{"product_id":"signal-processing-for-modern-informatics-a-bridge-between-theory-and-modern-artificial-intelligence-9781394434206","title":"Signal Processing for Modern Informatics: A Bridge Between Theory and Modern Artificial Intelligence","description":"\u003cp\u003e • Author(s): Vincenzo Dentamaro\u003cbr\u003e • Publisher: Wiley-IEEE Press\u003cbr\u003e • Publisher Imprint: Wiley-IEEE Press\u003cbr\u003e • BISAC: Signals \u0026amp; Signal Processing\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eUnify classical signal processing and modern AI under one paradigm\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eLeading machine learning architectures--CNNs, Transformers, Graph Neural Networks--are fundamentally signal-processing chains, yet most CS and AI curricula omit formal signal theory. \u003ci\u003eSignal Processing for Modern Informatics: A Bridge between Theory and Modern Artificial Intelligence\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003eReaders will also find: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eEnd-of-chapter code snippets executed on publicly available datasets, enabling immediate replication and experimentation with each technique\u003c\/li\u003e \u003cli\u003eDetailed treatment of graph signal processing and filtering methods applied to robotics and AI system design\u003c\/li\u003e \u003cli\u003eCoverage of sampling, quantization, and linear time-invariant systems grounded in their direct relevance to deep learning pipelines\u003c\/li\u003e \u003cli\u003ePractical examples demonstrating the Fourier Transform and FFT applied to real-world signal analysis and frequency-domain applications\u003c\/li\u003e \u003cli\u003eAnalysis of how convolutional neural network architectures and transformer attention mechanisms relate directly to classical signal structures\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eSignal Processing for Modern Informatics\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Hardcover","offer_id":48288062374039,"sku":"9781394434206","price":14535.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9781394434206.webp?v=1789724643","url":"https:\/\/atlanticbooks.com\/products\/signal-processing-for-modern-informatics-a-bridge-between-theory-and-modern-artificial-intelligence-9781394434206","provider":"Atlantic Books","version":"1.0","type":"link"}