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QCM4490/QCS4490 Linux Android软件调试指南 SP80-50439-5

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本文以高通QCS6490和QCM6490为例，详细解析了高通处理器中QCS与QCM系列的区别。两者基于相同的6nm工艺核心架构，包括Kryo™ 670八核CPU、Adreno 643 GPU和10 TOPS以上的AI能力，但在功能配置和市场定位上存在显著差异。QCM6490集成5G基带，主打移动终端如车载和手持设备，而QCS6490无基带，支持多系统（Android/Linux/Windows IoT），专注于高端物联网如工业机器人和医疗设备。文章强调两者虽技术同源，但因通信模块和系统支持差异，需根据具体场景选型，不可完全互换。

Soft computing and nature-inspired computing both play a significant role in developing a better understanding to machine learning. When studied together, they can offer new perspectives on the learning process of machines. The Handbook of Research on Soft Computing and Nature-Inspired Algorithms is an essential source for the latest scholarly research on applications of nature-inspired computing and soft computational systems. Featuring comprehensive coverage on a range of topics and perspectives such as swarm intelligence, speech recognition, and electromagnetic problem solving, this publication is ideally designed for students, researchers, scholars, professionals, and practitioners seeking current research on the advanced workings of intelligence in computing systems. Chapter 1 ApplicationofNatured-InspiredAlgorithmsfortheSolutionofComplexElectromagnetic Problems................................................................................................................................................. 1 Massimo Donelli, University of Trento, Italy Inthelastdecadenature-inspiredOptimizerssuchasgeneticalgorithms(GAs),particleswarm(PSO), antcolony(ACO),honeybees(HB),bacteriafeeding(BFO),firefly(FF),batalgorithm(BTO),invasive weed(IWO)andothersalgorithms,hasbeensuccessfullyadoptedasapowerfuloptimizationtools inseveralareasofappliedengineering,andinparticularforthesolutionofcomplexelectromagnetic problems.Thischapterisaimedatpresentinganoverviewofnatureinspiredoptimizationalgorithms (NIOs)asappliedtothesolutionofcomplexelectromagneticproblemsstartingfromthewell-known geneticalgorithms(GAs)uptorecentcollaborativealgorithmsbasedonsmartswarmsandinspired byswarmofinsects,birdsorflockoffishes.Thefocusofthischapterisontheuseofdifferentkind ofnaturedinspiredoptimizationalgorithmsforthesolutionofcomplexproblems,inparticulartypical microwavedesignproblems,inparticularthedesignandmicrostripantennastructures,thecalibration ofmicrowavesystemsandotherinterestingpracticalapplications.Startingfromadetailedclassification andanalysisofthemostusednaturedinspiredoptimizers(NIOs)thischapterdescribesthenotonly thestructuresofeachNIObutalsothestochasticoperatorsandthephilosophyresponsibleforthe correctevolutionoftheoptimizationprocess.Theoreticaldiscussionsconcernedconvergenceissues, parameterssensitivityanalysisandcomputationalburdenestimationarereportedaswell.Successively abriefreviewonhowdifferentresearchgroupshaveappliedorcustomizeddifferentNIOsapproaches forthesolutionofcomplexpracticalelectromagneticproblemrangingfromindustrialuptobiomedical applications.ItisworthnoticedthatthedevelopmentofCADtoolsbasedonNIOscouldprovidethe engineersanddesignerswithpowerfultoolsthatcanbethesolutiontoreducethetimetomarketof specific devices, (such as modern mobile phones, tablets and other portable devices) and keep the commercialpredominance:sincetheydonotrequireexpertengineersandtheycanstronglyreducethe computationaltimetypicalofthestandardtrialerrorsmethodologies.Suchusefulautomaticdesigntools basedonNIOshavebeentheobjectofresearchsincesomedecadesandtheimportanceofthissubject iswidelyrecognized.Inordertoapplyanaturedinspiredalgorithm,theproblemisusuallyrecastas aglobaloptimizationproblem.Formulatedinsuchaway,theproblemcanbeefficientlyhandledby naturedinspiredoptimizerbydefiningasuitablecostfunction(singleormulti-objective)thatrepresent thedistancebetweentherequirementsandtheobtainedtrialsolution.Thedeviceunderdevelopment canbeanalyzedwithclassicalnumericalmethodologiessuchasFEM,FDTD,andMoM.Asacommon feature,theseenvironmentsusuallyintegrateanoptimizerandacommercialnumericalsimulator.The chapterendswithopenproblemsanddiscussiononfutureapplications. Chapter 2 AComprehensiveLiteratureReviewonNature-InspiredSoftComputingandAlgorithms:Tabular andGraphicalAnalyses........................................................................................................................ 34 Bilal Ervural, Istanbul Technical University, Turkey Beyzanur Cayir Ervural, Istanbul Technical University, Turkey Cengiz Kahraman, Istanbul Technical University, Turkey SoftComputingtechniquesarecapableofidentifyinguncertaintyindata,determiningimprecisionof knowledge,andanalyzingill-definedcomplexproblems.Thenatureofrealworldproblemsisgenerally complexandtheircommoncharacteristicisuncertaintyowingtothemultidimensionalstructure.Analytical modelsareinsufficientinmanagingallcomplexitytosatisfythedecisionmakers’expectations.Under thisviewpoint,softcomputingprovidessignificantflexibilityandsolutionadvantages.Inthischapter, firstly,themajorsoftcomputingmethodsareclassifiedandsummarized.Thenacomprehensivereviewof eightnatureinspired–softcomputingalgorithmswhicharegeneticalgorithm,particleswarmalgorithm, antcolonyalgorithms,artificialbeecolony,fireflyoptimization,batalgorithm,cuckooalgorithm,and greywolfoptimizeralgorithmarepresentedandanalyzedundersomedeterminedsubjectheadings (classificationtopics)inadetailedway.Thesurveyfindingsaresupportedwithcharts,bargraphsand tablestobemoreunderstandable. Chapter 3 SwarmIntelligenceforElectromagneticProblemSolving................................................................... 69 Luciano Mescia, Politecnico di Bari, Italy Pietro Bia, EmTeSys Srl, Italy Diego Caratelli, The Antenna Company, The Netherlands & Tomsk Polytechnic University, Russia Johan Gielis, University of Antwerp, Belgium ThechapterwilldescribethepotentialoftheswarmintelligenceandinparticularquantumPSO-based algorithm,tosolvecomplicatedelectromagneticproblems.Thistaskisaccomplishedthroughaddressing the design and analysis challenges of some key real-world problems. A detailed definition of the conventionalPSOanditsquantum-inspiredversionarepresentedandcomparedintermsofaccuracyand computationalburden.Sometheoreticaldiscussionsconcerningtheconvergenceissuesandasensitivity analysisontheparametersinfluencingthestochasticprocessarereported. Chapter 4 ParameterSettingsinParticleSwarmOptimization........................................................................... 101 Snehal Mohan Kamalapur, K. K. Wagh Institute of Engineering Education and Research, India Varsha Patil, Matoshree College of Engineering and Research Center, India Theissueofparametersettingofanalgorithmisoneofthemostpromisingareasofresearch.Particle SwarmOptimization(PSO)ispopulationbasedmethod.TheperformanceofPSOissensitivetothe parametersettings.Intheliteratureofevolutionarycomputationtherearetwotypesofparametersettings - parametertuningandparametercontrol.Staticparametertuningmayleadtopoorperformanceas optimalvaluesofparametersmaybedifferentatdifferentstagesofrun.Thisleadstoparametercontrol. Thischapterhastwo-foldobjectivestoprovideacomprehensivediscussiononparametersettingsandon parametersettingsofPSO.Theobjectivesaretostudyparametertuningandcontrol,togettheinsight ofPSOandimpactofparameterssettingsforparticlesofPSO. Chapter 5 ASurveyofComputationalIntelligenceAlgorithmsandTheirApplications...................................133 Hadj Ahmed Bouarara, Dr. Tahar Moulay University of Saida, Algeria Thischaptersubscribesintheframeworkofananalyticalstudyaboutthecomputationalintelligence algorithms.Thesealgorithmsarenumerousandcanbeclassifiedintwogreatfamilies:evolutionary algorithms(geneticalgorithms,geneticprogramming,evolutionarystrategy,differentialevolutionary, paddyfieldalgorithm)andswarmoptimizationalgorithms(particleswarmoptimisationPSO,antcolony optimization(ACO),bacteriaforagingoptimisation,wolfcolonyalgorithm,fireworksalgorithm,bat algorithm,cockroachescolonyalgorithm,socialspidersalgorithm,cuckoosearchalgorithm,waspswarm optimisation,mosquitooptimisationalgorithm).Wehavedetailedeachalgorithmfollowingastructured organization(theoriginofthealgorithm,theinspirationsource,thesummary,andthegeneralprocess). Thispaperisthefruitofmanyyearsofresearchintheformofsynthesiswhichgroupsthecontributions proposedbyvariousresearchersinthisfield.Itcanbethestartingpointforthedesigningandmodelling newalgorithmsorimprovingexistingalgorithms. Chapter 6 OptimizationofProcessParametersUsingSoftComputingTechniques:ACaseWithWire ElectricalDischargeMachining..........................................................................................................177 Supriyo Roy, Birla Institute of Technology, India Kaushik Kumar, Birla Institute of Technology, India J. Paulo Davim, University of Aveiro, Portugal MachiningofhardmetalsandalloysusingConventionalmachininginvolvesincreaseddemandof time,energyandcost.Itcausestoolwearresultinginlossofqualityoftheproduct.Non-conventional machining,ontheotherhandproducesproductwithminimumtimeandatdesiredlevelofaccuracy.In thepresentstudy,EN19steelwasmachinedusingCNCWireElectricaldischargemachiningwithpredefinedprocessparameters.MaterialRemovalRateandSurfaceroughnesswereconsideredasresponses forthisstudy.Thepresentoptimizationproblemissingleandaswellasmulti-response.Consideringthe complexitiesofthispresentproblem,experimentaldataweregeneratedandtheresultswereanalyzed byusingTaguchi,GreyRelationalAnalysisandWeightedPrincipalComponentAnalysisundersoft computingapproach.Responsesvarianceswiththevariationofprocessparameterswerethoroughly studiedandanalyzed;also‘bestoptimalvalues’wereidentified.Theresultshowsanimprovementin responsesfrommeantooptimalvaluesofprocessparameters. Chapter 7 AugmentedLagrangeHopfieldNetworkforCombinedEconomicandEmissionDispatchwith FuelConstraint.................................................................................................................................... 221 Vo Ngoc Dieu, Ho Chi Minh City University of Technology, Vietnam Tran The Tung, Ho Chi Minh City University of Technology, Vietnam This chapter proposes an augmented Lagrange Hopfield network (ALHN) for solving combined economicandemissiondispatch(CEED)problemwithfuelconstraint.IntheproposedALHNmethod, theaugmentedLagrangefunctionisdirectlyusedastheenergyfunctionofcontinuousHopfieldneural network(HNN),thusthismethodcanproperlyhandleconstraintsbybothaugmentedLagrangefunction andsigmoidfunctionofcontinuousneuronsintheHNN.Fordealingwiththebi-objectiveeconomic dispatchproblem,theslopeofsigmoidfunctioninHNNisadjustedtofindthePareto-optimalfrontand thenthebestcompromisesolutionfortheproblemwillbedeterminedbyfuzzy-basedmechanism.The proposedmethodhasbeentestedonmanycasesandtheobtainedresultsarecomparedtothosefrom othermethodsavailabletheliterature.Thetestresultshaveshownthattheproposedmethodcanfind goodsolutionscomparedtotheothersforthetestedcases.Therefore,theproposedALHNcouldbea favourableimplementationforsolvingtheCEEDproblemwithfuelconstraint. Chapter 8 SpeakerRecognitionWithNormalandTelephonicAssameseSpeechUsingI-Vectorand Learning-BasedClassifier................................................................................................................... 256 Mridusmita Sharma, Gauhati University, India Rituraj Kaushik, Tezpur University, India Kandarpa Kumar Sarma, Gauhati University, India Speaker recognition is the task of identifying a person by his/her unique identification features or behaviouralcharacteristicsthatareincludedinthespeechutteredbytheperson.Speakerrecognition dealswiththeidentityofthespeaker.Itisabiometricmodalitywhichusesthefeaturesofthespeaker thatisinfluencedbyone’sindividualbehaviouraswellasthecharacteristicsofthevocalcord.Theissue becomesmorecomplexwhenregionallanguagesareconsidered.Here,theauthorsreportthedesignof aspeakerrecognitionsystemusingnormalandtelephonicAssamesespeechfortheircasestudy.Intheir work,theauthorshaveimplementedi-vectorsasfeaturestogenerateanoptimalfeaturesetandhaveused theFeedForwardNeuralNetworkfortherecognitionpurposewhichgivesafairlyhighrecognitionrate. Chapter 9 ANewSVMMethodforRecognizingPolarityofSentimentsinTwitter.......................................... 281 Sanjiban Sekhar Roy, VIT University, India Marenglen Biba, University of New York – Tirana, Albania Rohan Kumar, VIT University, India Rahul Kumar, VIT University, India Pijush Samui, NIT Patna, India Onlinesocialnetworkingplatforms,suchasWeblogs,microblogs,andsocialnetworksareintensively beingutilizeddailytoexpressindividual’sthinking.Thispermitsscientiststocollecthugeamountsof dataandextractsignificantknowledgeregardingthesentimentsofalargenumberofpeopleatascale thatwasessentiallyimpracticalacoupleofyearsback.Therefore,thesedays,sentimentanalysishasthe potentialtolearnsentimentstowardspersons,objectandoccasions.Twitterhasincreasinglybecome a significantsocialnetworkingplatformwherepeoplepostmessagesofupto140charactersknownas ‘Tweets’.Tweetshavebecomethepreferredmediumforthemarketingsectorasuserscaninstantlyindicate customersuccessorindicatepublicrelationsdisasterfarmorequicklythanawebpageortraditional mediadoes.Inthispaper,wehaveanalyzedtwitterdataandhavepredictedpositiveandnegativetweets withhighaccuracyrateusingsupportvectormachine(SVM). Chapter 10 AutomaticGenerationControlofMulti-AreaInterconnectedPowerSystemsUsingHybrid EvolutionaryAlgorithm...................................................................................................................... 292 Omveer Singh, Maharishi Markandeshwar University, India Anewtechniqueofevaluatingoptimalgainsettingsforfullstatefeedbackcontrollersforautomatic generationcontrol(AGC)problembasedonahybridevolutionaryalgorithms(EA)i.e.geneticalgorithm (GA)-simulatedannealing(SA)isproposedinthischapter.ThehybridEAalgorithmcantakedynamic curveperformanceashardconstraintswhicharepreciselyfollowedinthesolutions.Thisisincontrast tothemodernandsinglehybridevolutionarytechniquewheretheseconstraintsaretreatedassoft/hard constraints.Thistechniquehasbeeninvestigatedonanumberofcasestudiesandgivessatisfactorysolutions. Thistechniqueisalsocomparedwithlinearquadraticregulator(LQR)andGAbasedproportionalintegral (PI)controllers.Thisprovestobeagoodalternativeforoptimalcontroller’sdesign.Thistechniquecan beeasilyenhancedtoincludemorespecificationsviz.settlingtime,risetime,stabilityconstraints,etc. Chapter 11 MathematicalOptimizationbyUsingParticleSwarmOptimization,GeneticAlgorithm,and DifferentialEvolutionandItsSimilarities.......................................................................................... 325 Shailendra Aote, Ramdeobaba College of Engineering and Management, India Mukesh M. Raghuwanshi, Yeshwantrao Chavan College of Engineering, India Tosolvetheproblemsofoptimization,variousmethodsareprovidedindifferentdomain.Evolutionary computing(EC)isoneofthemethodstosolvetheseproblems.MostlyusedECtechniquesareavailable likeParticleSwarmOptimization(PSO),GeneticAlgorithm(GA)andDifferentialEvolution(DE). Thesetechniqueshavedifferentworkingstructurebuttheinnerworkingstructureissame.Different namesandformulaearegivenfordifferenttaskbutultimatelyalldothesame.Herewetriedtofindout thesimilaritiesamongthesetechniquesandgivetheworkingstructureineachstep.Allthestepsare providedwithproperexampleandcodewritteninMATLAB,forbetterunderstanding.Herewestarted ourdiscussionwithintroductionaboutoptimizationandsolutiontooptimizationproblemsbyPSO,GA andDE.Finally,wehavegivenbriefcomparisonofthese. Chapter 12 GA_SVM:AClassificationSystemforDiagnosisofDiabetes.......................................................... 359 Dilip Kumar Choubey, Birla Institute of Technology Mesra, India Sanchita Paul, Birla Institute of Technology Mesra, India Themodernsocietyispronetomanylife-threateningdiseaseswhichifdiagnosisearlycanbeeasily controlled.Theimplementationofadiseasediagnosticsystemhasgainedpopularityovertheyears.The mainaimofthisresearchistoprovideabetterdiagnosisofdiabetes.Therearealreadyseveralexisting methods,whichhavebeenimplementedforthediagnosisofdiabetes.Inthismanuscript,firstly,Polynomial Kernel,RBFKernel,SigmoidFunctionKernel,LinearKernelSVMusedfortheclassificationofPIDD. SecondlyGAusedasanAttributeselectionmethodandthenusedPolynomialKernel,RBFKernel, SigmoidFunctionKernel,LinearKernelSVMonthatselectedattributesofPIDDforclassification.So, herecomparedtheresultswithandwithoutGAinPIDD,andLinearKernelprovedbetteramongallof thenotedaboveclassificationmethods.ItdirectlyseemsinthepaperthatGAisremovinginsignificant features,reducingthecostandcomputationtimeandimprovingtheaccuracy,ROCofclassification. Theproposedmethodcanbealsousedforotherkindsofmedicaldiseases. Chapter 13 TheInsectsofNature-InspiredComputationalIntelligence............................................................... 398 Sweta Srivastava, B.I.T. Mesra, India Sudip Kumar Sahana, B.I.T. Mesra, India Thedesirablemeritsoftheintelligentcomputationalalgorithmsandtheinitialsuccessinmanydomains haveencouragedresearcherstoworktowardstheadvancementofthesetechniques.Amajorplunge inalgorithmicdevelopmenttosolvetheincreasinglycomplexproblemsturnedoutasbreakthrough towardsthedevelopmentofcomputationalintelligence(CI)techniques.Natureprovedtobeoneofthe greatestsourcesofinspirationfortheseintelligentalgorithms.Inthischapter,computationalintelligence techniquesinspiredbyinsectsarediscussed.Thesetechniquesmakeuseoftheskillsofintelligent agentbymimickinginsectbehaviorsuitablefortherequiredproblem.Thediversitiesinthebehaviorof theinsectfamiliesandsimilaritiesamongthemthatareusedbyresearchersforgeneratingintelligent techniquesarealsodiscussedinthischapter. Chapter 14 Bio-InspiredComputationalIntelligenceandItsApplicationtoSoftwareTesting............................ 429 Abhishek Pandey, UPES Dehradun, India Soumya Banerjee, BIT Mesra, India Bioinspiredalgorithmsarecomputationalprocedureinspiredbytheevolutionaryprocessofnature andswarmintelligencetosolvecomplexengineeringproblems.Intherecenttimesithasgainedmuch popularityintermsofapplicationstodiverseengineeringdisciplines.Nowadaysbioinspiredalgorithms arealsoappliedtooptimizethesoftwaretestingprocess.Inthischapterauthorswilldiscusssomeof thepopularbioinspiredalgorithmsandalsogivestheframeworkofapplicationofthesealgorithmsfor softwaretestingproblemssuchastestcasegeneration,testcaseselection,testcaseprioritization,test caseminimization.Bioinspiredcomputationalalgorithmsincludesgeneticalgorithm(GA),genetic programming (GP), evolutionary strategies (ES), evolutionary programming (EP) and differential evolution(DE)intheevolutionaryalgorithmscategoryandAntcolonyoptimization(ACO),Particle swarmoptimization(PSO),ArtificialBeeColony(ABC),Fireflyalgorithm(FA),Cuckoosearch(CS), Batalgorithm(BA)etc.intheSwarmIntelligencecategory(SI). Chapter 15 Quantum-InspiredComputationalIntelligenceforEconomicEmissionDispatchProblem.............. 445 Fahad Parvez Mahdi, Universiti Teknologi Petronas, Malaysia Pandian Vasant, Universiti Teknologi Petronas, Malaysia Vish Kallimani, Universiti Teknologi Petronas, Malaysia M. Abdullah-Al-Wadud, King Saud University, Saudi Arabia Junzo Watada, Universiti Teknologi Petronas, Malaysia Economicemissiondispatch(EED)problemsareoneofthemostcrucialproblemsinpowersystems. Growingenergydemand,limitedreservesoffossilfuelandglobalwarmingmakethistopicintothe centerofdiscussionandresearch.Inthischapter,wewilldiscusstheuseandscopeofdifferentquantum inspiredcomputationalintelligence(QCI)methodsforsolvingEEDproblems.Wewillevaluateeach previouslyusedQCImethodsforEEDproblemanddiscusstheirsuperiorityandcredibilityagainst othermethods.WewillalsodiscussthepotentialityofusingotherquantuminspiredCImethodslike quantumbatalgorithm(QBA),quantumcuckoosearch(QCS),andquantumteachingandlearningbased optimization(QTLBO)techniqueforfurtherdevelopmentinthisarea. Chapter 16 IntelligentExpertSystemtoOptimizetheQuartzCrystalMicrobalance(QCM)Characterization Test:IntelligentSystemtoOptimizetheQCMCharacterizationTest............................................... 469 Jose Luis Calvo-Rolle, University of A Coruña, Spain José Luis Casteleiro-Roca, University of A Coruña, Spain María del Carmen Meizoso-López, University of A Coruña, Spain Andrés José Piñón-Pazos, University of A Coruña, Spain Juan Albino Mendez-Perez, Universidad de La Laguna, Spain Thischapterdescribesanapproachtoreducesignificantlythetimeinthefrequencysweeptestofa QuartzCrystalMicrobalance(QCM)characterizationmethodbasedontheresonanceprincipleofpassive components.Onthistest,thespenttimewaslarge,becauseitwasnecessarycarryoutabigfrequency sweepduetothefactthattheresonancefrequencywasunknown.Moreover,thisfrequencysweephas greatstepsandconsequentlylowaccuracy.Then,itwasnecessarytoreducethesweepsanditssteps graduallywiththeaimtoincreasetheaccuracyandtherebybeingabletofindtheexactfrequency.An intelligentexpertsystemwascreatedasasolutiontothedisadvantagedescribedofthemethod.This modelprovidesamuchsmallerfrequencyrangethantheinitiallyemployedwiththeoriginalproposal. Thisfrequencyrangedependsofthecircuitcomponentsofthemethod.Then,thankstothenewapproach oftheQCMcharacterizationisachievedbetteraccuracyandthetesttimeisreducedsignificantly. Chapter 17 OptimizationThroughNature-InspiredSoft-ComputingandAlgorithmonECGProcess................ 489 Goutam Kumar Bose, Haldia Institute of Technology, India Pritam Pain, Haldia Institute of Technology, India Inthepresentresearchworkselectionofsignificantmachiningparametersdependingonnature-inspired algorithmisprepared,duringmachiningalumina-aluminuminterpenetratingphasecompositesthrough electrochemical grinding process. Here during experimentation control parameters like electrolyte concentration(C),voltage(V),depthofcut(D)andelectrolyteflowrate(F)areconsidered.Theresponse dataareinitiallytrainedandtestedapplyingArtificialNeuralNetwork.Theparadoxicalresponseslike highermaterialremovalrate(MRR),lowersurfaceroughness(Ra),lowerovercut(OC)andlowercutting force(Fc)areaccomplishedindividuallybyemployingCuckooSearchAlgorithm.Amultiresponse optimizationforalltheresponseparametersiscompiledprimarilybyusingGeneticalgorithm.Finally, inordertoachieveasinglesetofparametriccombinationforalltheoutputssimultaneouslyfuzzy basedGreyRelationalAnalysistechniqueisadopted.Thesenature-drivensoftcomputingtechniques corroborateswellduringtheparametricoptimizationofECGprocess. Chapter 18 AnOverviewoftheLastAdvancesandApplicationsofArtificialBeeColonyAlgorithm.............. 520 Airam Expósito Márquez, University of La Laguna, Spain Christopher Expósito-Izquierdo, University of La Laguna, Spain SwarmIntelligenceisdefinedascollectivebehaviorofdecentralizedandself-organizedsystemsofa naturalorartificialnature.Inthelastyearsandtoday,SwarmIntelligencehasproventobeabranchof ArtificialIntelligencethatisabletosolvingefficientlycomplexoptimizationproblems.SomeofwellknownexamplesofSwarmIntelligenceinnaturalsystemsreportedintheliteraturearecolonyofsocial insectssuchasbeesandants,birdflocks,fishschools,etc.Inthisrespect,ArtificialBeeColonyAlgorithm isanatureinspiredmetaheuristic,whichimitatesthehoneybeeforagingbehaviourthatproducesan intelligentsocialbehaviour.ABChasbeenusedsuccessfullytosolveawidevarietyofdiscreteand continuousoptimizationproblems.InordertofurtherenhancethestructureofArtificialBeeColony, thereareavarietyofworksthathavemodifiedandhybridizedtoothertechniquesthestandardversion ofABC.Thisworkpresentsareviewpaperwithasurveyofthemodifications,variantsandapplications oftheArtificialBeeColonyAlgorithm. Chapter 19 ASurveyoftheCuckooSearchandItsApplicationsinReal-WorldOptimizationProblems........... 541 Christopher Expósito-Izquierdo, University of La Laguna, Spain Airam Expósito-Márquez, University of La Laguna, Spain ThechapterathandseekstoprovideageneralsurveyoftheCuckooSearchAlgorithmanditsmost highlightedvariants.TheCuckooSearchAlgorithmisarelativelyrecentnature-inspiredpopulationbasedmeta-heuristicalgorithmthatisbaseduponthelifestyle,egglaying,andbreedingstrategyof somespeciesofcuckoos.Inthiscase,theLévyflightisusedtomovethecuckooswithinthesearch spaceoftheoptimizationproblemtosolveandobtainasuitablebalancebetweendiversificationand intensification.Asdiscussedinthischapter,theCuckooSearchAlgorithmhasbeensuccessfullyapplied toawiderangeofheterogeneousoptimizationproblemsfoundinpracticalapplicationsoverthelast fewyears.Someofthereasonsofitsrelevancearethereducednumberofparameterstoconfigureand itseaseofimplementation.

内置AI处理器Dual HVX和4K HMX，AI综合算力超过14 Tops支持双屏异显(FHD 2520*1080 @60fps)应用领域： VR Camera、智能机器人、视频监控、智能驾舱、高端收银机等。集成GPS 定位，满足不同环境下快速、精准定位的需求。视频支持4K@30fps录制/4K@60fps播放。支持PCIe/I2C/I2S/SPI/UART。搭载阿加犀大模型算法，支持多种场景应用。4路摄像头接口，最高支持64MP像素。支持 Wi-Fi 6E (2。支持BT5.2和GNSS。

【代码】【Qualcomm QCM4490】Voice dual mic。

高通推出全新品牌跃龙（Dragonwing），与现有骁龙品牌形成双线布局。骁龙面向消费级市场，主要应用于智能手机等终端设备；跃龙则聚焦工业和企业级市场，涵盖工业机器人、无人机、智能摄像头等领域。跃龙系列产品强调边缘AI能力、高性能计算和卓越连接性，目前已推出QCS/QCM等多个系列处理器，采用4nm/6nm工艺，支持Wi-Fi7/6E和5G连接，AI算力最高达48TOPS。该系列提供长期产品支持（至2030-2033年），适用于工业自动化、智能零售等多种场景.

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