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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.

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Summary
This summary is machine-generated.

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.

Keywords:
communicationsdata analysisimage and audio processinginformation theory applicationsmachine learningsignal processing

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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.