Radoslaw Kycia

Anisotropy of Metamaterials: Beyond Conventional Paradigms provides a comprehensive introduction to the mathematical modeling of metamaterials based on the macroscopic complex-valued permittivity tensor of dispersed random composites. Key topics include physical and mathematical theory, computer simulations, constructive homogenization, classification of dispersed random composites and their applications in cancer recognition. Image processing and machine learning algorithms are used. The book also discusses the precision of various effective medium approximations, including Bruggeman and Maxwell-Garnett formulas. New analytical, approximate and exact formulas and bounds for the macroscopic permittivity and piezoelectric constants of composites are derived. This book is a valuable tool for academics and professionals in photonics, presenting sustainable materials for sensing, health diagnostics and cancer detection methodologies. Key features: Offers key insights into the current trends and techniques in the study of the macroscopic properties of metamaterials, aiming at stimulating new avenues of research Presents examples of image analysis, the primary tool for non-destructive metamaterials analysis Discusses the applications of Machine Learning to image processing, illustrated using specific code in Python programming language

Complex, Hypercomplex, and Fuzzy-valued Neural Networks are extensions of classical neural networks to higher dimensions. In recent decades, this theory has emerged as a forefront in neural networks theory. There are several approaches to extend classical neural network models: quaternionic analysis, which merely uses quaternions; Clifford analysis, which relies on Clifford algebras; and finally generalizations of complex variables to higher dimensions. This book reflects a selection of papers related to complex, hypercomplex analysis, and fuzzy approaches applied to neural networks theory. The topics covered represent new perspectives and current trends in neural networks and their applications to mathematical physics, image analysis and processing, mechanics, and beyond.

Anisotropy of Metamaterials: Beyond Conventional Paradigms provides a comprehensive introduction to the mathematical modeling of metamaterials based on the macroscopic complex-valued permittivity tensor of dispersed random composites. Key topics include physical and mathematical theory, computer simulations, constructive homogenization, classification of dispersed random composites and their applications in cancer recognition. Image processing and machine learning algorithms are used. The book also discusses the precision of various effective medium approximations, including Bruggeman and Maxwell-Garnett formulas. New analytical, approximate and exact formulas and bounds for the macroscopic permittivity and piezoelectric constants of composites are derived. This book is a valuable tool for academics and professionals in photonics, presenting sustainable materials for sensing, health diagnostics and cancer detection methodologies. Key features:Offers key insights into the current trends and techniques in the study of the macroscopic properties of metamaterials, aiming at stimulating new avenues of researchPresents examples of image analysis, the primary tool for non-destructive metamaterials analysisDiscusses the applications of Machine Learning to image processing, illustrated using specific code in Python programming language