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Fourier and Wavelet Analysis in Artificial Intelligence

Foundations, Techniques, and Applications in Feature Extraction and Computer Vision

Artificial Intelligence systems are only as powerful as the representations they learn from data. Whether analyzing speech signals, recognizing faces, interpreting medical images, processing sensor streams, or understanding complex visual environments, intelligent systems depend on effective methods for extracting meaningful information from raw data.

Among the most influential mathematical tools for signal representation and feature extraction are Fourier Analysis and Wavelet Analysis.

The book \"Fourier and Wavelet Analysis in Artificial Intelligence: Foundations, Techniques, and Applications in Feature Extraction and Computer Vision\" provides a comprehensive exploration of these two transformative mathematical frameworks and demonstrates how they contribute to modern Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Speech Processing, Biomedical Analytics, and Sensor Intelligence.

Fourier analysis revolutionized science by enabling signals to be represented in the frequency domain, revealing patterns hidden from direct observation. Wavelet analysis extended this capability by introducing multi-resolution representations capable of analyzing signals simultaneously in both time and frequency domains.

Today, these mathematical foundations power numerous AI applications including:

• Computer Vision and Image Recognition

• Speech and Audio Processing

• Biomedical Signal Analysis

• Edge AI and Embedded Systems

• Sensor Networks and Internet of Things (IoT)

• Feature Engineering for Machine Learning

• Deep Learning Architectures

• Pattern Recognition and Classification

• Signal Compression and Denoising

• Intelligent Decision Support Systems

This book begins with the mathematical foundations of Fourier and wavelet transforms before progressing toward modern applications in machine learning and deep learning.

Readers will learn:

• Fourier Series and Fourier Transform Theory

• Discrete Fourier Transform (DFT)

• Fast Fourier Transform (FFT)

• Continuous and Discrete Wavelet Transforms

• Multiresolution Analysis

• Wavelet Packets and Filter Banks

• Signal Processing Foundations

• Time-Frequency Analysis

• Feature Extraction Techniques

• Fourier Descriptors and Texture Analysis

• Wavelet-Based Feature Engineering

• Fourier Neural Operators

• Wavelet Scattering Networks

• CNN-Wavelet Hybrid Models

• Image Compression and Enhancement

• Speech Recognition Systems

• Biomedical Signal Classification

• IoT Sensor Analytics

• Emerging Quantum Fourier Applications

Unlike traditional signal-processing textbooks that focus exclusively on engineering applications, this book emphasizes Artificial Intelligence perspectives, showing how signal representations become intelligent features that drive machine learning and deep learning models.

The content balances mathematical rigor, practical implementation, real-world applications, and future research directions, making it equally useful for students, researchers, educators, and industry professionals.

Who Should Read This Book?

• BCA, MCA, B.Tech, M.Tech, BSc and MSc Students

• Artificial Intelligence and Machine Learning Learners

• Data Scientists and AI Engineers

• Computer Vision Researchers

• Signal Processing Professionals

• Biomedical Engineering Students

• Electronics and Communication Engineers

• Researchers in AI, Pattern Recognition, and Deep Learning

What Makes This Book Unique?

✔ Bridges mathematics, signal processing, and Artificial Intelligence

✔ Covers both classical and modern transform techniques

✔ Includes machine learning and deep learning applications

✔ Provides practical implementations using Python, MATLAB, TensorFlow, PyTorch, and OpenCV

✔ Features real-world case studies across multiple domains

✔ Explores emerging research areas such as Fourier Neural Operators and Wavelet Scattering Networks

✔ Suitable for academic courses, self-study, research, and professional development

By combining theoretical foundations with practical AI applications, this book helps readers understand not only how Fourier and wavelet transforms work, but also why they remain essential in the age of deep learning and intelligent systems.

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Kick off your book project in 3 hours! Live workshop on Zoom. You’ll leave with a real book project, progress on your first chapter, and a clear plan to keep going. Saturday, June 6, 2026. Learn more…

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