Emerging Extended Reality Technologies for Industry 4.0

Jolanda G. Tromp is a Human-Computer Interaction expert for User-Centered design and evaluation of new technologies (VR/AR/AI/IoT), with 20 years' experience as principal Usability investigator. She has a PhD in Systematic Usability Design and Evaluation for Collaborative Virtual Environments, 2001, University of Nottingham, United Kingdom, a BSc in Psychology (with honors) University of Amsterdam, Holland (1995). She is a research consultant for the Center of Visualization and Simulation at Duy Tan University, Vietnam; for the Mixed Reality Task Group of the State University of New York; and for the global Simulations Working Group. Dac-Nhuong Le is PhD Deputy-Head of Faculty of Information Technology, Haiphong University, Vietnam. His areas of research include: evolutionary computation, specialized with evolutionary multiobjective optimization, approximate algorithms, network communication, security and vulnerability, network performance analysis and simulation, cloud computing, image processing in biomedical. His core work in evolutionary multi-objective optimization, network security, wireless, mobile computing and virtual reality. He has edited several books for the Wiley-Scrivener imprint. Chung Van Le is Vice-Director Center of Visualization and Simulation. He has a MSc in Computer Science from Duy Tan University, 2011, Vietnam and a BSc in Computer Science at Da Nang University, 2004, Vietnam. He is currently pursuing a PhD at Duy Tan University, Vietnam. He researches medical image processing, e-Health, virtual simulation in medicine. He is Duy Tan University Lead Software Developer for 3D virtual body system for teaching anatomy and virtual endoscopic techniques for medical students.

• List of Figures xiList of Tables xvForeword xviiIntroduction xixPreface xxiiiAcknowledgments xxvAcronyms xxviiPart I Extended Reality Education1 Mixed Reality Use in Higher Education: Results from an International Survey 3J. Riman, N. Winters, J. Zelenak, I. Yucel, J. G. Tromp1.1 Introduction 41.2 Organizational Framework 41.3 Online Survey About MR Usage 51.4 Results 61.4.1 Use in Classrooms 81.4.2 Challenges 91.4.3 Examples of Research in Action 101.4.4 Hardware and Software for Use in Classrooms and Research 101.4.5 Challenges Described by Researcher Respondents 121.4.6 Anecdotal Responses about Challenges 121.5 Conclusion 13References 152 Applying 3D VR Technology for Human Body Simulation to Teaching, Learning and Studying 17Le Van Chung, Gia Nhu Nguyen, Tung Sanh Nguyen, Tri Huu Nguyen, Dac-Nhuong Le2.1 Introduction 182.2 Related Works 182.3 3D Human Body Simulation System 192.3.1 The Simulated Human Anatomy Systems 192.3.2 Simulated Activities and Movements 202.3.3 Evaluation of the System 232.4 Discussion of Future Work 252.5 Conclusion 26References 26Part II Internet of Things3 A Safety Tracking and Sensor System for School Buses in Saudi Arabia 31Samah Abbas, Hajar Mohammed, Laila Almalki Maryam Hassan, Maram Meccawy3.1 Introduction 323.2 Related Work 323.3 Data Gathering Phase 333.3.1 Questionnaire 343.3.2 Driver Interviews 353.4 The Proposed Safety Tracking and Sensor School Bus System 363.4.1 System Analysis and Design 373.4.2 User Interface Design 383.5 Testing and Results 413.6 Discussion and Limitation 423.7 Conclusions and Future Work 42References 424 A Lightweight Encryption Algorithm Applied to a Quantized Speech Image for Secure IoT 45Mourad Talbi4.1 Introduction 464.2 Applications of IoT 464.3 Security Challenges in IoT 474.4 Cryptographic Algorithms for IoT 474.5 The Proposed Algorithm 484.6 Experimental Setup 504.7 Results and Discussion 524.8 Conclusion 57References 58Part III Mobile Technology5 The Impact of Social Media Adoption on Entrepreneurial Ecosystem 63Bodor Almotairy, Manal Abdullah, Rabeeh Abbasi5.1 Introduction 645.2 Background 655.2.1 Small and Medium-Sized Enterprises (SMEs) 655.2.2 Social Media 655.2.3 Social Networks and Entrepreneurial Activities 665.3 Analysis Methodology 665.4 Understanding the Entrepreneurial Ecosystem 675.5 Social Media and Entrepreneurial Ecosystem 695.5.1 Social Media Platforms and Entrepreneurship 715.5.2 The Drivers of Social Media Adoption 715.5.3 The Motivations and Benefits for Entrepreneurs to Use Social Media 715.5.4 Entrepreneurship Activities Analysis Techniques in Social Media Networks 715.6 Research Gap and Recommended Solution 735.6.1 Research Gap 735.6.2 Recommended Solution 745.7 Conclusion 74References 756 Human Factors for E-Health Training System: UX Testing for XR Anatomy Training App 81Zhushun Timothy Cai, Oliver Medonza, Kristen Ray, Chung Van Le, Damian Schofield, Jolanda Tromp6.1 Introduction 826.2 Mobile Learning Applications 826.3 Ease of Use and Usability 826.3.1 Effectiveness 836.3.2 Efficiency 836.3.3 Satisfaction 836.4 Methods and Materials 866.5 Results 896.5.1 Task Completion Rate (TCR) 896.5.2 Time-on-Task (TOT) 906.5.3 After-Scenario Questionnaire (ASQ) 916.5.4 Post-Study System Usability Questionnaire (PSSUQ) 936.6 Conclusion 93References 94Part IV Towards Digital Twins and Robotics7 Augmented Reality at Heritage Sites: Technological Advances and Embodied Spatially Minded Interactions 101Lesley Johnston, Romy Galloway, Jordan John Trench, Matthieu Poyade, Jolanda Tromp, Hoang Thi My7.1 Introduction 1027.2 Augmented Reality Devices 1037.3 Detection and Tracking 1057.4 Environmental Variation 1067.5 Experiential and Embodied Interactions 1097.6 User Experience and Presence in AR 1147.7 Conclusion 115References 1168 TELECI Architecture for Machine Learning Algorithms Integration in an Existing LMS 121V. Zagorskis, A. Gorbunovs, A. Kapenieks8.1 Introduction 1228.2 TELECI Architecture 1238.2.1 TELECI Interface to a Real LMS 1238.2.2 First RS Steps in the TELECI System 1248.2.3 Real Student Data for VS Model 1258.2.4 TELECI Interface to VS Subsystem 1268.2.5 TELECI Interface to AI Component 1288.3 Implementing ML Technique 1288.3.1 Organizational Activities 1288.3.2 Data Processing 1298.3.3 Computing and Networking Resources 1308.3.4 Introduction to Algorithm 1308.3.5 Calibration Experiment 1328.4 Learners’ Activity Issues 1338.5 Conclusion 136References 137Part V Big Data Analytics9 Enterprise Innovation Management in Industry 4.0: Modeling Aspects 141V. Babenko9.1 Introduction 1429.2 Conceptual Model of Enterprise Innovation Process Management 1449.3 Formation of Restrictions for Enterprise Innovation Management Processes 1479.4 Formation of Quality Criteria for Assessing Implementation of Enterprise Innovation Management Processes 1489.5 Statement of Optimization Task of Implementation of Enterprise Innovation Management Processes 1489.6 Structural and Functional Model for Solving the Task of Dynamic 1509.7 Formulation of the Task of Minimax Program Management of Innovation Processes at Enterprises 1529.8 General Scheme for Solving the Task of Minimax Program Management of Innovation Processes at the Enterprises 1549.9 Model of Multicriteria Optimization of Program Management of Innovation Processes 1569.10 Conclusion 161References 16210 Using Simulation for Development of Automobile Gas Diesel Engine Systems and their Operational Control 165Mikhail G. Shatrov, Vladimir V. Sinyavski, Andrey Yu. Dunin, Ivan G. Shishlov, Sergei D. Skorodelov, Andrey L. Yakovenko10.1 Introduction 16610.2 Computer Modeling 16710.3 Gas Diesel Engine Systems Developed 16810.3.1 Electronic Engine Control System 16810.3.2 Modular Gas Feed System 16910.3.3 Common Rail Fuel System for Supply of the Ignition Portion of Diesel Fuel 16910.4 Results and Discussion 17210.4.1 Results of Diesel Fuel Supply System Simulation 17210.4.2 Results of Engine Bed Tests 18110.5 Conclusion 183References 184Part VI Towards Cognitive Computing11 Classification of Concept Drift in Evolving Data Stream 189Mashail Althabiti and Manal Abdullah11.1 Introduction 19011.2 Data Mining 19011.3 Data Stream Mining 19111.3.1 Data Stream Challenges 19111.3.2 Features of Data Stream Methods 19311.4 Data Stream Sources 19311.5 Data Stream Mining Components 19311.5.1 Input 19411.5.2 Estimators 19411.6 Data Stream Classification and Concept Drift 19411.6.1 Data Stream Classification 19411.6.2 Concept Drift 19411.6.3 Data Stream Classification Algorithms with Concept Drift 19611.6.4 Single Classifier 19611.6.5 Ensemble Classifiers 19711.6.6 Output 20011.7 Datasets 20011.8 Evaluation Measures 20011.9 Data Stream Mining Tools 20111.10 Data Stream Mining Applications 20211.11 Conclusion 202References 20212 Dynamical Mass Transfer Systems in Buslaev Contour Networks with Conflicts 207Marina Yashina, Alexander Tatashev, Ivan Kuteynikov12.1 Introduction 20812.2 Construction of Buslaev Contour Networks 21012.3 Concept of Spectrum 21112.4 One-Dimensional Contour Network Binary Chain of Contours 21212.5 Two-Dimensional Contour Network-Chainmail 21412.6 Random Process with Restrictions on the Contour with the Possibility of Particle Movement in Both Directions 21812.7 Conclusion 218References 21913 Parallel Simulation and Visualization of Traffic Flows Using Cellular Automata Theory and QuasigasDynamic Approach 223Antonina Chechina, Natalia Churbanova, Pavel Sokolov, Marina Trapeznikova, Mikhail German, Alexey Ermakov, Obidzhon Bozorov13.1 Introduction 22413.2 The Original CA Model 22413.3 The Slow-to-Start Version of the CA Model 22513.4 Numerical Realization 22513.5 Test Predictions for the CA Model 22913.6 The QGD Approach to Traffic Flow Modeling 23013.7 Parallel Implementation of the QGD Traffic Model 23213.8 Test Predictions for the QGD Traffic Model 23213.9 Conclusion 235References 236