Andrew Kiruluta (23 results)
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Hardcover. Condition: new. Hardcover. From Signal Processing to AGI: A Mathematical Foundation develops the central claim that artificial intelligence is best understood not as a break from classical signal processing, but as its high-dimensional, adaptive, and learned continuation. The book begins with the mathematics of signal…s, representation spaces, uncertainty, Fourier and wavelet analysis, optimization, statistical learning, kernels, and nonlinear operators, showing that the essential problems of AI-perception, estimation, compression, prediction, and decision-already live inside the deeper structure of signal-processing theory. From that foundation, it builds a unified language in which observations become structured signals, learned models become operators on representation spaces, and intelligence itself becomes the transformation of uncertain measurements into useful internal state, inference, and action. The result is a mathematically rigorous bridge from classical analysis to modern machine learning, grounded in Hilbert spaces, stochastic processes, spectral methods, and variational principles.As the book progresses, it extends this framework into the core architectures and frontier problems of contemporary AI: convolutional networks, recurrent and state-space models, transformers, self-supervised learning, multimodal fusion, generative modeling, diffusion, causal representation learning, world models, agentic planning, safety, and the search for a unified theory of intelligent systems. Rather than treating these as disconnected technologies, the manuscript argues that they are all instances of a common mathematical pattern: structured observation, representation, latent dynamics, operator adaptation, and decision under uncertainty. In that sense, the book is both a graduate-level theoretical synthesis and a research program. It offers a coherent view of how signal processing, probability, geometry, optimization, and dynamical systems can be brought together to explain modern AI and to frame the path toward more general, robust, and scientifically grounded intelligence. This book presents AI as the mathematically rigorous continuation of signal processing, showing how representation, learning, generation, memory, planning, and control can be unified within a single framework of signals, and adaptive systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Hardcover. Condition: new. Hardcover. This book develops a rigorous, end-to-end theory of intelligent magnetic resonance imaging by unifying the physics of spin systems, the mathematics of inverse problems, and the modern machinery of artificial intelligence. Beginning from the quantum and semiclassical foundations of nuclear ma…gnetic resonance, it builds systematically through Bloch dynamics, signal formation, Fourier encoding, image reconstruction, parameter estimation, uncertainty quantification, and the geometry of learned representations. Across the chapters, MRI is treated not simply as an imaging modality but as a layered computational-physical system in which measurements, models, and inference are inseparably linked. The book shows how classical tools such as Hilbert-space analysis, regularization theory, stochastic processes, and optimization evolve naturally into contemporary methods involving deep neural networks, graph models, generative priors, diffusion reconstruction, Bayesian inference, and operator learning. At the same time, the book argues that the future of MRI lies in systems that are not merely automated, but mathematically grounded, adaptive, and scientifically interpretable. It examines how AI can guide acquisition, reconstruction, multimodal fusion, motion correction, artifact suppression, quantitative imaging, and even agentic workflow orchestration across the entire MRI pipeline. Throughout, the emphasis remains on preserving physical consistency, clinical trustworthiness, and theoretical clarity while extending MRI into a new era of intelligent imaging. The result is a comprehensive research-level treatment of how magnetic resonance imaging is being transformed from a sequence of handcrafted procedures into a unified framework of physics-constrained, data-driven, and increasingly autonomous inference. A rigorous research-level book on the physics, mathematics, and artificial intelligence of MRI, showing how spin dynamics, inverse problems, and modern AI come together to create intelligent imaging systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Hardcover. Condition: new. Hardcover. Optical Intelligence develops a rigorous and wide-ranging theory of how optics and artificial intelligence can be understood within a single mathematical and physical framework. The book begins from first principles, treating light as a structured medium of information transformation governe…d by Maxwell's equations, wave propagation, Fourier analysis, statistical optics, and inverse problems. From there, it shows how classical optical processing, computational imaging, and functional analysis naturally connect to core ideas in modern AI, including representation learning, operator composition, kernel methods, optimization, and inference. Its central thesis is that optics is not merely a sensor front-end for digital intelligence, but a physically grounded computational substrate whose geometry, propagation laws, and material structure can actively shape learning and decision-making.As the book progresses, it moves from foundations into advanced architectures and emerging research directions, including diffractive neural networks, integrated photonic systems, nonlinear optical learning, optical reservoirs, holographic memory, and hybrid optical-electronic intelligence. Throughout, the text emphasizes both theoretical depth and systems-level insight, showing how physical propagation, measurement, and learning can be co-designed to create new forms of intelligent sensing and computation. The result is a unified account of optical intelligence as a serious scientific discipline at the intersection of electromagnetism, signal processing, machine learning, and information theory. Rather than presenting optics and AI as separate domains that occasionally interact, the book argues that their deepest future lies in their integration into trainable, physically embodied systems for imaging, inference, communication, and adaptive computation. Optical Intelligence is a rigorous exploration of the emerging field where optics and artificial intelligence meet. Blending the physics of light with the mathematics of information, inference, and learning. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Hardcover. Condition: new. Hardcover. Optical Intelligence develops a rigorous and wide-ranging theory of how optics and artificial intelligence can be understood within a single mathematical and physical framework. The book begins from first principles, treating light as a structured medium of information transformation governe…d by Maxwell's equations, wave propagation, Fourier analysis, statistical optics, and inverse problems. From there, it shows how classical optical processing, computational imaging, and functional analysis naturally connect to core ideas in modern AI, including representation learning, operator composition, kernel methods, optimization, and inference. Its central thesis is that optics is not merely a sensor front-end for digital intelligence, but a physically grounded computational substrate whose geometry, propagation laws, and material structure can actively shape learning and decision-making.As the book progresses, it moves from foundations into advanced architectures and emerging research directions, including diffractive neural networks, integrated photonic systems, nonlinear optical learning, optical reservoirs, holographic memory, and hybrid optical-electronic intelligence. Throughout, the text emphasizes both theoretical depth and systems-level insight, showing how physical propagation, measurement, and learning can be co-designed to create new forms of intelligent sensing and computation. The result is a unified account of optical intelligence as a serious scientific discipline at the intersection of electromagnetism, signal processing, machine learning, and information theory. Rather than presenting optics and AI as separate domains that occasionally interact, the book argues that their deepest future lies in their integration into trainable, physically embodied systems for imaging, inference, communication, and adaptive computation. Optical Intelligence is a rigorous exploration of the emerging field where optics and artificial intelligence meet. Blending the physics of light with the mathematics of information, inference, and learning. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Hardcover. Condition: new. Hardcover. This book develops a rigorous, end-to-end theory of intelligent magnetic resonance imaging by unifying the physics of spin systems, the mathematics of inverse problems, and the modern machinery of artificial intelligence. Beginning from the quantum and semiclassical foundations of nuclear ma…gnetic resonance, it builds systematically through Bloch dynamics, signal formation, Fourier encoding, image reconstruction, parameter estimation, uncertainty quantification, and the geometry of learned representations. Across the chapters, MRI is treated not simply as an imaging modality but as a layered computational-physical system in which measurements, models, and inference are inseparably linked. The book shows how classical tools such as Hilbert-space analysis, regularization theory, stochastic processes, and optimization evolve naturally into contemporary methods involving deep neural networks, graph models, generative priors, diffusion reconstruction, Bayesian inference, and operator learning. At the same time, the book argues that the future of MRI lies in systems that are not merely automated, but mathematically grounded, adaptive, and scientifically interpretable. It examines how AI can guide acquisition, reconstruction, multimodal fusion, motion correction, artifact suppression, quantitative imaging, and even agentic workflow orchestration across the entire MRI pipeline. Throughout, the emphasis remains on preserving physical consistency, clinical trustworthiness, and theoretical clarity while extending MRI into a new era of intelligent imaging. The result is a comprehensive research-level treatment of how magnetic resonance imaging is being transformed from a sequence of handcrafted procedures into a unified framework of physics-constrained, data-driven, and increasingly autonomous inference. A rigorous research-level book on the physics, mathematics, and artificial intelligence of MRI, showing how spin dynamics, inverse problems, and modern AI come together to create intelligent imaging systems. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Hardcover. Condition: new. Hardcover. From Signal Processing to AGI: A Mathematical Foundation develops the central claim that artificial intelligence is best understood not as a break from classical signal processing, but as its high-dimensional, adaptive, and learned continuation. The book begins with the mathematics of signal…s, representation spaces, uncertainty, Fourier and wavelet analysis, optimization, statistical learning, kernels, and nonlinear operators, showing that the essential problems of AI-perception, estimation, compression, prediction, and decision-already live inside the deeper structure of signal-processing theory. From that foundation, it builds a unified language in which observations become structured signals, learned models become operators on representation spaces, and intelligence itself becomes the transformation of uncertain measurements into useful internal state, inference, and action. The result is a mathematically rigorous bridge from classical analysis to modern machine learning, grounded in Hilbert spaces, stochastic processes, spectral methods, and variational principles.As the book progresses, it extends this framework into the core architectures and frontier problems of contemporary AI: convolutional networks, recurrent and state-space models, transformers, self-supervised learning, multimodal fusion, generative modeling, diffusion, causal representation learning, world models, agentic planning, safety, and the search for a unified theory of intelligent systems. Rather than treating these as disconnected technologies, the manuscript argues that they are all instances of a common mathematical pattern: structured observation, representation, latent dynamics, operator adaptation, and decision under uncertainty. In that sense, the book is both a graduate-level theoretical synthesis and a research program. It offers a coherent view of how signal processing, probability, geometry, optimization, and dynamical systems can be brought together to explain modern AI and to frame the path toward more general, robust, and scientifically grounded intelligence. This book presents AI as the mathematically rigorous continuation of signal processing, showing how representation, learning, generation, memory, planning, and control can be unified within a single framework of signals, and adaptive systems. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Hardcover. Condition: new. Hardcover. From Signal Processing to AGI: A Mathematical Foundation develops the central claim that artificial intelligence is best understood not as a break from classical signal processing, but as its high-dimensional, adaptive, and learned continuation. The book begins with the mathematics of signal…s, representation spaces, uncertainty, Fourier and wavelet analysis, optimization, statistical learning, kernels, and nonlinear operators, showing that the essential problems of AI-perception, estimation, compression, prediction, and decision-already live inside the deeper structure of signal-processing theory. From that foundation, it builds a unified language in which observations become structured signals, learned models become operators on representation spaces, and intelligence itself becomes the transformation of uncertain measurements into useful internal state, inference, and action. The result is a mathematically rigorous bridge from classical analysis to modern machine learning, grounded in Hilbert spaces, stochastic processes, spectral methods, and variational principles.As the book progresses, it extends this framework into the core architectures and frontier problems of contemporary AI: convolutional networks, recurrent and state-space models, transformers, self-supervised learning, multimodal fusion, generative modeling, diffusion, causal representation learning, world models, agentic planning, safety, and the search for a unified theory of intelligent systems. Rather than treating these as disconnected technologies, the manuscript argues that they are all instances of a common mathematical pattern: structured observation, representation, latent dynamics, operator adaptation, and decision under uncertainty. In that sense, the book is both a graduate-level theoretical synthesis and a research program. It offers a coherent view of how signal processing, probability, geometry, optimization, and dynamical systems can be brought together to explain modern AI and to frame the path toward more general, robust, and scientifically grounded intelligence. This book presents AI as the mathematically rigorous continuation of signal processing, showing how representation, learning, generation, memory, planning, and control can be unified within a single framework of signals, and adaptive systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Hardcover. Condition: new. Hardcover. This book develops a rigorous, end-to-end theory of intelligent magnetic resonance imaging by unifying the physics of spin systems, the mathematics of inverse problems, and the modern machinery of artificial intelligence. Beginning from the quantum and semiclassical foundations of nuclear ma…gnetic resonance, it builds systematically through Bloch dynamics, signal formation, Fourier encoding, image reconstruction, parameter estimation, uncertainty quantification, and the geometry of learned representations. Across the chapters, MRI is treated not simply as an imaging modality but as a layered computational-physical system in which measurements, models, and inference are inseparably linked. The book shows how classical tools such as Hilbert-space analysis, regularization theory, stochastic processes, and optimization evolve naturally into contemporary methods involving deep neural networks, graph models, generative priors, diffusion reconstruction, Bayesian inference, and operator learning. At the same time, the book argues that the future of MRI lies in systems that are not merely automated, but mathematically grounded, adaptive, and scientifically interpretable. It examines how AI can guide acquisition, reconstruction, multimodal fusion, motion correction, artifact suppression, quantitative imaging, and even agentic workflow orchestration across the entire MRI pipeline. Throughout, the emphasis remains on preserving physical consistency, clinical trustworthiness, and theoretical clarity while extending MRI into a new era of intelligent imaging. The result is a comprehensive research-level treatment of how magnetic resonance imaging is being transformed from a sequence of handcrafted procedures into a unified framework of physics-constrained, data-driven, and increasingly autonomous inference. A rigorous research-level book on the physics, mathematics, and artificial intelligence of MRI, showing how spin dynamics, inverse problems, and modern AI come together to create intelligent imaging systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Hardcover. Condition: new. Hardcover. Optical Intelligence develops a rigorous and wide-ranging theory of how optics and artificial intelligence can be understood within a single mathematical and physical framework. The book begins from first principles, treating light as a structured medium of information transformation governe…d by Maxwell's equations, wave propagation, Fourier analysis, statistical optics, and inverse problems. From there, it shows how classical optical processing, computational imaging, and functional analysis naturally connect to core ideas in modern AI, including representation learning, operator composition, kernel methods, optimization, and inference. Its central thesis is that optics is not merely a sensor front-end for digital intelligence, but a physically grounded computational substrate whose geometry, propagation laws, and material structure can actively shape learning and decision-making.As the book progresses, it moves from foundations into advanced architectures and emerging research directions, including diffractive neural networks, integrated photonic systems, nonlinear optical learning, optical reservoirs, holographic memory, and hybrid optical-electronic intelligence. Throughout, the text emphasizes both theoretical depth and systems-level insight, showing how physical propagation, measurement, and learning can be co-designed to create new forms of intelligent sensing and computation. The result is a unified account of optical intelligence as a serious scientific discipline at the intersection of electromagnetism, signal processing, machine learning, and information theory. Rather than presenting optics and AI as separate domains that occasionally interact, the book argues that their deepest future lies in their integration into trainable, physically embodied systems for imaging, inference, communication, and adaptive computation. Optical Intelligence is a rigorous exploration of the emerging field where optics and artificial intelligence meet. Blending the physics of light with the mathematics of information, inference, and learning. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Buch. Condition: Neu. Optical Intelligence | Andrew Kiruluta | Buch | Englisch | 2026 | BlochSpin | EAN 9798904174828 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Buch. Condition: Neu. From Signal Processing to AGI | A Mathematical Foundation | Andrew Kiruluta | Buch | Englisch | 2026 | BlochSpin | EAN 9798904174842 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - From Signal Processing to AGI: A Mathematical Foundation develops the central claim that artificial intelligence is best understood not as a break from classical signal processing, but as its high-dimensional, adaptive, and learned continuation…. The book begins with the mathematics of signals, representation spaces, uncertainty, Fourier and wavelet analysis, optimization, statistical learning, kernels, and nonlinear operators, showing that the essential problems of AI-perception, estimation, compression, prediction, and decision-already live inside the deeper structure of signal-processing theory. From that foundation, it builds a unified language in which observations become structured signals, learned models become operators on representation spaces, and intelligence itself becomes the transformation of uncertain measurements into useful internal state, inference, and action. The result is a mathematically rigorous bridge from classical analysis to modern machine learning, grounded in Hilbert spaces, stochastic processes, spectral methods, and variational principles.As the book progresses, it extends this framework into the core architectures and frontier problems of contemporary AI: convolutional networks, recurrent and state-space models, transformers, self-supervised learning, multimodal fusion, generative modeling, diffusion, causal representation learning, world models, agentic planning, safety, and the search for a unified theory of intelligent systems. Rather than treating these as disconnected technologies, the manuscript argues that they are all instances of a common mathematical pattern: structured observation, representation, latent dynamics, operator adaptation, and decision under uncertainty. In that sense, the book is both a graduate-level theoretical synthesis and a research program. It offers a coherent view of how signal processing, probability, geometry, optimization, and dynamical systems can be brought together to explain modern AI and to frame the path toward more general, robust, and scientifically grounded intelligence.
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book develops a rigorous, end-to-end theory of intelligent magnetic resonance imaging by unifying the physics of spin systems, the mathematics of inverse problems, and the modern machinery of artificial intelligence. Beginning from the qua…ntum and semiclassical foundations of nuclear magnetic resonance, it builds systematically through Bloch dynamics, signal formation, Fourier encoding, image reconstruction, parameter estimation, uncertainty quantification, and the geometry of learned representations. Across the chapters, MRI is treated not simply as an imaging modality but as a layered computational-physical system in which measurements, models, and inference are inseparably linked. The book shows how classical tools such as Hilbert-space analysis, regularization theory, stochastic processes, and optimization evolve naturally into contemporary methods involving deep neural networks, graph models, generative priors, diffusion reconstruction, Bayesian inference, and operator learning. At the same time, the book argues that the future of MRI lies in systems that are not merely automated, but mathematically grounded, adaptive, and scientifically interpretable. It examines how AI can guide acquisition, reconstruction, multimodal fusion, motion correction, artifact suppression, quantitative imaging, and even agentic workflow orchestration across the entire MRI pipeline. Throughout, the emphasis remains on preserving physical consistency, clinical trustworthiness, and theoretical clarity while extending MRI into a new era of intelligent imaging. The result is a comprehensive research-level treatment of how magnetic resonance imaging is being transformed from a sequence of handcrafted procedures into a unified framework of physics-constrained, data-driven, and increasingly autonomous inference.
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Optical Intelligence develops a rigorous and wide-ranging theory of how optics and artificial intelligence can be understood within a single mathematical and physical framework. The book begins from first principles, treating light as a structu…red medium of information transformation governed by Maxwell's equations, wave propagation, Fourier analysis, statistical optics, and inverse problems. From there, it shows how classical optical processing, computational imaging, and functional analysis naturally connect to core ideas in modern AI, including representation learning, operator composition, kernel methods, optimization, and inference. Its central thesis is that optics is not merely a sensor front-end for digital intelligence, but a physically grounded computational substrate whose geometry, propagation laws, and material structure can actively shape learning and decision-making.As the book progresses, it moves from foundations into advanced architectures and emerging research directions, including diffractive neural networks, integrated photonic systems, nonlinear optical learning, optical reservoirs, holographic memory, and hybrid optical-electronic intelligence. Throughout, the text emphasizes both theoretical depth and systems-level insight, showing how physical propagation, measurement, and learning can be co-designed to create new forms of intelligent sensing and computation. The result is a unified account of optical intelligence as a serious scientific discipline at the intersection of electromagnetism, signal processing, machine learning, and information theory. Rather than presenting optics and AI as separate domains that occasionally interact, the book argues that their deepest future lies in their integration into trainable, physically embodied systems for imaging, inference, communication, and adaptive computation.
