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Abstract: The XR hardware ecosystem primarily consists of various devices like  headsets  and  smartglasses , which rely heavily on a range of  sensors  for tracking and spatial awareness, and advanced  display technologies  to create immersive experiences.   XR Devices XR devices range from fully immersive systems to portable enhancements of the real world, each serving different application needs.   Virtual Reality (VR) Headsets:  These devices, such as the  Meta Quest  and  HTC Vive , fully enclose the user's field of vision to create a completely digital, 360-degree immersive environment. They require sensors to track user movement and position within the virtual space. Augmented Reality (AR) Smartglasses/Headsets:  AR devices, like the  Google Glass  and  Magic Leap 2 , overlay digital information onto the user's view of the real world. They typically have a more compact, transparent fo...

Abstract: "The Science of Immersion: Human Perception and XR Experience Design"  explores how to create effective extended reality (XR) experiences by understanding the core principles of human  perception ,  presence , and  engagement . The book (or related academic work) synthesizes various theories into unified frameworks for designing impactful and believable virtual environments.   Key topics and concepts include: Immersion vs. Immersive Experience:  The book distinguishes between  immersion  as a technological capability (e.g., high-fidelity visuals and audio provided by an immersive system) and  immersive experience  as the user's psychological and cognitive response. Core Dimensions of Immersive Experience:  The experience is built on four main components: Physical Presence:  The feeling of actually being physically situated in a virtual environment, a "sense of being there" (telepresence). Socia...

CHAPTER 1 Understanding Reality — From Physical to Virtual** 1.1 Introduction to the Nature of Reality Human beings perceive the world not as it truly is, but as our senses interpret it. What we call “reality” is constructed by our brain using signals from our eyes, ears, skin, and other sensory systems. These perceptions allow us to interact with the world—but they also have limitations. We see only certain wavelengths of light, hear only specific frequencies, and can process only a fraction of the sensory data around us. This chapter explores how Extended Reality (XR) builds upon the boundaries of human perception by altering, supplementing, or simulating sensory input. Before we understand XR technologies, we must understand what “reality” means in the human context. 1.2 Human Perception: How We Sense Reality 1.2.1 Vision: The Dominant Sense More than 70% of human sensory input is visual. Our eyes collect light, convert it into electrical signals, and send it to...

📘 INTRODUCTION Extended Reality—or XR—is more than a technological trend. It is a new paradigm for experiencing information, interacting with environments, and extending the limits of human perception. Comprising Virtual Reality (VR) , Augmented Reality (AR) , and Mixed Reality (MR) , XR bridges the gap between the digital and the physical, creating a continuum of immersive possibilities. What makes XR remarkable is its versatility . It can transport a person into a fully virtual world, overlay digital objects onto real surroundings, or blend both realms so seamlessly that they coexist in real time. As XR evolves, it is redefining how we educate students, train professionals, treat patients, design products, simulate scenarios, collaborate globally, and experience entertainment. Why This Book? Despite growing interest, the XR ecosystem remains complex and often confusing. Technologies change rapidly—hardware improves, software platforms evolve, new interaction models e...

📘 PREFACE Beyond Boundaries: A Complete Guide to Extended Reality (XR) is written at a time when the boundaries between the physical and digital worlds are dissolving faster than ever before. Technologies that once belonged to science fiction—immersive VR, intelligent AR overlays, spatial computing, mixed-reality collaboration—have now entered classrooms, operating theatres, manufacturing plants, design studios, and even our living rooms. The world is witnessing a profound shift in how humans learn, work, communicate, create, and experience reality. This book was conceptualized with a clear aim: to offer a holistic, structured, and accessible foundation to Extended Reality —a domain that sits at the intersection of computer science, human psychology, design, engineering, and creativity. While many books focus on either the technical development, the design aspects, or the business applications, this book takes an integrated approach. It offers an end-to-end perspective:...

Abstract:  Below is the  Annexure 10: PyTorch Problems for University Exams — well-structured, university-level, and divided into Short Answer , Long Answer , Coding , and Case Study problems. This annexure is ready to be inserted into the book. **Annexure 10 PyTorch Problems for University Exams (Short, Long, Coding & Case Study Questions)** This annexure contains exam-oriented questions designed for undergraduate, postgraduate, and professional certification evaluations. Questions range from basic conceptual understanding to advanced applications and coding tasks. A. Short Answer Questions (2–5 Marks Each) 1. What is a tensor in PyTorch? How is it different from NumPy arrays? 2. Define Autograd. Why is it important in deep learning? 3. What is the purpose of requires_grad=True in PyTorch tensors? 4. Explain the difference between CPU tensors and CUDA tensors. 5. What is a computational graph? 6. What does a PyTorch state_dict contain? 7. Def...

Abstract: Below is the  Annexure 9: PyTorch Glossary of Key Terms (Beginner to Advanced) — concise, complete, and ready to insert into the book. **Annexure 9 PyTorch Glossary of Key Terms (Beginner to Advanced)** This annexure compiles the most essential and frequently used PyTorch terms. It covers foundational concepts, intermediate constructs, and advanced components used in deep learning research and deployment. A. Beginner-Level Terms 1. Tensor A multi-dimensional array used for all computations in PyTorch. Analogous to NumPy arrays but with GPU support. 2. Tensor Rank The number of dimensions (e.g., 0D scalar, 1D vector, 2D matrix). 3. Autograd PyTorch’s automatic differentiation engine that computes gradients for tensors with requires_grad=True . 4. Computational Graph A directed graph representing operations performed on tensors. PyTorch builds it dynamically. 5. Gradient The derivative of a function with respect to its variables; essential for o...

Abstract: Below is the complete Annexure 8: End-to-End PyTorch Projects (Complete Code Pipelines) . **ANNEXURE 8 End-to-End PyTorch Projects (Complete Code Pipelines)** This annexure provides full, end-to-end project templates that include: Data loading Model building Training and evaluation Saving/loading Inference Deployment options Each project is kept concise yet fully functional—easy to extend for academic or production use. Included Projects: Image Classification (CNN) – CIFAR-10 Text Sentiment Analysis (LSTM/Embedding) Object Detection (Faster R-CNN) Time Series Forecasting (LSTM) Reinforcement Learning with DQN (CartPole) **PROJECT 1 Image Classification with CNN (CIFAR-10)** 1. Imports and Device Setup import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader from torchvision import datasets, transforms, models device = torch.device("cuda" if torch.c...

Abstract: Below is the complete Annexure 7: PyTorch Templates and Boilerplates for All Common Tasks . **ANNEXURE 7 PyTorch Templates and Boilerplates for All Common Tasks** This annexure provides practical, ready-to-use templates for every major task performed in PyTorch—including data loading, model building, training loops, evaluation, saving/loading models, visualization, and deployment. These templates are designed to be directly copy-paste ready for academic projects, production workflow, and fast prototyping. 1. Standard PyTorch Project Structure A clean and reusable project folder format: project/ │── data/ │── models/ │── utils/ │── train.py │── inference.py │── requirements.txt 2. Template: Device Configuration import torch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print("Using device:", device) 3. Template: Dataset & DataLoader 3.1 Custom Dataset Template from torch.utils.data import Da...