Advances in Augmented Reality and Virtual Reality

Editorial Advisory Board
Preface
Contents
Editors and Contributors
Understanding Virtual Environments
Recreating Reality: Classification of Computer-Assisted Environments
1 Introduction
2 Virtual Reality
3 Augmented Reality
4 Mixed Reality
5 Extended Reality
6 Conclusion
References
Impact of Different Field-of-Views on Visuospatial Memory and Cognitive Workload in a Complex Virtual Environment
1 Introduction
2 Background
3 Materials and Methods
3.1 Participants
3.2 The Virtual Environment
3.3 Experiment Design
3.4 Procedure
4 Results
4.1 Performance Measures
4.2 Workload Measures
4.3 Correlation Analysis
5 Discussion and Conclusions
References
Free-Roam Virtual Reality: A New Avenue for Gaming
1 Introduction
2 Defining Virtual Reality
3 Free-Roam Gaming
4 The Case of Zero Latency
5 Coronavirus: Challenges and Opportunities
References
Application in Engineering
Computer Simulation of Physical Processes in an Electric Circuit with Nonlinear Inductance
1 Introduction
2 Characterization of Methods for the Analysis of Nonlinear Circuits with Steel
3 Determination of the Magnetization Curve of the Inductor with Saturation
4 A Computerized Complex for Studying the Characteristics of an Electrical Circuit Using the LabVIEW Application Package
5 Conclusions
References
A Comparative Motion Study of Mated Gears on AutoCAD and SOLIDWORKS
1 Introduction
2 Problem Description
3 Methodology
3.1 AutoCAD
3.2 Solidworks
4 Conclusion
References
An Overview of Different Approaches for Ternary Reversible Logic Circuits Synthesis Using Ternary Reversible Gates with Special Reference to Virtual Reality
1 Introduction
2 Basic Concepts of Ternary Reversible Logic Circuits
2.1 Physical Implementation of Three-Level Quantum System
3 Ternary Gate Representation with Elementary Gates
4 Different Synthesis Approaches for Ternary Reversible Logic Circuits
4.1 Synthesis Based on Group Theory Approach
4.2 Synthesis Based on Max-Min Approach
4.3 Synthesis Based on Ternary Galois Field Sum of Products
4.4 Synthesis Based on Ternary Decision Diagram
4.5 Soft Computing-Based Synthesis
5 Conclusion
References
Applications in Agriculture and Medical
Tomato’s Disease Identification Using Machine Learning Techniques with the Potential of AR and VR Technologies for Inclusiveness
1 Introduction
2 Historical Background
3 Literature Review
4 Research Methodology
5 Methodology and Modeling
6 MATLAB Training Image Labeler
6.1 ROI
6.2 Training
7 Hardware Implementation
7.1 Arduino UNO
7.2 Rail Track
7.3 Robot
7.4 User-Friendly App
7.5 Complete Hardware Setup
7.6 Algorithms Description
8 Results Analysis
8.1 Image Detection
8.2 Camera Resolution
8.3 Distance
8.4 Effect of Different Color Light
9 Cost Analysis
10 Conclusion
References
Augmented Reality and Virtual Reality Creating Ripple in Medical and Pharmaceutical World
1 Augmented Reality (AR)
2 Virtual Reality
3 Types of Virtual Reality Systems
4 Types of Immersion
5 Applications of Augmented Reality and Virtual Reality in Medical and Pharmaceutical Field
6 Case Studies-Based Applications of AR and VR in Health care
7 Conclusion
References
Autonomous Drones for Medical Assistance Using Reinforcement Learning
1 Introduction
1.1 Current Drone Traffic Management Systems and Their Limitations
1.2 Solutions for Drone Traffic Management System
1.3 Reinforcement Learning Solutions for Drone Traffic Management
1.4 Evaluation of Autonomous Drones
2 Conceptual Framework
2.1 Lane Positioning System
2.2 Drone Positioning System
2.3 Reinforcement Learning Component
2.4 Working Methodology
3 Model-Free and Model-Based Reinforcement Learning
3.1 Model-Free Reinforcement Learning
3.2 Model-Based Reinforcement Learning
4 Implementation
4.1 Real-World Grid Setup
4.2 Drone Used
4.3 Custom Open AI Gym Environment
4.4 Q-Learning Implementation
4.5 SARSA Implementation
4.6 Deep Q-Learning Implementation
4.7 Autonomous Drone Navigation Implementation
5 Evaluation and Results
5.1 Q-Learning Evaluation
5.2 Tuning the Alpha Value
5.3 Tuning the Discount Factor
5.4 Calculating the Regret
6 SARSA Evaluation
7 Deep Q-Learning Evaluation
8 Shortness of the Path to Reach the Goal
9 Smoothness of the Path
10 Time Taken to Complete the Full Flight
11 Conclusion and Future Work
References
Integration with Artificial Intelligence
Autonomous Navigation of Drones Using Reinforcement Learning
1 Introduction
2 Related Works
2.1 Current Drone Traffic Management Systems and Their Limitations
2.2 Solutions for Drone Traffic Management System
2.3 Reinforcement Learning Solutions for Drone Traffic Management
2.4 Evaluation of Autonomous Drones
3 Conceptual Framework for Drone Path Planning Using Bike Lanes
3.1 Bike Lane Detection Component
3.2 Validation Component
3.3 Reinforcement Learning Component
4 Training and Evaluation Phase
4.1 User Training
4.2 Gym Training
5 Conclusion and Future Work
References
Human Face Detection and Recognition Protection System Based on Machine Learning Algorithms with Proposed AR Technology
1 Introduction
2 Hardware and Software Description
2.1 Raspberry Pi 3 Model B+ 
2.2 Camera Module (I)
2.3 SD Card
2.4 Raspbian
2.5 SQLite Studio
2.6 Camera Module (II)
3 Methods of Recognition
3.1 Haar Classifier
3.2 Local Binary Pattern Histogram
3.3 Template Matching
4 Block Diagram of Design Prototype
5 Methods of Simulations
5.1 Simulation with Template Matching
5.2 Simulation with Local Binary Pattern Histogram
6 Final Hardware Implementation
7 Results and Graphical Analysis
8 Cost Analysis of Design Prototype
9 Future Scope on Security System Based on AR
10 Conclusion
References
A Reinforcement Learning Approach for Shortest Path Navigation in Automated Guided Vehicles for Medical Assistance
1 Introduction
1.1 Research Question and Hypothesis
1.2 Limitations in Autonomous Navigation
1.3 Solutions for Autonomous Navigation
1.4 Autonomous Navigation Solutions in Medical Industry
1.5 Reinforcement Learning Solutions for Autonomous Navigation
1.6 Evaluation of Autonomous Navigation
2 Conceptual Framework for Autonomous Navigation
2.1 Environment Learning Component
3 Obstacle Detection Component
3.1 Model-Free Algorithms
3.2 Model-Based Algorithms
4 Implementation
4.1 ANKI Vector
4.2 ANKI Vector Specification
4.3 Grid Environment
4.4 Open AI Framework
4.5 Obstacle Detection Component
4.6 Training Phase
4.7 Deployment Phase
5 Evaluation
5.1 Comparison of Q Learning and Double Q Learning
5.2 Q Learning Hyperparameters
5.3 Double Q Learning Hyperparameters
6 Conclusion and Future Works
References