The Singularity Expansion Method In Electromagnetics A Summary Survey And Open Questions
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THE SINGULARITY EXPANSION METHOD IN ELECTROMAGNETICS: A SUMMARY SURVEY AND OPEN QUESTIONS
The Singularity Expansion Method (SEM) is probably the most celebrated feather in Carl Baum's cap, among his many other accomplishments such as Nuclear Electromagnetic Pulse (NEMP) simulators, sensors, EM topology etc. SEM was born out of a simple realization that the natural frequencies of both simple (ex: a piece of conducting wire) and complex objects (ex: Boeing 747) are points in the complex frequency or the s-plane. Although expansions based on natural frequencies in mathematical physics preceded Carl's SEM formulation, he must be credited with its vast applicability in the field of NEMP during the cold war era. Coupling coefficients are described as the "transfer function" between the incident waves and the natural modes. The determination of the coupling coefficients was an important step on the path to synthesizing the responses.
Chipless RFID
This book examines the design of chipless RFID systems. The authors begin with the philosophy of RFID and its effect on commercial applications. Then, they discuss the chipless RFID systems and the application of chipless RFID systems, the advantages it provides compared to conventional barcode ID and chipped RFID tags. The text then covers chipless RFID components in block diagram representation and introduce FCC requirements which should be considered in the design procedure of each component. The third chapter is dedicated to the complex natural resonance-based design of chipless RFID tags. The next chapter concerns about the detection techniques introduced for the identification of chipless RFID tags. The fifth chapter is dedicated to the localization and anti-collision techniques in chipless RFID systems. Final chapter is chipless RFID tags as sensors. It provides some applications where the tag can be used as both ID and sensor. The tag specifications and detection issues are addressed in this section.
Numerical Methods for Large Eigenvalue Problems
This revised edition discusses numerical methods for computing the eigenvalues and eigenvectors of large sparse matrices. It provides an in-depth view of the numerical methods that are applicable for solving matrix eigenvalue problems that arise in various engineering and scientific applications. Each chapter was updated by shortening or deleting outdated topics, adding topics of more recent interest and adapting the Notes and References section. Significant changes have been made to Chapters 6 through 8, which describe algorithms and their implementations and now include topics such as the implicit restart techniques, the Jacobi-Davidson method and automatic multilevel substructuring.