Randomness and Elements of Decision Theory Applied to Signals

This book provides an overview of random variables, random processes, and decision theory for solving real-world problems, with MATLAB algorithms to demonstrate implementation. It is relevant to students and professionals.

Format: Paperback / softback
Length: 242 pages
Publication date: 11 December 2022
Publisher: Springer Nature Switzerland AG

This comprehensive book provides a thorough exploration of the most significant and contemporary topics in random variables, random processes, and decision theory, offering invaluable insights for solving real-world problems. After providing a foundational introduction to statistics and signals, the book delves into a wide range of essential applications in signal processing, including denoising, texture classification, histogram equalization, deep learning, and feature extraction. By utilizing MATLAB algorithms, the book showcases the practical implementation of these theoretical concepts, making it an invaluable resource for students and professionals seeking a concise yet practical introduction to dealing with random signals and processes.

The book begins by introducing the fundamental concepts of probability theory and random variables, providing a solid foundation for the subsequent chapters. It then delves into the study of random processes, including stochastic processes, Markov chains, and Brownian motion, which are fundamental in understanding various natural and social phenomena.

In the subsequent chapters, the book explores the applications of random variables and random processes in signal processing. It discusses various techniques for signal denoising, such as Wiener filtering and adaptive filtering, which are used to remove noise from signals. Texture classification is another important application, where random variables are used to describe the spatial and spectral properties of images and videos. Histogram equalization is employed to enhance the contrast and visibility of images, particularly in medical imaging.

Deep learning, a rapidly evolving field, is also discussed in the book. Random variables are used to model complex patterns in data, and neural networks are used to learn and predict these patterns. Feature extraction is a crucial step in deep learning, where random variables are used to identify important features from raw data.

Throughout the book, MATLAB algorithms are used to demonstrate the implementation of the theory to real systems. This makes the contents of the book relevant to students and professionals who need a quick introduction but practical introduction how to deal with random signals and processes.

In conclusion, this book is a valuable resource for anyone interested in random variables, random processes, and decision theory. It provides a comprehensive and up-to-date introduction to these topics, covering a wide range of applications in signal processing. With its practical implementation using MATLAB algorithms, the book is particularly useful for students and professionals seeking a quick and practical introduction to dealing with random signals and processes.

Weight: 403g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030903169
Edition number: 1st ed. 2021