Based on the bestselling Artech House classic title, Hilbert Transforms Signal Processing, this comprehensive new resource introduces complex and hypercomplex analytic signals and their applications. Professionals find in-depth explanations of the theory of multidimensional complex and hypercomplex signals illustrated with numerous examples and followed by practical applications. The survey of chosen hypercomplex algebras and the orthants of the n-dimensional Cartesian space and single-orthant operators are explored. This book also covers topics including, the polar representation of analytic signals, quasi-analytic signals, the space-frequency of n-D complex and hypercomplex signals as well as the causality of signals.
Quaternion and Clifford Fourier and wavelet transformations generalize the classical theory to higher dimensions and are becoming increasingly important in diverse areas of mathematics, physics, computer science and engineering. This edited volume presents the state of the art in these hypercomplex transformations. The Clifford algebras unify Hamilton’s quaternions with Grassmann algebra. A Clifford algebra is a complete algebra of a vector space and all its subspaces including the measurement of volumes and dihedral angles between any pair of subspaces. Quaternion and Clifford algebras permit the systematic generalization of many known concepts. This book provides comprehensive insights into current developments and applications including their performance and evaluation. Mathematically, it indicates where further investigation is required. For instance, attention is drawn to the matrix isomorphisms for hypercomplex algebras, which will help readers to see that software implementations are within our grasp. It also contributes to a growing unification of ideas and notation across the expanding field of hypercomplex transforms and wavelets. The first chapter provides a historical background and an overview of the relevant literature, and shows how the contributions that follow relate to each other and to prior work. The book will be a valuable resource for graduate students as well as for scientists and engineers.
Hypercomplex analysis is the extension of complex analysis to higher dimensions where the concept of a holomorphic function is substituted by the concept of a monogenic function. In recent decades this theory has come to the forefront of higher dimensional analysis. There are several approaches to this: quaternionic analysis which merely uses quaternions, Clifford analysis which relies on Clifford algebras, and generalizations of complex variables to higher dimensions such as split-complex variables. This book includes a selection of papers presented at the session on quaternionic and hypercomplex analysis at the ISAAC conference 2013 in Krakow, Poland. The topics covered represent new perspectives and current trends in hypercomplex analysis and applications to mathematical physics, image analysis and processing, and mechanics.
Feature extraction and, particularly, orientation estimation of multidimensional images is of paramount importance for the Image Processing and Computer Vision communities. This dissertation focuses on this topic; specifically, we deal with the problem of local structure tensor (LST) estimation, as a mean of characterizing the local behavior of a multidimensional signal. The LST can be seen as a measure of the uncertainty of a multidimensional signal with respect to a given orientation. LST estimation can be achieved by estimating the local energy of a signal in different orientations. Then, the LST is computed as a linear combination of the local energy for each orientation with a tensor basis whose elements are calculated for each orientation. This kind of methods for the estimation of the LST are based on quadrature filters to obtain the local energy of the signal. While the LST based on quadrature filters is well defined for signals that vary locally only in one orientation (simple signals), the estimation method fails with complex signals, i.e. signals that consist of several differently-oriented simple signals. In this dissertation, an analytical study of the distortions of the tensor eigenvalues due to such complex neighborhoods is carried out. From this analytical study, two constructive methods are proposed for the estimation of the LST. The first method is based on a maximum likelihood estimation of the quadrature filter outputs. The second method uses a measure of phase consistency based on generalized quadrature filters which are formally derived from an extension of the analytic signal to multidimensional signals known as the monogenic signal. The interpretation of a multidimensional image as a function graph, i.e. a Riemannian manifold, instead of just intensity variations on the Euclidean space, has important implications that are exploited in this dissertation. Image processing tasks can then be performed by solving partial differential equations on the Riemannian manifold. In this dissertation, Riemannian geometry is used to study the evolution of fronts under mean curvature flow on a Riemannian manifold using a level set framework. For our purposes, the Riemannian manifold is defined by the induced metric of the image that is related to the LST. The Riemannian mean curvature flow is the theoretical basis for the definition of a level set segmentation method. The methods proposed in this dissertation are applied to two medical image applications. The first consists in a freehand 3D ultrasound reconstruction technique that uses the LST to perform an adaptive interpolation based on normalized convolution. Our results show that our method outperforms traditional technique for this interpolation problem. The second application uses the level set method based on Riemannian mean curvature flow to segment anatomical structures in dataset from magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound (US). This novel method reveals as a feasible approach to medical image segmentation.
This book explores mathematics in a wide variety of applications, ranging from problems in electronics, energy and the environment, to mechanics and mechatronics. The book gathers 81 contributions submitted to the 20th European Conference on Mathematics for Industry, ECMI 2018, which was held in Budapest, Hungary in June 2018. The application areas include: Applied Physics, Biology and Medicine, Cybersecurity, Data Science, Economics, Finance and Insurance, Energy, Production Systems, Social Challenges, and Vehicles and Transportation. In turn, the mathematical technologies discussed include: Combinatorial Optimization, Cooperative Games, Delay Differential Equations, Finite Elements, Hamilton-Jacobi Equations, Impulsive Control, Information Theory and Statistics, Inverse Problems, Machine Learning, Point Processes, Reaction-Diffusion Equations, Risk Processes, Scheduling Theory, Semidefinite Programming, Stochastic Approximation, Spatial Processes, System Identification, and Wavelets. The goal of the European Consortium for Mathematics in Industry (ECMI) conference series is to promote interaction between academia and industry, leading to innovations in both fields. These events have attracted leading experts from business, science and academia, and have promoted the application of novel mathematical technologies to industry. They have also encouraged industrial sectors to share challenging problems where mathematicians can provide fresh insights and perspectives. Lastly, the ECMI conferences are one of the main forums in which significant advances in industrial mathematics are presented, bringing together prominent figures from business, science and academia to promote the use of innovative mathematics in industry.
This comprehensive resource provides the latest information on digitization and reconstruction (D&R) of analog signals in digital radios. Readers learn how to conduct comprehensive analysis, concisely describe the major signal processing procedures carried out in the radios, and demonstrate the dependence of these procedures on the quality of D&R. The book presents and analyzes the most promising and theoretically sound ways to improve the characteristics of D&R circuits and illustrate the influence of these improvements on the capabilities of digital radios. The book is intended to bridge the gap that exists between theorists and practical engineers developing D&R techniques by introducing new signal transmission and reception methods that can effectively utilize the unique capabilities offered by novel digitization and reconstruction techniques.
Updating the original, Transforms and Applications Handbook, Third Edition solidifies its place as the complete resource on those mathematical transforms most frequently used by engineers, scientists, and mathematicians. Highlighting the use of transforms and their properties, this latest edition of the bestseller begins with a solid introduction to signals and systems, including properties of the delta function and some classical orthogonal functions. It then goes on to detail different transforms, including lapped, Mellin, wavelet, and Hartley varieties. Written by top experts, each chapter provides numerous examples and applications that clearly demonstrate the unique purpose and properties of each type. The material is presented in a way that makes it easy for readers from different backgrounds to familiarize themselves with the wide range of transform applications. Revisiting transforms previously covered, this book adds information on other important ones, including: Finite Hankel, Legendre, Jacobi, Gengenbauer, Laguerre, and Hermite Fraction Fourier Zak Continuous and discrete Chirp-Fourier Multidimensional discrete unitary Hilbert-Huang Most comparable books cover only a few of the transforms addressed here, making this text by far the most useful for anyone involved in signal processing—including electrical and communication engineers, mathematicians, and any other scientist working in this field.
This book constitutes the refereed proceedings of the International Conference on Computer Vision and Graphics, ICCVG 2018, held in Warsaw, Poland, in September 2018. The 45 full papers were selected from 117 submissions. The contributions are thematically arranged as follows: computer graphics, image quality and graphic, user interfaces, object classification and features, 3D and stereo image processing, low-level and middle-level image processing, medical image analysis, motion analysis and tracking, security and protection, pattern recognition and new concepts in classification.
Energy and power are fundamental concepts in electromagnetism and circuit theory, as well as in optics, signal processing, power engineering, electrical machines, and power electronics. However, in crossing the disciplinary borders, we encounter understanding difficulties due to (1) the many possible mathematical representations of the same physical objects, and (2) the many possible physical interpretations of the same mathematical entities. The monograph proposes a quantum and a relativistic approach to electromagnetic power theory that is based on recent advances in physics and mathematics. The book takes a fresh look at old debates related to the significance of the Poynting theorem and the interpretation of reactive power. Reformulated in the mathematical language of geometric algebra, the new expression of electromagnetic power reflects the laws of conservation of energy-momentum in fields and circuits. The monograph offers a mathematically consistent and a physically coherent interpretation of the power concept and of the mechanism of power transmission at the subatomic (mesoscopic) level. The monograph proves (paraphrasing Heaviside) that there is no finality in the development of a vibrant discipline: power theory.
Contains selected papers from the ISAAC conference 2007 and invited contributions. This book covers various topics that represent the main streams of research in hypercomplex analysis as well as the expository articles. It is suitable for researchers and postgraduate students in various areas of mathematical analysis.
This three-volume set of books presents advances in the development of concepts and techniques in the area of new technologies and contemporary information system architectures. It guides readers through solving specific research and analytical problems to obtain useful knowledge and business value from the data. Each chapter provides an analysis of a specific technical problem, followed by the numerical analysis, simulation and implementation of the solution to the problem. The books constitute the refereed proceedings of the 2017 38th International Conference “Information Systems Architecture and Technology,” or ISAT 2017, held on September 17–19, 2017 in Szklarska Poręba, Poland. The conference was organized by the Computer Science and Management Systems Departments, Faculty of Computer Science and Management, Wroclaw University of Technology, Poland. The papers have been organized into topical parts: Part I— includes discourses on topics including, but not limited to, Artificial Intelligence Methods, Knowledge Discovery and Data Mining, Big Data, Knowledge Discovery and Data Mining, Knowledge Based Management, Internet of Things, Cloud Computing and High Performance Computing, Distributed Computer Systems, Content Delivery Networks, and Service Oriented Computing. Part II—addresses topics including, but not limited to, System Modelling for Control, Recognition and Decision Support, Mathematical Modelling in Computer System Design, Service Oriented Systems and Cloud Computing and Complex Process Modeling. Part III—deals with topics including, but not limited to, Modeling of Manufacturing Processes, Modeling an Investment Decision Process, Management of Innovation, Management of Organization.