This book
provides a comprehensive foundation in the mathematical principles of information theory and their professional application in communications, electrical engineering, and computer science. The text establishes a rigorous theoretical framework, beginning with the fundamental concepts of
Information Theory
-such as entropy and mutual information-to explain the physical and mathematical necessity for efficient coding. It then provides an in-depth exploration of the two primary pillars of coding theory:
Source Coding
, including advanced quantization (analog-to-digital conversion) and high-efficiency compression (approaching the theoretical limits of interpretation), and
Channel Coding
for error detection and correction. The third part of the book integrates
Data Security
, covering modern cryptographic algorithms and secure network protocols essential for protecting information integrity. To a multidisciplinary extent, the content also explores the intersections of coding with
Artificial Intelligence
(Transformers and LSTMs) and the
biological "Coding Brain."
Designed for a professional and academic audience, this book is an essential resource for those developing projects in
data science, industrial digital networks, IoT, and Big Data management.
Through its lab-based approach and practical templates, it bridges the gap between high-level theory and real-world implementation.
