprocessingelements,localinterconnectandmassiveparallelism.ThesearchitecturesareagoodmatchformanyimageandvideoprocessingapplicationsandcanbesubstantiallyacceleratedwithReconfigurableComputers.Wepresentaflexiblesoftware/hardwareframeworkfordesign,implementationandautomaticsynthesisofcellularimageprocessingalgorithms.Thesystemprovidesanextremelyflexiblesetofparallel,pipelinedandtime-multiplexedcomponentswhichcanbetailoredthroughreconfigurablehardwareforparticularapplications.Themostnovelaspectsofourframeworkincludeahighlypipelinedarchitectureformulti-scalecellularimageprocessingaswellassupportforseveraldifferentpatternrecognitionapplications.Inthispaper,wewilldescribethesystemindetailandpresentourperformanceassessments.Thesystemachievedspeed-upofatleast100×forcomputationallyexpensivesub-problemsand10×forend-to-endapplicationscomparedtosoftwareimplementations.

ArticleOutline

1.Introduction

2.Background

2.1.Typicalneighborhoodfunctions

2.2.Implementingneighborhoodfunctions

2.2.1.Dataparallel

2.2.2.Instructionparallel

3.Systemoverview

3.1.Networkspecificationfile

3.2.Networkparameterfile

4.Hardwareoverview

4.1.HardwareAPI

4.2.SoftwareAPI

5.Hardwarecomponents

5.1.Neighborhoodmemoryaccess

5.2.Neighborhoodfunctions

5.2.1.Convolution

5.2.2.Morphology

5.2.3.Threshold

5.3.Datasequencing

5.3.1.Downsampling

5.3.2.Streamsplitting

5.3.3.Streammixing

5.3.4.Upsampling

5.4.Parametermodule

6.Applicationcasestudies

6.1.ApplicationI

6.2.ApplicationII

7.Conclusion

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239

APHID:

Anarchitectureforprivate,high-performanceintegrateddatamining  OriginalResearchArticle

FutureGenerationComputerSystems,Volume26,Issue7,July2010,Pages891-904

JimmySecretan,MichaelGeorgiopoulos,AnnaKoufakou,KelCardona

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Abstract

Whiletheemergingfieldofprivacypreservingdatamining（PPDM）willenablemanynewdataminingapplications,itsuffersfromseveralpracticaldifficulties.PPDMalgorithmsarechallengingtodevelopandcomputationallyintensivetoexecute.DevelopersneedconvenientabstractionstosimplifytheengineeringofPPDMapplications.Theindividualpartiesinvolvedinthedataminingprocessneedawaytobringhigh-performance,parallelcomputerstobearonthecomputationallyintensivepartsofthePPDMtasks.ThispaperdiscussesAPHID（ArchitectureforPrivateandHigh-performanceIntegratedDatamining）,apracticalarchitectureandsoftwareframeworkfordevelopingandexecutinglargescalePPDMapplications.Atonetier,thesystemsupportssimplifieduseofclusterandgridresources,andatanothertier,thesystemabstractscommunicationforeasyPPDMalgorithmdevelopment.ThispaperoffersadetailedanalysisofthechallengesindevelopingPPDMalgorithmswithexistingframeworks,andmotivatesthedesignofanewinfrastructurebasedonthesechallenges.

ArticleOutline

1.Introduction

2.Distributedandprivacypreservingdatamining

3.Background/relatedwork

3.1.Highperformanceparalleldataminingonclustersandgrids

3.2.Distributeddatamining

3.2.1.Agent-basedapproachesforDDM

3.3.DDMmiddleware

3.3.1.WebservicesforDDM

3.4.ArchitecturestosupportPPDM

4.PPDMpreliminaries

5.APHID

5.1.Developmentmodel

5.1.1.Programstructure

5.1.2.Sharedvariables

5.2.Mainexecutionlayer

5.3.High-performancecomputingservices

5.4.PPDMservices

5.5.Datamanagementbetweenlayersandparties

6.Examplealgorithmimplementation

6.1.Notation

6.2.Example:

privacypreservingNaïveBayesclassifier

6.2.1.Securesum

6.2.2.Calculatingfrequencyofattributes

7.Performanceresults

8.Discussion

9.Conclusionsandfuturework

Acknowledgements

References

Vitae

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240

Perspectivesforprocesssystemsengineering—Personalviewsfromacademiaandindustry  OriginalResearchArticle

Computers&ChemicalEngineering,Volume33,Issue3,20March2009,Pages536-550

Karsten-UlrichKlatt,WolfgangMarquardt

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Abstract

Processsystemsengineering（PSE）hasbeenanactiveresearchfieldforalmost50years.Itsmajorachievementsincludemethodologiesandtoolstosupportprocessmodeling,simulationandoptimization（MSO）.Mature,commerciallyavailabletechnologieshavebeenpenetratingallfieldsofchemicalengineeringinacademiaaswellasinindustrialpractice.MSOtechnologieshavebecomeacommodity,theyarenotadistinguishingfeatureofthePSEfieldanymore.Consequently,PSEhastoreassessandtorepositionitsfutureresearchagenda.Emphasisshouldbep