Jun Liu – författare

This book is invaluable in enabling wireless operators to extract maximum performance offered by 3GPP’s HSPA radio technology, consisting of both downlink (HSDPA) and uplink (HSUPA) elements. It focuses on real-world performance, sharing practical implementation methods and tradeoffs for deploying, optimizing and maintaining networks using the HSPA air interface.

This book offers a systematic presentation of persymmetric adaptive detection, including detector derivations and the definition of key concepts, followed by detailed discussion relating to theoretical underpinnings, design methodology, design considerations, and techniques enabling its practical implementation.

The received data for modern radar systems are usually multichannel, namely, vector-valued, or even matrix-valued. Multichannel signal detection in Gaussian backgrounds is a fundamental problem for radar applications. With an overarching focus on persymmetric adaptive detectors, this book presents the mathematical models and design principles necessary for analyzing the behavior of each kind of persymmetric adaptive detector. Building upon that, it also introduces new design approaches and techniques that will guide engineering students as well as radar engineers toward efficient detector solutions, especially in challenging sample-starved environments where training data are limited.

This book will be of interest to students, scholars, and engineers in the field of signal processing. It will be especially useful for those who have a solid background in statistical signal processing, multivariate statistical analysis, matrix theory, and mathematical analysis.

This book offers a systematic presentation of persymmetric adaptive detection, including detector derivations and the definition of key concepts, followed by detailed discussion relating to theoretical underpinnings, design methodology, design considerations, and techniques enabling its practical implementation.

The received data for modern radar systems are usually multichannel, namely, vector-valued, or even matrix-valued. Multichannel signal detection in Gaussian backgrounds is a fundamental problem for radar applications. With an overarching focus on persymmetric adaptive detectors, this book presents the mathematical models and design principles necessary for analyzing the behavior of each kind of persymmetric adaptive detector. Building upon that, it also introduces new design approaches and techniques that will guide engineering students as well as radar engineers toward efficient detector solutions, especially in challenging sample-starved environments where training data are limited.

This book will be of interest to students, scholars, and engineers in the field of signal processing. It will be especially useful for those who have a solid background in statistical signal processing, multivariate statistical analysis, matrix theory, and mathematical analysis.

Formal methods is a field of computer science that emphasizes the use of rigorous mathematical techniques for verification and design of hardware and software systems. Analysis and design of nonlinear control design plays an important role across many disciplines of engineering and applied sciences, ranging from the control of an aircraft engine to the design of genetic circuits in synthetic biology.

While linear control is a well-established subject, analysis and design of nonlinear control systems remains a challenging topic due to some of the fundamental difficulties caused by nonlinearity. Formal Methods for Control of Nonlinear Systems provides a unified computational approach to analysis and design of nonlinear systems.

Features

Formal methods is a field of computer science that emphasizes the use of rigorous mathematical techniques for verification and design of hardware and software systems. Analysis and design of nonlinear control design plays an important role across many disciplines of engineering and applied sciences, ranging from the control of an aircraft engine to the design of genetic circuits in synthetic biology.

While linear control is a well-established subject, analysis and design of nonlinear control systems remains a challenging topic due to some of the fundamental difficulties caused by nonlinearity. Formal Methods for Control of Nonlinear Systems provides a unified computational approach to analysis and design of nonlinear systems.

Features

Artificial intelligence (AI) is impacting industries worldwide by introducing new methods and altering traditional practices. This book examines AI’s diverse effects, providing insights into its applications, challenges, and future prospects across education, healthcare, finance, and more.

The chapters explore how AI technologies, such as large language models, enhance feedback in higher education and influence legal studies while upholding academic integrity. A review of key technical approaches—knowledge-based systems, machine learning, and intelligent optimization—lays the groundwork for understanding AI’s potential. Real-world examples illustrate AI’s role in medical imaging, presenting new diagnostic methods and the use of language models for image interpretation. The book also discusses financial applications, including techniques for credit card fraud detection and forecasting natural gas prices using innovative models. Additionally, it covers personalized federated learning models, highlighting the importance of data privacy and security in AI’s evolution.

This comprehensive guide is valuable for educators, researchers, practitioners, and students interested in AI’s current and future developments. By combining theory with practical examples, the book offers readers a clear understanding of how AI affects various sectors, enabling them to engage effectively with this rapidly evolving field.

Artificial intelligence (AI) is impacting industries worldwide by introducing new methods and altering traditional practices. This book examines AI’s diverse effects, providing insights into its applications, challenges, and future prospects across education, healthcare, finance, and more.

The chapters explore how AI technologies, such as large language models, enhance feedback in higher education and influence legal studies while upholding academic integrity. A review of key technical approaches—knowledge-based systems, machine learning, and intelligent optimization—lays the groundwork for understanding AI’s potential. Real-world examples illustrate AI’s role in medical imaging, presenting new diagnostic methods and the use of language models for image interpretation. The book also discusses financial applications, including techniques for credit card fraud detection and forecasting natural gas prices using innovative models. Additionally, it covers personalized federated learning models, highlighting the importance of data privacy and security in AI’s evolution.

This comprehensive guide is valuable for educators, researchers, practitioners, and students interested in AI’s current and future developments. By combining theory with practical examples, the book offers readers a clear understanding of how AI affects various sectors, enabling them to engage effectively with this rapidly evolving field.

This volume details generation of gene-edited cell lines and organisms as models for human diseases, pest control, and large animal welfare and production outcomes. Chapters guide readers through gene regulation, editing, screening of cell lines, genome editing, and an overview of the tools for efficient genome editing including; ZFNs, TALENs, and CRISPR. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and reagents, includes tips on troubleshooting and known pitfalls, and step- by-step, readily reproducible protocols.

Authoritative and cutting-edge, Applications of Genome Modulation and Editing aims to be a useful and practical guide for researchers to commence or advance their study in this field.

Explore a comprehensive and practical approach to reinforcement learning

Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory—optimal control and dynamic programming – or on algorithms—most of which are simulation-based.

Model-Based Reinforcement Learning provides a model-based framework to bridge these two aspects, thereby creating a holistic treatment of the topic of model-based online learning control. In doing so, the authors seek to develop a model-based framework for data-driven control that bridges the topics of systems identification from data, model-based reinforcement learning, and optimal control, as well as the applications of each. This new technique for assessing classical results will allow for a more efficient reinforcement learning system. At its heart, this book is focused on providing an end-to-end framework—from design to application—of a more tractable model-based reinforcement learning technique.

Model-Based Reinforcement Learning readers will also find:

Model-Based Reinforcement Learning is a useful reference for senior undergraduate students, graduate students, research assistants, professors, process control engineers, and roboticists.

Explore a comprehensive and practical approach to reinforcement learning

Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory—optimal control and dynamic programming – or on algorithms—most of which are simulation-based.

Model-Based Reinforcement Learning provides a model-based framework to bridge these two aspects, thereby creating a holistic treatment of the topic of model-based online learning control. In doing so, the authors seek to develop a model-based framework for data-driven control that bridges the topics of systems identification from data, model-based reinforcement learning, and optimal control, as well as the applications of each. This new technique for assessing classical results will allow for a more efficient reinforcement learning system. At its heart, this book is focused on providing an end-to-end framework—from design to application—of a more tractable model-based reinforcement learning technique.

Model-Based Reinforcement Learning readers will also find:

Model-Based Reinforcement Learning is a useful reference for senior undergraduate students, graduate students, research assistants, professors, process control engineers, and roboticists.