Unify classical signal processing and modern AI under one paradigm Leading machine learning architectures—CNNs, Transformers, Graph Neural Networks—are fundamentally signal-processing chains, yet most CS and AI curricula omit formal signal theory. Signal Processing for Modern Informatics: A Bridge between Theory and Modern Artificial Intelligence closes that gap. It re-examines discrete-time signal processing from sampling through filtering, then systematically places each concept within current AI practice and deep model design. The book provides an integrated treatment tying Hilbert transforms, STFT, wavelets, Graph Fourier Transform, CNN filters, and attention heads together under a unified signal-processing framework. It compares model-based versus data-driven methods in depth, with guidance on constructing hybrid models. Coverage extends to empirical mode decomposition, singular spectrum analysis, and feature extraction for audio and image data. Readers will also find: End-of-chapter code snippets executed on publicly available datasets, enabling immediate replication and experimentation with each techniqueDetailed treatment of graph signal processing and filtering methods applied to robotics and AI system designCoverage of sampling, quantization, and linear time-invariant systems grounded in their direct relevance to deep learning pipelinesPractical examples demonstrating the Fourier Transform and FFT applied to real-world signal analysis and frequency-domain applicationsAnalysis of how convolutional neural network architectures and transformer attention mechanisms relate directly to classical signal structuresSignal Processing for Modern Informatics serves professors, graduate students, and senior undergraduates in DSP and machine learning courses, as well as researchers and industry professionals seeking a unified conceptual framework. It is also suited for professional continuing-education programs in deep learning for audio, images, and graph signal processing.
