Shi Jin – författare
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9 produkter
9 produkter
Inbunden, Engelska, 2025
935 kr
Skickas inom 7-10 vardagar
This focused textbook demonstrates cutting-edge concepts at the intersection of machine learning (ML) and wireless communications, providing students with a deep and insightful understanding of this emerging field. It introduces students to a broad array of ML tools for effective wireless system design, and supports them in exploring ways in which future wireless networks can be designed to enable more effective deployment of federated and distributed learning techniques to enable AI systems. Requiring no previous knowledge of ML, this accessible introduction includes over 20 worked examples demonstrating the use of theoretical principles to address real-world challenges, and over 100 end-of-chapter exercises to cement student understanding, including hands-on computational exercises using Python. Accompanied by code supplements and solutions for instructors, this is the ideal textbook for a single-semester senior undergraduate or graduate course for students in electrical engineering, and an invaluable reference for academic researchers and professional engineers in wireless communications.
E-bok
PDF, Engelska, 20251 108 kr
Läs direkt efter köp
Del 14 - SEMA SIMAI Springer Series
Uncertainty Quantification for Hyperbolic and Kinetic Equations
Häftad, Engelska, 2018
1 184 kr
Skickas inom 10-15 vardagar
This book explores recent advances in uncertainty quantification for hyperbolic, kinetic, and related problems. The contributions address a range of different aspects, including: polynomial chaos expansions, perturbation methods, multi-level Monte Carlo methods, importance sampling, and moment methods. The interest in these topics is rapidly growing, as their applications have now expanded to many areas in engineering, physics, biology and the social sciences. Accordingly, the book provides the scientific community with a topical overview of the latest research efforts.
Inbunden, Engelska, 2019
1 077 kr
Skickas inom 10-15 vardagar
This book tackles important problems of anomaly detection and health status analysis in complex core router systems, integral to today’s Internet Protocol (IP) networks. The techniques described provide the first comprehensive set of data-driven resiliency solutions for core router systems. The authors present an anomaly detector for core router systems using correlation-based time series analysis, which monitors a set of features of a complex core router system. They also describe the design of a changepoint-based anomaly detector such that anomaly detection can be adaptive to changes in the statistical features of data streams. The presentation also includes a symbol-based health status analyzer that first encodes, as a symbol sequence, the long-term complex time series collected from a number of core routers, and then utilizes the symbol sequence for health analysis. Finally, the authors describe an iterative, self-learning procedure for assessing the health status.Enables Accurate Anomaly Detection Using Correlation-Based Time-Series Analysis;Presents the design of a changepoint-based anomaly detector;Includes Hierarchical Symbol-based Health-Status Analysis;Describes an iterative, self-learning procedure for assessing the health status.
E-bok
PDF, Engelska, 20191 179 kr
Läs direkt efter köp
This book tackles important problems of anomaly detection and health status analysis in complex core router systems, integral to today’s Internet Protocol (IP) networks. The techniques described provide the first comprehensive set of data-driven resiliency solutions for core router systems. The authors present an anomaly detector for core router systems using correlation-based time series analysis, which monitors a set of features of a complex core router system. They also describe the design of a changepoint-based anomaly detector such that anomaly detection can be adaptive to changes in the statistical features of data streams. The presentation also includes a symbol-based health status analyzer that first encodes, as a symbol sequence, the long-term complex time series collected from a number of core routers, and then utilizes the symbol sequence for health analysis. Finally, the authors describe an iterative, self-learning procedure for assessing the health status.Enables Accurate Anomaly Detection Using Correlation-Based Time-Series Analysis;Presents the design of a changepoint-based anomaly detector;Includes Hierarchical Symbol-based Health-Status Analysis;Describes an iterative, self-learning procedure for assessing the health status.
Häftad, Engelska, 2020
916 kr
Skickas inom 10-15 vardagar
This book tackles important problems of anomaly detection and health status analysis in complex core router systems, integral to today’s Internet Protocol (IP) networks. The techniques described provide the first comprehensive set of data-driven resiliency solutions for core router systems. The authors present an anomaly detector for core router systems using correlation-based time series analysis, which monitors a set of features of a complex core router system. They also describe the design of a changepoint-based anomaly detector such that anomaly detection can be adaptive to changes in the statistical features of data streams. The presentation also includes a symbol-based health status analyzer that first encodes, as a symbol sequence, the long-term complex time series collected from a number of core routers, and then utilizes the symbol sequence for health analysis. Finally, the authors describe an iterative, self-learning procedure for assessing the health status.Enables Accurate Anomaly Detection Using Correlation-Based Time-Series Analysis;Presents the design of a changepoint-based anomaly detector;Includes Hierarchical Symbol-based Health-Status Analysis;Describes an iterative, self-learning procedure for assessing the health status.
Del 14 - SEMA SIMAI Springer Series
Uncertainty Quantification for Hyperbolic and Kinetic Equations
Inbunden, Engelska, 2018
1 184 kr
Skickas inom 10-15 vardagar
This book explores recent advances in uncertainty quantification for hyperbolic, kinetic, and related problems. The contributions address a range of different aspects, including: polynomial chaos expansions, perturbation methods, multi-level Monte Carlo methods, importance sampling, and moment methods. The interest in these topics is rapidly growing, as their applications have now expanded to many areas in engineering, physics, biology and the social sciences. Accordingly, the book provides the scientific community with a topical overview of the latest research efforts.
E-bok
Engelska, 20181 582 kr
Läs direkt efter köp
This book explores recent advances in uncertainty quantification for hyperbolic, kinetic, and related problems. The contributions address a range of different aspects, including: polynomial chaos expansions, perturbation methods, multi-level Monte Carlo methods, importance sampling, and moment methods. The interest in these topics is rapidly growing, as their applications have now expanded to many areas in engineering, physics, biology and the social sciences. Accordingly, the book provides the scientific community with a topical overview of the latest research efforts.
Inbunden, Engelska, 2027
1 965 kr
Kommande
The relentless evolution of computing—from classical algorithms to artificial intelligence (AI), brain-inspired architectures, and quantum systems—demands a reimagining of the mathematical foundations that underpin these transformative technologies. Edited by Prof. Shi Jin from Shanghai Jiao Tong University, this volume bridges abstract mathematical theory and cutting-edge computational practice, equipping researchers, engineers, and students with fundamental understanding to navigate and shape the future of scientific computing.This book unifies interdisciplinary advances in quantum computation, neural and brain-inspired systems, AI-driven molecular modeling, and data-intensive particle dynamics under a cohesive mathematical framework. It synthesizes insights from 9 meticulously structured chapters, each authored by leading experts, to address the mathematical challenges and innovations arising in:- Quantum advantage for solving partial differential equations (PDEs),- Biologically plausible AI modeling brain dynamics and protein engineering,- Statistical rigor in randomized experiments and observational studies,- Operator learning for kinetic equations and interacting particle systems,- Graph/hypergraph neural networks via phase-transition physics.The book aims to reveal the mathematical "language" of next-generation computing paradigms. As computing paradigms fracture into specialized niches, this volume is a unifying compass. It transforms isolated breakthroughs—such as particle-based graph networks, operator learning for chemistry, or quantum PDE solvers—into a coherent mathematical arsenal. For mathematicians, it reveals uncharted problems in computation; for engineers, it provides rigor to harness AI, quantum, and bio-inspired tools; for students, it maps the emerging landscape where equations meet evolution.