Free Download Fourier and wavelet analysis in artificial intelligence: Foundations techniques and applications in feature extraction and computer vision (Math and Artificial Intelligence) by Anshuman Mishra
English | August 24, 2025 | ISBN: N/A | ASIN: B0FNNGL1XB | 426 pages | EPUB | 0.34 Mb
Artificial Intelligence (AI) is no longer confined to futuristic imagination. It is embedded in our daily lives-from virtual assistants and self-driving cars to medical diagnosis and financial predictions. At the heart of these intelligent systems lies a powerful truth: AI is fueled by data, and data is often in the form of signals-images, sounds, biomedical readings, or sensor streams. To make sense of these signals, to extract meaningful features, and to enable machines to interpret them intelligently, we need strong mathematical tools.
Among the most profound tools that humanity has developed for analyzing signals are Fourier analysis and wavelet analysis. These mathematical frameworks allow us to understand not only what information a signal contains but also how that information is distributed across time and frequency.
This book, "Fourier and Wavelet Analysis in Artificial Intelligence: Foundations, Techniques, and Applications in Feature Extraction and Computer Vision", is dedicated to exploring these two pillars of signal analysis in the context of Artificial Intelligence. It aims to bring together the mathematical foundations, computational techniques, and real-world applications of Fourier and wavelet transforms, showing how they empower modern AI systems.
Why Fourier and Wavelet Analysis Matter in AI
The journey of Artificial Intelligence is often narrated through the lens of algorithms and models-neural networks, decision trees, reinforcement learning agents, and generative transformers. However, underneath these models lies an often-overlooked stage: feature extraction and representation.Fourier analysis enables us to represent signals in the frequency domain, making patterns visible that remain hidden in the time or spatial domain. This is crucial in applications like speech recognition, where frequency components carry essential information.Wavelet analysis, in contrast, allows us to look at both frequency and time simultaneously, providing a multi-resolution perspective. It is invaluable for analyzing non-stationary signals such as biomedical waveforms, seismic activity, or natural images.In AI systems, features derived from Fourier and wavelet transforms serve as the raw material for machine learning models. Without effective feature extraction, even the most sophisticated deep learning algorithm may fail to achieve accuracy or robustness.
This book shows readers how to use Fourier and wavelet analysis as gateways to intelligent perception, bridging the gap between raw data and AI-driven decision-making.
Purpose and Scope of the Book
The purpose of this book is threefold:To provide foundational knowledge
Students will be introduced to the basics of Fourier and wavelet transforms, ensuring they understand the mathematics, algorithms, and intuitions behind them.To connect theory with practice
Readers will see how Fourier and wavelet methods are applied in AI tasks such as image recognition, speech processing, biomedical diagnosis, and computer vision.To prepare readers for research and innovation
By exploring emerging areas such as Fourier Neural Operators, wavelet scattering networks, and hybrid deep learning models, this book highlights how classical mathematics is shaping the future of AI.
Who Should Read This Book?
This book is designed for:Undergraduate and postgraduate students in Computer Science, Electronics, and Applied Mathematics, who want a practical yet rigorous understanding of signal analysis in AI.Researchers exploring interdisciplinary applications of Fourier and wavelets in computer vision, natural language processing, or biomedical AI.
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