AI for Emerging Verticals: Human-Robot Computing, Sensing and Networking

385 Pages·2021·17.813 MB·English

by  Muhammad Zeeshan Shakir, Naeem Ramzan

IETCOMPUTINGSERIES34
AI for Emerging Verticals
Othervolumesinthisseries:
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Volume3 TroubledITProjects:PreventionandturnaroundJ.M.Smith
Volume4 UMLforSystemsEngineering:Watchingthewheels,2ndEditionJ.Holt
Volume5 IntelligentDistributedVideoSurveillanceSystemsS.A.Velastinand
P.Remagnino(Editors)
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Volume18 ModelingandSimulationofComplexCommunicationM.A.Niazi(Editor)
Volume20 SysMLforSystemsEngineering:Amodel-basedapproach,3rdEdition
J.HoltandS.Perry
Volume23 DataasInfrastructureforSmartCitiesL.SuzukiandA.Finkelstein
Volume24 UltrascaleComputingSystemsJ.Carretero,E.JeannotandA.Zomaya
Volume25 BigData-EnabledInternetofThingsM.Khan,S.Khan,A.Zomaya(Editors)
Volume26 HandbookofMathematicalModelsforLanguagesandComputation
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Volume33 EdgeComputing:Models,technologiesandapplicationsJ.Taheriand
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Volume35 BigDataRecommenderSystemsVols.1and2O.Khalid,S.U.Khan,
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AI for Emerging Verticals
Human-robot computing, sensing
and networking
Edited by
Muhammad Zeeshan Shakir and Naeem Ramzan
TheInstitutionofEngineeringandTechnology
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Contents
Abouttheeditors xiii
Preface xv
PARTI Human–robot 1
1 Deeplearningtechniquesformodellinghumanmanipulationand
itstranslationforautonomousroboticgraspingwithsoft
end-effectors 3
Visar Arapi, Yujie Zhang, Giuseppe Averta, Cosimo Della Santina, and
MatteoBianchi
1.1 Introduction 3
1.2 Investigationofthehumanexample 5
1.2.1 Methods 6
1.2.2 Experiments 9
1.3 Autonomousgraspingwithanthropomorphicsofthands 12
1.3.1 Highlevel:deepclassifier 12
1.3.2 Transferringgraspingprimitivestorobots 17
1.3.3 Experimentalsetup 18
1.3.4 Results 21
1.4 Discussionandconclusions 22
Acknowledgement 25
References 26
2 Artificialintelligenceforaffectivecomputing:anemotion
recognitioncasestudy 29
Pablo Arnau-González, Stamos Katsigiannis, Miguel Arevalillo-Herráez,
andNaeemRamzan
2.1 Introduction 29
2.2 Modelsofhumanaffect 30
2.2.1 Discretemodelsofaffect 30
2.2.2 Continuous(dimensional)modelsofaffect 31
2.3 Previousworkonemotionrecognition 34
2.4 Datasetsforemotionrecognition 35
2.5 Proposedmethodology 36
2.5.1 Connectivityfeatures 36
2.5.2 Energyfeatures 37
2.5.3 Dimensionalityreduction 37
vi AIforemergingverticals
2.6 Experimentalresults 38
2.7 Conclusionsanddiscussion 40
Acknowledgement 41
References 41
3 Machinelearning-basedaffectdetectionwithinthecontextof
human–horseinteraction 45
TurkeAlthobaiti,StamosKatsigiannis,DauneWest,HassanRabah,
andNaeemRamzan
3.1 Introduction 45
3.2 Background 47
3.3 Experimentalprotocol 48
3.3.1 Fieldexperimentsetting 48
3.3.2 Experimentaldataacquisition 49
3.3.3 Self-reportingofemotionalstate 50
3.3.4 Participants 51
3.4 Analysisofcaptureddata 51
3.4.1 Pre-processingofphysiologicalsignals 51
3.4.2 Extractionoffeaturesfromphysiologicalsignals 52
3.4.3 Emotionlabels 53
3.5 Experimentalresults 55
3.6 Discussion 57
3.7 Conclusion 58
References 58
4 Robotintelligenceforreal-worldapplications 63
EleftheriosTriantafyllidis,ChuanyuYang,ChristopherMcGreavy,
WenbinHu,andZhibinLi
4.1 Introduction 63
4.2 Novelroboticapplicationsinlocomotion 64
4.2.1 Deep reinforcement learning for dynamic locomotion of
bipedalrobots 64
4.2.2 Learningfromhumans 70
4.3 Novelroboticapplicationsinhuman-guidedmanipulation 74
4.3.1 Background,trendsandchallenges 74
4.3.2 Discussionandfrontiersinhuman-guidedmanipulation 81
4.4 Novelroboticapplicationsinfullyautonomousmanipulation 82
4.4.1 Background 83
4.4.2 Relatedwork 84
4.4.3 Reaching,graspingandre-grasping 86
4.5 Conclusion 87
References 87
Contents vii
5 VisualobjecttrackingbyquadrotorAR.Droneusingartificial
neuralnetworksandfuzzylogiccontroller 101
KamelBoudjit,CherifLarbesandNaeemRamzan
5.1 Introduction 101
5.2 Systemoverview 102
5.2.1 Controlsystem 103
5.2.2 Quadrotordynamicmodel 104
5.3 Fuzzy-logic-basedidentificationandtargettracking 105
5.3.1 Simulationstudies 106
5.3.2 Experimentalsetup 113
5.4 Artificial neural networks (ANN) for target identification and
trackingusingaquadrotor 117
5.4.1 Designoftheneuralnetwork 119
5.4.2 Simulationresults 123
5.4.3 Experimentalvalidationandresults 127
5.5 Conclusion 129
References 130
PARTII Network 133
6 Predictivemobilitymanagementincellularnetworks 135
MetinÖztürk,PauloValenteKlaine,SajjadHussain,and
MuhammadAliImran
6.1 Introduction 135
6.1.1 Thepathtowards5G 136
6.1.2 5Genablersandchallenges 137
6.1.3 Issuesfromnewtechnologies 138
6.1.4 Chapterobjectives 139
6.1.5 Organisationofthechapter 139
6.2 Mobilitymanagementincellularnetworks 139
6.3 Predictivemobilitymanagement 142
6.3.1 Stateoftheartinpredictive
mobilitymanagement 145
6.4 AdvancedMarkov-chain-assistedpredictive
mobilitymanagement 147
6.4.1 Markov-chain-basedmobilityprediction 147
6.4.2 ProblemwiththeconventionalMarkovchains 148
6.4.3 Introductionto3Dtransitionmatrix 149
6.4.4 Performanceevaluation 149
6.5 Summary 152
References 152
viii AIforemergingverticals
7 Artificial intelligence and data analytics in 5G and beyond-5G
wirelessnetworks 157
MaziarNekovee,DehaoWu,YueWangandMehrdadShariat
7.1 Introduction 157
7.2 CasestudiesofAIin5Gwirelessnetworks 159
7.2.1 Casestudy1:AIforcellselection 159
7.2.2 Casestudy2:AIfor5Gfronthaul 163
7.2.3 Casestudy3:AIforcoexistenceofmultipleradioaccess
technologies 164
7.3 Dataanalyticsin5G 167
7.4 Industryandstandardactivities 168
7.4.1 Openstandardsrequired 168
7.4.2 Achievementsandactivatesofstandardization 169
7.5 Challengesandopenquestions 169
7.5.1 Bigdataorsmalldata 170
7.5.2 Centralizedordistributedlearning 170
7.6 Conclusions 171
References 171
8 DeepQ-network-basedcoverageholedetectionforfuturewireless
networks 173
ShahriarAbdullahAl-Ahmed,MuhammadZeeshanShakir,
andQasimZeeshanAhmed
8.1 Introduction 173
8.1.1 Motivationandbackground 174
8.2 Machinelearning 175
8.2.1 Reinforcementlearning 176
8.2.2 Deepreinforcementlearning 177
8.3 Systemmodel 177
8.4 DQN-basedcoverageholedetection 179
8.4.1 DQNcomponentstodetectcoveragehole 180
8.4.2 DQNprinciplestodetectcoveragehole 181
8.5 Simulationresultsanddiscussion 183
8.6 Conclusions 186
References 187
9 Artificialintelligenceforlocalizationof
ultrawidebandwidth(UWB)sensornodes 189
FuhuChe,AbbasAhmed,QasimZeeshanAhmed,and
MuhammadZeeshanShakir
9.1 Introduction 189
9.2 Indoorpositioningsystem 191
9.2.1 UWBindoorpositioningsystem 192
Contents ix
9.3 UWBrangingaccuracyevaluation 194
9.3.1 NaïveBayes(NB)classifier 194
9.3.2 Receiveroperatingcharacteristic 195
9.4 Implementationandevaluation 196
9.4.1 Experimentsetupandenvironment 198
9.4.2 NBandROCresults 198
9.5 Conclusion 201
References 201
10 ACascadedMachineLearningApproachforindoorclassification
andlocalizationusingadaptivefeatureselection 205
MohamedI.AlHajri,NazarT.AliandRaedM.Shubair
10.1 Introduction 205
10.2 Indoorradiopropagationchannel 206
10.2.1 CharacteristicsofRFindoorchannel 206
10.2.2 DesignconsiderationsfortheRFindoorchannel 207
10.3 Datacollectionphase:practicalmeasurementscampaign 207
10.4 Signaturesofindoorenvironment 208
10.4.1 PrimaryRFfeatures 208
10.4.2 HybridRFfeatures 209
10.5 Spatialcorrelationcoefficient 210
10.6 Machinelearningalgorithms 213
10.6.1 Decisiontrees 213
10.6.2 Supportvectormachine 213
10.6.3 k-Nearestneighbor 214
10.7 CascadedMachineLearningApproach 215
10.7.1 Machinelearningforindoorenvironmentclassification 215
10.7.2 Machinelearningforlocalizationpositionestimation 216
10.8 Conclusion 219
References 220
PARTIII Sensing 225
11 EEG-basedbiometrics:effectsoftemplateageing 227
Pablo Arnau-González, Stamos Katsigiannis, Miguel Arevalillo-Herraez
andNaeemRamzan
11.1 Introduction 227
11.2 Background 228
11.2.1 Biometrics 228
11.2.2 Electroencephalography 230
11.2.3 EEGinbiometrics 232
11.3 Dataacquisitionandexperimentalprotocol 232
11.3.1 Stimuli 232
11.3.2 Experimentalprotocol 233