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A new framework for computing understanding: a consistent set of principles that spans technologies, domains, algorithms, architectures, and designs. This novel textbook introduces the fundamental principles necessary for a one-semester undergraduate course in computing theory. It provides the most accessible and motivating course material for undergraduate computer theory classes available. Aimed at students who may struggle to grasp the course's relevance to their future jobs, the text assists them in becoming more acquainted with the procedures necessary for advanced study of computer science. Students will be motivated by the book's numerous examples, exercises, and comprehensive proofs that simplify complicated theory. Cover 1 Title Page 5 Copyright 6 ABOUT THE AUTHOR 7 TABLE OF CONTENTS 9 List of Figures 13 List of Tables 17 List of Abbreviations 19 Preface 23 Chapter 1 Fundamentals of Computation 25 1.1. Introduction 26 1.2. Computing 27 1.3. Hardware 28 1.4. Processors 34 1.5. Software 37 1.6. Processing 49 References 53 Chapter 2 Principles and Applications of DNA Computing 63 2.1. Introduction 64 2.2. Construction of DNA Logic Gates as the Basic Computing Components 66 2.3. Scaling Up DNA Logic Gates for Building Computing Systems 72 2.4. DNA Molecular Computing for Intelligent Diagnostics 76 2.5. DNA Arithmetical Computation For Intelligent Diagnostics 83 2.6. Summary 85 References 86 Chapter 3 Stochastic Computing Principles 93 3.1. Introduction 94 3.2. Stochastic Thinking 96 3.3. Fundamentals of Stochastic Computing 99 3.4. Stochastic Computing Techniques 104 3.5. Optimization Methods For Stochastic Systems 108 3.6. Technology and Design 110 3.7. Stochastic Computing Applications and Potential Research Areas 112 3.8. Summary 116 References 117 Chapter 4 Principles and Applications of Social Computing 125 4.1. Introduction 126 4.2. The Nature of Social Computing 128 4.3. Challenges 131 4.4. Approach 133 4.5. Summary 135 References 137 Chapter 5 Computational Principles in Memory Storage 141 5.1. Introduction 142 5.2. Creating Persistence From Memory-Less Components 146 5.3. Robustness to Noise 154 5.4. Memory Capacity 157 5.5. Model Mechanisms: Tests and Questions 164 5.6. Biological Versus Computer Memory 166 References 169 Chapter 6 Application of Computational Models in Clinical Applications 179 6.1. Introduction 180 6.2. Modeling Approaches for Clinical Applications in Personalized Medicine 183 6.3. Models in Clinical Research for Discovery, Diagnosis, and Therapy 193 6.4. Challenges and Recommendations 201 References 208 Chapter 7 Application of Computational Models in Climate Analysis and Remote Sensing 217 7.1. Introduction 218 7.2. Theoretical Background 223 7.3. Analyzing Remote Sensing and Climate Data Over Data Mining Techniques 226 7.4. Future Research Directions 227 References 229 Chapter 8 A Socio-Technical Perspective of Computational Sustainability 233 8.1. Introduction 234 8.2. Background of Computational Sustainability 235 8.3. Sustainability in General 236 8.4. Computational Sustainability 242 References 254 Index 261 Back Cover 266