Introduction to Microwave Imaging.- Sequential Forward Solver.- Global Optimization: Differential Evolution, Genetic Algorithms, Particle Swarm and Hybrid Methods.- Sequential Optimization: Genetic Algorithm.- Inclusion of A Priori Information using Neural Networks.- Parallel Forward Solver.- Parallel Optimization Methods.- Benchmarking Parallel Evolutionary Algorithms.
Über den Autor
Sima Noghanian is an associate professor and chair of the Antenna and Applied Electromagnetics department of the University of North Dakota.
Travis Desell is an assistant professor at the University of North Dakota.
Abas Sabouni is a research associate at Concordia University.
Ali Ashtari is the lead researcher at Invenia Technical Computing.
Inhaltsverzeichnis
Introduction to Microwave Imaging.- Sequential Forward Solver.- Global Optimization: Differential Evolution, Genetic Algorithms, Particle Swarm and Hybrid Methods.- Sequential Optimization: Genetic Algorithm.- Inclusion of A Priori Information using Neural Networks.- Parallel Forward Solver.- Parallel Optimization Methods.- Benchmarking Parallel Evolutionary Algorithms.
Klappentext
This book provides a detailed overview on the use of global optimization and parallel computing in microwave tomography techniques. The book focuses on techniques that are based on global optimization and electromagnetic numerical methods. The authors provide parallelization techniques on homogeneous and heterogeneous computing architectures on high performance and general purpose futuristic computers. The book also discusses the multi-level optimization technique, hybrid genetic algorithm and its application in breast cancer imaging.
Focuses on microwave tomography imaging which provide quantitative images and requires solve inverse scattering problem using iterative technique
Introduces techniques that can be efficiently used for real time analysis of medical imaging techniques
Examines the combination of classification methods to include a priori information, global optimization and numerical forward solver that is unique
