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Information Theory Applications in Signal Processing
Sergio Cruces1, Rubén Martín-Clemente1, Wojciech Samek2
1Departamento de Teoría de la Señal y Comunicaciones, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Seville, Spain.
Entropy (Basel, Switzerland)
|December 3, 2020
Summary
Information Theory, pioneered by Claude Shannon, provides a mathematical framework for communication systems. This foundational work enables efficient data transmission and storage, crucial for modern technology.
Area of Science:
- Information Theory
- Communication Systems Engineering
- Mathematical Foundations of Computer Science
Background:
- The genesis of Information Theory is intrinsically linked to Claude Shannon's seminal 1948 paper, "A Mathematical Theory of Communication."
- Shannon's work established a rigorous mathematical basis for understanding information, quantification, and transmission limits.
Discussion:
- This foundational theory underpins the principles of data compression, error correction, and channel capacity.
- It provides a universal language for analyzing and optimizing the performance of communication and data storage systems.
Key Insights:
- Quantification of information using bits as the fundamental unit.
- Definition of channel capacity as the maximum rate of reliable communication.
- Introduction of entropy as a measure of information uncertainty.
Outlook:
- Continued relevance in digital communication, data science, and emerging fields like quantum information.
- Ongoing research explores advanced coding schemes and the theoretical limits of information processing.
Keywords:
communicationsdata analysisimage and audio processinginformation theory applicationsmachine learningsignal processingMore Related Videos
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