Advanced Finite Difference Time Domain Methods With The Applications To Resonant Propagation And Scattering Problems

Theory and Computation of Electromagnetic Fields

Reviews the fundamental concepts behind the theory and computation of electromagnetic fields The book is divided in two parts. The first part covers both fundamental theories (such as vector analysis, Maxwell’s equations, boundary condition, and transmission line theory) and advanced topics (such as wave transformation, addition theorems, and fields in layered media) in order to benefit students at all levels. The second part of the book covers the major computational methods for numerical analysis of electromagnetic fields for engineering applications. These methods include the three fundamental approaches for numerical analysis of electromagnetic fields: the finite difference method (the finite difference time-domain method in particular), the finite element method, and the integral equation-based moment method. The second part also examines fast algorithms for solving integral equations and hybrid techniques that combine different numerical methods to seek more efficient solutions of complicated electromagnetic problems. Theory and Computation of Electromagnetic Fields, Second Edition: Provides the foundation necessary for graduate students to learn and understand more advanced topics Discusses electromagnetic analysis in rectangular, cylindrical and spherical coordinates Covers computational electromagnetics in both frequency and time domains Includes new and updated homework problems and examples Theory and Computation of Electromagnetic Fields, Second Edition is written for advanced undergraduate and graduate level electrical engineering students. This book can also be used as a reference for professional engineers interested in learning about analysis and computation skills.

Advanced Pattern Recognition Techniques

Pattern recognition is the extraction of consistent information from noisy spatiotemporal data. It can be and is currently being used in systems for battlefield supervision, smart weapons, and anti-counterfeiting of all kinds. A currrent application is the automatic detection of land mines and unexploded ordnance. (UXO). The methods employed can be subdivided in the following manner: (1) statistical methods, (2) neuro - methods, (3) fuzzy - methods, and (4) neuro fuzzy methods. Each of these methods has its special advantages and drawbacks, but all of them require the computation of feature variables from measurement or simulation data, e.g. from microwave backscattering. The Lecture series covers the following topics: (1) Introductory overview on pattern recognition techniques, (1) - (4); (2)Feature extraction for pattern recognition by; (a)Electromagnetic, magnetic, and acoustic singularity identification; (b)Model based scattering signatures; (c) Wavelet techniques; (d) SAR/ISAR imaging; (e)Bistatic microwave imaging; and (f)Electromagnetic inversion techniques; (3) Real-time implementation of pattern recognition methods; and (4)Introduction to software and hardware for pattern recognition.

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