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Mathematical Models for Registration and Applications to Medical Imaging
Image registration is an emerging topic in image processing with many applications in medical imaging, picture and movie processing. The classical problem of image registration is concerned with ?nding an appropriate transformation between two data sets. This fuzzy de?nition of registration requires a mathematical modeling and in particular a mathematical speci?cation of the terms appropriate transformations and correlation between data sets. Depending on the type of application, typically Euler, rigid, plastic, elastic deformations are considered. The variety of similarity p measures ranges from a simpleL distance between the pixel values of the data to mutual information or entropy distances. This goal of this book is to highlight by some experts in industry and medicine relevant and emerging image registration applications and to show new emerging mathematical technologies in these areas. Currently, many registration application are solved based on variational prin- ple requiring sophisticated analysis, such as calculus of variations and the theory of partial differential equations, to name but a few. Due to the numerical compl- ity of registration problems ef?cient numerical realization are required. Concepts like multi-level solver for partial differential equations, non-convex optimization, and so on play an important role. Mathematical and numerical issues in the area of registration are discussed by some of the experts in this volume.
Mathematical Modelling for Sustainable Development
Mathematics needs Sustainable Development. When science was gradually reinvented in European medieval societies, it was legitimised as contributing to the disclosure of God’s divine creation. The conflicts that emerged became well known as a result of the clash between Galileo and the Church. Science found a new legitimacy through recognition that it was a powerful force against superstition. In the Enlightenment the argument was pushed forward by attributing Progress to the advancement of science: science could produce a better world by promoting rationality. In our modern society, science has become intimately linked to technology. Science for its own sake unfortunately rarely has positive outcomes in terms of research grant applications. Meanwhile, science and technology, and the progress they are supposed to produce, meet with wide scale scepticism. We all know of the current global problems: climate change, resource depletion, a thinning ozone layer, space debris, declining biodiversity, malnutrition, dying ecosystems, global inequity, and the risk of unprecedented nuclear wars
Mathematical Modeling of Biological Systems ; Vol. II : Epidemiology, Evolution and Ecology,Immunology, Neural Systems and the Brain, and Innovative Mathematical Methods
This two-volume, interdisciplinary work is a unified presentation of a broad range of state-of-the-art topics in the rapidly growing field of mathematical modeling in the biological sciences. Highlighted throughout both works are mathematical and computational approaches to examine central problems in the life sciences, ranging from the organizational principles of individual cells to the dynamics of large populations.
Mathematical Methods for Robust and Nonlinear Control : EPSRC Summer School
The underlying theory on which much modern robust and nonlinear control is based can often be dif?cult for the student to grasp. In particular, the mathematical - pects can be problematic for students from a standard engineering background. The EPSRC sponsored Summer School which was held in Leicester in September 2006 attempted to “?ll the gap” in students’ appreciation the theory relevant to several important areas of control. This book is a collection of lecture notes which were p- sented at that workshop and consists of, broadly, two parts. The ?rst nine chapters are devoted to the theory behind several areas of robust and nonlinear control and are aimed at introducing fundamental concepts to the reader. The last six chapters contain detailed case studies which aim to demonstrate the use and effectiveness of these modern techniques in real engineering applications. It is hoped that this book will provide a useful introduction to many of the more common robust and nonlinear control techniques and serve as a valuable reference for the more adept practitioner.
Mathematical Control Theory and Finance
This book highlights recent developments in mathematical control theory and its applications to finance. It presents a collection of original contributions by distinguished scholars, addressing a large spectrum of problems and techniques. Control theory provides a large set of theoretical and computational tools with applications in a wide range of fields, ranging from "pure" areas of mathematics up to applied sciences like finance. Stochastic optimal control is a well established and important tool of mathematical finance. Other branches of control theory have found comparatively less applications to financial problems, but the exchange of ideas and methods has intensified in recent years. This volume should contribute to establish bridges between these separate fields. The diversity of topics covered as well as the large array of techniques and ideas brought in to obtain the results make this volume a valuable resource for advanced students and researchers.
Mathematical and Computational Models for Congestion Charging
This book presents rigorous treatments of issues related to congestion pricing. The chapters describe recent advances in areas such as mathematical and computational models for predicting traffic congestion, determining when, where, and how much to levy tolls, and analyzing the impact of tolls on transporation systems. The analyses and methodologies developed in this book provide Mechanisms that aid in determining and comparing congestion pricing schemes; Methodologies for evaluating the efficiency of existing and proposed congestion pricing schemes; A means to predict the impact of pricing on urban transporation systems; and Information essential to the financial and political success of congestion pricing programs.
Material Inhomogeneities and their Evolution : A Geometric Approach
The first part of the book deals with the geometrical description of uniform bodies and their homogeneity (i.e., integrability) conditions. In the second part, a theory of material evolution is developed and its relevance in various applied contexts discussed. The necessary geometrical notions are introduced as needed in the first two parts but often without due attention to an uncompromising mathematical rigour. This task is left for the third part of the book, which is a highly technical compendium of those concepts of modern differential geometry that are invoked in the first two parts (differentiable manifolds, Lie groups, jets, principal fibre bundles, G-structures, connections, frame bundles, integrable prolongations, groupoids, etc.).
Markov Chains : Models, Algorithms and Applications
Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This monograph will present a series of Markov models, starting from the basic models and then building up to higher-order models. Included in the higher-order discussions are multivariate models, higher-order multivariate models, and higher-order hidden models. In each case, the focus is on the important kinds of applications that can be made with the class of models being considered in the current chapter. Special attention is given to numerical algorithms that can efficiently solve the models. Therefore, Markov Chains: Models, Algorithms and Applications outlines recent developments of Markov chain models for modeling queueing sequences, Internet, re-manufacturing systems, reverse logistics, inventory systems, bio-informatics, DNA sequences, genetic networks, data mining, and many other practical systems.
Manufacturing Execution Systems - MES
The production plants of today develop into modern service centers. Economic efficiency of modern added value is not a property of products alone but of the process. Decisive potential in business now is a question of process capability, rather than production capability. Process capability in business requires real-time systems for optimization. Business-IT needs to be developed from telecommunications and ERP to real time services, which are not offered by the prevailing ERP systems. Today, only modern Manufacturing Execution Systems (MES) offer real-time applications. They generate current as well as historic mappings of production facilities and thus they can be used as basis for optimizations. It is important to map the supply chain in real time. Increasing complexity in production requires an integrated view of the production and service facilities: detailed scheduling, status collection, quality, performance analysis, tracing of material and so on have to be recorded and displayed in an integrated way.
Managing Weather and Climate Risks in Agriculture
In many parts of the world, weather and climate are one of the biggest production risks and uncertainty factors impacting on agricultural systems performance and management. Both structural and non-structural measures can be used to reduce the impacts of the variability (including extremes) of climate resources on crop production. While the structural measures include strategies such as irrigation, water harvesting, windbreaks etc., the non-structural measures include use of seasonal to interannual climate forecasts, improved application of medium-range weather forecasts and crop insurance. This book based on an International Workshop held in New Delhi, India should be of interest to all organizations and agencies interested in improved risk management in agriculture.
Managing Complexity : Insights, Concepts, Applications
Each chapter in Managing Complexity focuses on analyzing real-world complex systems and transferring knowledge from the complex-systems sciences to applications in business, industry and society. The interdisciplinary contributions range from markets and production through logistics, traffic control, and critical infrastructures, up to network design, information systems, social conflicts and building consensus. They serve to raise readers' awareness concerning the often counter-intuitive behavior of complex systems and to help them integrate insights gained in complexity research into everyday planning, decision making, strategic optimization, and policy.
Malliavin Calculus for Lévy Processes with Applications to Finance
While the original works on Malliavin calculus aimed to study the smoothness of densities of solutions to stochastic differential equations, this book has another goal. It portrays the most important and innovative applications in stochastic control and finance, such as hedging in complete and incomplete markets, optimisation in the presence of asymmetric information and also pricing and sensitivity analysis. In a self-contained fashion, both the Malliavin calculus with respect to Brownian motion and general Lévy type of noise are treated. Besides, forward integration is included and indeed extended to general Lévy processes. The forward integration is a recent development within anticipative stochastic calculus that, together with the Malliavin calculus, provides new methods for the study of insider trading problems.
Machining: Fundamentals and Recent Advances
Machining is one of the most important manufacturing processes. Parts manufactured by others processes often require further operations before the product is ready for application. Machining is the broad term used to describe the removal of material from a work-piece. Machining processes can be applied to work metallic and non-metallic materials such as polymers, wood, ceramics and composites.
Low-Power High-Level Synthesis for Nanoscale CMOS Circuits
Low-Power High-Level Synthesis for Nanoscale CMOS Circuits addresses the need for analysis, characterization, estimation, and optimization of the various forms of power dissipation in the presence of process variations of nano-CMOS technologies. The authors show very large-scale integration (VLSI) researchers and engineers how to minimize the different types of power consumption of digital circuits.
Logic Synthesis for Compositional Microprogram Control Units
In this book control algorithms are represented by the linear graph-schemes of algorithms (GSA), where the number of operator vertices is not less than 75% of the total number of all algorithm vertices. A special class of control units named as compositional microprogram control units (CMCU) is proposed as the best way for interpretation of linear control algorithms.
Linkage in Evolutionary Computation
The whole volume consisting of 19 chapters is divided into 3 parts: Models and Theories; Operators and Frameworks; Applications. This edited volume will serve as a useful guide and reference for researchers who are currently working in the area of linkage. For postgraduate research students, this volume will serve as a good source of reference. It is also suitable as a text for a graduate level course focusing on linkage issues.
Linear Programming : Foundations and Extensions
Linear Programming: Foundations and Extensions is an introduction to the field of optimization. The book emphasizes constrained optimization, beginning with a substantial treatment of linear programming, and proceeding to convex analysis, network flows, integer programming, quadratic programming, and convex optimization. The book is carefully written. Specific examples and concrete algorithms precede more abstract topics. Topics are clearly developed with a large number of numerical examples worked out in detail.
Linear Optimization Problems with Inexact Data
Linear programming attracted the interest of mathematicians during and after World War II when the first computers were constructed and methods for solving large linear programming problems were sought in connection with specific practical problems—for example, providing logistical support for the U.S. Armed Forces or modeling national economies. Early attempts to apply linear programming methods to solve practical problems failed to satisfy expectations. There were various reasons for the failure. One of them, which is the central topic of this book, was the inexactness of the data used to create the models. This phenomenon, inherent in most pratical problems, has been dealt with in several ways. At first, linear programming models used "average” values of inherently vague coefficients, but the optimal solutions of these models were not always optimal for the original problem itself. Later researchers developed the stochastic linear programming approach, but this too has its limitations. Recently, interest has been given to linear programming problems with data given as intervals, convex sets and/or fuzzy sets. The individual results of these studies have been promising, but the literature has not presented a unified theory. Linear Optimization Problems with Inexact Data attempts to present a comprehensive treatment of linear optimization with inexact data, summarizing existing results and presenting new ones within a unifying framework.
Linear Models for Optimal Test Design
Begins with a reflection on the history of test design--the core activity of all educational and psychological testing. It then presents a standard language for modeling test design problems as instances of multi-objective constrained optimization. The main portion of the book discusses test design models for a large variety of problems from the daily practice of testing, and illustrates their use with the help of numerous empirical examples. The presentation includes models for the assembly of tests to an absolute or relative target for their information functions, classical test assembly, test equating problems, item matching, test splitting, simultaneous assembly of multiple tests, tests with item sets, multidimensional tests, and adaptive test assembly. Two separate chapters are devoted to the questions of how to design item banks for optimal support of programs with fixed and adaptive tests. Linear Models for Optimal Test Design, which does not require any specific mathematical background, has been written to be a helpful resource on the desk of any test specialist.
Linear Models and Generalizations : Least Squares and Alternatives
Gives an up-to-date account of the theory and applications of linear models. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for courses in other areas. Some of the highlights in this book are as follows. A relatively extensive chapter on matrix theory (Appendix A) provides the necessary tools for proving theorems discussed in the text and offers a selection of classical and modern algebraic results that are useful in research work in econometrics, engineering, and optimization theory. The matrix theory of the last ten years has produced a series of fundamental results aboutthe de?niteness ofmatrices,especially forthe di?erences ofmatrices, which enable superiority comparisons of two biased estimates to be made for the ?rst time. We have attempted to provide a uni?ed theory of inference from linear models with minimal assumptions
