Relativistic Field Theory For Microwave Engineers
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Relativistic Field Theory for Microwave Engineers
This book explores the fundamental but often overlooked connection between Maxwell's equations, as they are taught in undergraduate electrical engineering courses, and special relativity. Written for an audience of practical engineers instead of theoretical physicists, it exposes the underlying contradictions brought about by the emergence of electromagnetic theory, one of the greatest triumphs in mathematical physics of all time that unified the phenomena of electricity, magnetism, and light, into a world in which the classical Galilean principle of relativity was considered incontrovertible. It explains how Einstein redefined the concepts of space and time and what it means to measure them, while altogether disbanding the notion of global simultaneity. A manifestly relativistic formulation of electromagnetic laws is first presented and then applied to common engineering problems, like the interaction of electromagnetic fields at dynamic interfaces, the derivation of propagating modes in closed metal waveguides, and the foundations of microwave network theory. Mathematical toolkits for relativistic analysis, such as tensor notation and spacetime algebra, are explained. These tools are then used to analyze the consequences of motion at relativistic speeds upon otherwise well-known electromagnetic circuit behaviors. Well-drawn and insightful diagrams along with articulate explanations help the reader to gain an intuitive understanding of four-dimensional spacetime and the nature of the electromagnetic field in that context, while summary tables and comprehensive appendices serve as a resource for further selfdirected exploration. Readers trained in microwave engineering will learn to see their field from a new perspective, and shall gain from that new insight the ability to conceive of unexpected solutions to practical engineering problems that might otherwise defy one's intuition.
Microstrip Lines and Slotlines, Fourth Edition
Microstrip Lines and Slotlines, Fourth Edition is an indispensable resource for practicing engineers. This edition is updated to reflect the latest developments in the field, providing extensive analysis techniques and CAD design and modeling information. This volume covers approximate and full-wave analysis techniques, accurate circuit model extraction, and design information focusing on miniaturization, broadband operation, and precise design. It also incorporates the most recent information from conferences, journals, books, and personal research. New topics on Metamaterial Based Transmission Lines and Numerical Simulation and Modeling of transmission lines are introduced, aligning with the book's core themes. Some of the key updates included in this fourth edition are the integration of significant materials, significant revisions reflecting the latest research, and the introduction of cutting-edge topics, enhancing the existing content. With these comprehensive updates, Microstrip Lines and Slotlines, Fourth Edition ensures that readers have access to the most relevant and cutting-edge information in the field of printed transmission lines. This book is a must-have for professionals and researchers dedicated to advancing their knowledge and expertise in this dynamic area.
Radio Frequency Machine Learning: A Practical Deep Learning Perspective
Radio Frequency Machine Learning: A Practical Deep Learning Perspective goes beyond general introductions to deep learning, offering a focused exploration of how modern deep learning techniques can be applied directly to radio frequency (RF) challenges. It covers a wide range of applications, including classification tasks where deep learning is used to label and categorize signals based on a labeled training dataset, as well as clustering tasks that group similar signals together without labels. Additionally, it expands into deep learning (generative AI) for waveform synthesis and how reinforcement learning can be used within the domain. This book also investigates advanced topics like RF sensor control, feedback mechanisms, and real-time system operations, offering a comprehensive understanding of how deep learning can be integrated into dynamic RF environments. This resource addresses the practical concerns of deploying machine learning in operational RF systems. It goes beyond applications and techniques, covering how to ensure the robustness of solutions, with insights into data sources, augmentation techniques, and strategies for integrating ML with existing RF infrastructure. The full development process is examined, from data collection to deployment, along with numerous case studies throughout. Looking to the future, the book explores emerging trends like edge computing and federated learning, offering a forward-looking perspective on the continued evolution of RF machine learning. Whether the reader is just beginning the journey into RF machine learning or is looking to refine skills, this book provides an essential resource for understanding the intersection of deep learning and RF technology. This is a must-have resource for anyone interested in the cutting edge of wireless technologies and their potential to shape the future of communication.