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.
Author(s): S.P. Upadhyay
Publisher: AclerPress
Year: 2023
Language: English
Pages: 266
Cover
Title Page
Copyright
ABOUT THE AUTHOR
TABLE OF CONTENTS
List of Figures
List of Tables
List of Abbreviations
Preface
Chapter 1 Fundamentals of Computation
1.1. Introduction
1.2. Computing
1.3. Hardware
1.4. Processors
1.5. Software
1.6. Processing
References
Chapter 2 Principles and Applications of DNA Computing
2.1. Introduction
2.2. Construction of DNA Logic Gates as the Basic Computing Components
2.3. Scaling Up DNA Logic Gates for Building Computing Systems
2.4. DNA Molecular Computing for Intelligent Diagnostics
2.5. DNA Arithmetical Computation For Intelligent Diagnostics
2.6. Summary
References
Chapter 3 Stochastic Computing Principles
3.1. Introduction
3.2. Stochastic Thinking
3.3. Fundamentals of Stochastic Computing
3.4. Stochastic Computing Techniques
3.5. Optimization Methods For Stochastic Systems
3.6. Technology and Design
3.7. Stochastic Computing Applications and Potential Research Areas
3.8. Summary
References
Chapter 4 Principles and Applications of Social Computing
4.1. Introduction
4.2. The Nature of Social Computing
4.3. Challenges
4.4. Approach
4.5. Summary
References
Chapter 5 Computational Principles in Memory Storage
5.1. Introduction
5.2. Creating Persistence From Memory-Less Components
5.3. Robustness to Noise
5.4. Memory Capacity
5.5. Model Mechanisms: Tests and Questions
5.6. Biological Versus Computer Memory
References
Chapter 6 Application of Computational Models in Clinical Applications
6.1. Introduction
6.2. Modeling Approaches for Clinical Applications in Personalized Medicine
6.3. Models in Clinical Research for Discovery, Diagnosis, and Therapy
6.4. Challenges and Recommendations
References
Chapter 7 Application of Computational Models in Climate Analysis and Remote Sensing
7.1. Introduction
7.2. Theoretical Background
7.3. Analyzing Remote Sensing and Climate Data Over Data Mining Techniques
7.4. Future Research Directions
References
Chapter 8 A Socio-Technical Perspective of Computational Sustainability
8.1. Introduction
8.2. Background of Computational Sustainability
8.3. Sustainability in General
8.4. Computational Sustainability
References
Index
Back Cover
