This textbook presents the application of mathematical methods and theorems tosolve engineering problems, rather than focusing on mathematical proofs. Applications of Vector Analysis and Complex Variables in Engineering explains the mathematical principles in a manner suitable for engineering students, who generally think quite differently than students of mathematics. The objective is to emphasize mathematical methods and applications, rather than emphasizing general theorems and principles, for which the reader is referred to the literature. Vector analysis plays an important role in engineering, and is presented in terms of indicial notation, making use of the Einstein summation convention. This text differs from most texts in that symbolic vector notation is completely avoided, as suggested in the textbooks on tensor algebra and analysis written in German by Duschek and Hochreiner, in the 1960s. The defining properties of vector fields, the divergence and curl, are introduced in terms of fluid mechanics. The integral theorems of Gauss (the divergence theorem), Stokes, and Green are introduced also in the context of fluid mechanics. The final application of vector analysis consists of the introduction of non-Cartesian coordinate systems with straight axes, the formal definition of vectors and tensors. The stress and strain tensors are defined as an application. Partial differential equations of the first and second order are discussed. Two-dimensional linear partial differential equations of the second order are covered, emphasizing the three types of equation: hyperbolic, parabolic, and elliptic. The hyperbolic partial differential equations have two real characteristic directions, and writing the equations along these directions simplifies the solution process. The parabolic partial differential equations have two coinciding characteristics; this gives useful information regarding the character of the equation, but does not help in solving problems. The elliptic partial differential equations do not have real characteristics. In contrast to most texts, rather than abandoning the idea of using characteristics, here the complex characteristics are determined, and the differential equations are written along these characteristics. This leads to a generalized complex variable system, introduced by Wirtinger. The vector field is written in terms of a complex velocity, and the divergence and the curl of the vector field is written in complex form, reducing both equations to a single one. Complex variable methods are applied to elliptical problems in fluid mechanics, and linear elasticity. The techniques presented for solving parabolic problems are the Laplace transform and separation of variables, illustrated for problems of heat flow and soil mechanics. Hyperbolic problems of vibrating strings and bars, governed by the wave equation are solved by the method of characteristics as well as by Laplace transform. The method of characteristics for quasi-linear hyperbolic partial differential equations is illustrated for the case of a failing granular material, such as sand, underneath a strip footing. The Navier Stokes equations are derived and discussed in the final chapter as an illustration of a highly non-linear set of partial differential equations and the solutions are interpreted by illustrating the role of rotation (curl) in energy transfer of a fluid.

This volume comprises the latest developments in both fundamental science and patient-specific applications, discussing topics such as: cellular mechanics, injury biomechanics, biomechanics of the heart and vascular system, algorithms of computational biomechanics for medical image analysis, and both patient-specific fluid dynamics and solid mechanics simulations. With contributions from researchers world-wide, Computational Biomechanics for Medicine: Measurments, Models, and Predictions provides an opportunity for specialists in the field to present their latest methodologies and advancements.

This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible. It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory. Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines. From that, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results.

Most texts on computational methods are borne out of research activities at postgraduate study programs, and this is no exception. After being introduced to the boundary element method (BEM) (then referred to as the boundary integral equation method (BIEM)) in 1981 by Prof. Jim Liggett of Cornell University, a number of graduate students and myself under his supervision took active interest in the development of the theory and its application to a wide range of engineering problems. We certainly achieved some amount of success. A personal desire to have a deeper understanding and appreciation of computational methods prompted one to take related courses in fmite deference method, and to undertake a self-instructed study of variational and fmite element methods. These exposures were not only quite instructive but fruitful, and may have provided the motivation for the current research on the Green element method (GEM) - a name coined by Prof. Liggett in 1987 during my visit as Professor to the School of Civil & Environmental Engineering, Cornell University. The main objectives of this text are to serve as an instructional material to senior undergraduate and first year graduate students undertaking a course in computational methods, and as a resource material for research scientists, applied mathematicians, numerical analysts, and engineers who may wish to take these ideas to other frontiers and applications.

Discover the benefits of applying algorithms to solve scientific, engineering, and practical problems
Providing a combination of theory, algorithms, and simulations, Handbook of Applied Algorithms presents an all-encompassing treatment of applying algorithms and discrete mathematics to practical problems in "hot" application areas, such as computational biology, computational chemistry, wireless networks, and computer vision.
In eighteen self-contained chapters, this timely book explores:
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Localized algorithms that can be used in topology control for wireless ad-hoc or sensor networks
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Bioinformatics algorithms for analyzing data
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Clustering algorithms and identification of association rules in data mining
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Applications of combinatorial algorithms and graph theory in chemistry and molecular biology
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Optimizing the frequency planning of a GSM network using evolutionary algorithms
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Algorithmic solutions and advances achieved through game theory
Complete with exercises for readers to measure their comprehension of the material presented, Handbook of Applied Algorithms is a much-needed resource for researchers, practitioners, and students within computer science, life science, and engineering.
Amiya Nayak, PhD, has over seventeen years of industrial experience and is Full Professor at the School of Information Technology and Engineering at the University of Ottawa, Canada. He is on the editorial board of several journals. Dr. Nayak's research interests are in the areas of fault tolerance, distributed systems/algorithms, and mobile ad-hoc networks. Ivan StojmenoviC?, PhD, is Professor at the University of Ottawa, Canada (www.site.uottawa.ca/ ivan), and Chair Professor of Applied Computing at the University of Birmingham, United Kingdom. Dr. Stojmenovic? received the Royal Society Wolfson Research Merit Award. His current research interests are mostly in the design and analysis of algorithms for wireless ad-hoc and sensor networks.

This book highlights the theory and practical applications of the chemical master equation (CME) approach for very large biochemical networks, which provides a powerful general framework for model building in a variety of biological networks. The aim of the book is to not only highlight advanced numerical solution methods for the CME, but also reveal their potential by means of practical examples. The case studies presented are mainly from biology; however, the applications from novel methods are discussed comprehensively, underlining the interdisciplinary approach in simulation and the potential of the chemical master equation approach for modelling bionetworks. The book is a valuable guide for researchers, graduate students, and professionals alike.

This book presents the multi-criteria approach to decision support, as well as the various multi-criteria tools to help avoid multi-objective optimization. The book is intended as a tool for understanding the multi-criteria tools for decision support and modeling in mathematical programming. It helps to structure models, to easily model complex constraints, to have a basic modeling guide for any multi-criteria system and to better understand models already existing in the literature. The book is structured in the same order as components of the methodology, established in a multi-criteria optimization problem. It introduces the elements of the actors, the decision-making activity under criteria, calculations, specifications and objective criterion.

Nonlinear dynamical systems play an important role in a number of disciplines. The physical, biological, economic and even sociological worlds are comprised of com plex nonlinear systems that cannot be broken down into the behavior of their con stituents and then reassembled to form the whole. The lack of a superposition principle in such systems has challenged researchers to use a variety of analytic and numerical methods in attempts to understand the interesting nonlinear interactions that occur in the World around us. General relativity is a nonlinear dynamical theory par excellence. Only recently has the nonlinear evolution of the gravitational field described by the theory been tackled through the use of methods used in other disciplines to study the importance of time dependent nonlinearities. The complexity of the equations of general relativity has been (and still remains) a major hurdle in the formulation of concrete mathematical concepts. In the past the imposition of a high degree of symmetry has allowed the construction of exact solutions to the Einstein equations. However, most of those solutions are nonphysical and of those that do have a physical significance, many are often highly idealized or time independent."

Fundamentals of Artificial Intelligence introduces the foundations of present day AI and provides coverage to recent developments in AI such as Constraint Satisfaction Problems, Adversarial Search and Game Theory, Statistical Learning Theory, Automated Planning, Intelligent Agents, Information Retrieval, Natural Language & Speech Processing, and Machine Vision. The book features a wealth of examples and illustrations, and practical approaches along with the theoretical concepts. It covers all major areas of AI in the domain of recent developments. The book is intended primarily for students who major in computer science at undergraduate and graduate level but will also be of interest as a foundation to researchers in the area of AI.

This is an introduction to the mathematical basis of finite element analysis as applied to vibrating systems. Finite element analysis is a technique that is very important in modeling the response of structures to dynamic loads. Although this book assumes no previous knowledge of finite element methods, those who do have knowledge will still find the book to be useful. It can be utilised by aeronautical, civil, mechanical, and structural engineers as well as naval architects. This second edition includes information on the many developments that have taken place over the last twenty years. Existing chapters have been expanded where necessary, and three new chapters have been included that discuss the vibration of shells and multi-layered elements and provide an introduction to the hierarchical finite element method.

This book presents a collection of papers on recent advances in problems concerning dynamics, optimal control and optimization. In many chapters, computational techniques play a central role. Set-oriented techniques feature prominently throughout the book, yielding state-of-the-art algorithms for computing general invariant sets, constructing globally optimal controllers and solving multi-objective optimization problems.

HIS book presents a generalized state-space theory for the analysis T and synthesis of finite horizon suboptimal Hoo controllers. We de rive expressions for a suboptimal controller in a general setting and propose an approximate solution to the Hoo performance robustness problem. The material in the book is taken from a collection of research papers written by the author. The book is organized as follows. Chapter 1 treats nonlinear optimal control problems in which the cost functional is of the form of a quotient or a product of powers of definite integrals. The problems considered in Chap ter 1 are very general, and the results are useful for the computation of the actual performance of an Hoo suboptimal controller. Such an application is given in Chapters 4 and 5. Chapter 2 gives a criterion for the evaluation of the infimal Hoc norm in the finite horizon case. Also, a differential equation is derived for the achievable performance as the final time is varied. A general suboptimal control problem is then posed, and an expression for a subopti mal Hoo state feedback controller is derived. Chapter 3 develops expressions for a suboptimal Hoo output feedback controller in a very general case via the solution of two dynamic Riccati equations. Assuming the adequacy of linear expressions, Chapter 4 gives an iterative procedure for the synthesis of a suboptimal Hoo controller that yields the required performance even under parameter variations."

With the diversification of Internet services and the increase in mobile users, efficient management of network resources has become an extremely important issue in the field of wireless communication networks (WCNs). Adaptive resource management is an effective tool for improving the economic efficiency of WCN systems as well as network design and construction, especially in view of the surge in mobile device demands. This book presents modelling methods based on queueing theory and Markov processes for a wide variety of WCN systems, as well as precise and approximate analytical solution methods for the numerical evaluation of the system performance. This is the first book to provide an overview of the numerical analyses that can be gleaned by applying queueing theory, traffic theory and other analytical methods to various WCN systems. It also discusses the recent advances in the resource management of WCNs, such as broadband wireless access networks, cognitive radio networks, and green cloud computing. It assumes a basic understanding of computer networks and queueing theory, and familiarity with stochastic processes is also recommended. The analysis methods presented in this book are useful for first-year-graduate or senior computer science and communication engineering students. Providing information on network design and management, performance evaluation, queueing theory, game theory, intelligent optimization, and operations research for researchers and engineers, the book is also a valuable reference resource for students, analysts, managers and anyone in the industry interested in WCN system modelling, performance analysis and numerical evaluation.

Dr. Jay Liebowitz Orkand Endowed Chair in Management and Technology University of Maryland University College Graduate School of Management & Technology 3501 University Boulevard East Adelphi, Maryland 20783-8030 USA jliebowitz@umuc. edu When I first heard the general topic of this book, Marketing Intelligent Systems or what I'll refer to as Marketing Intelligence, it sounded quite intriguing. Certainly, the marketing field is laden with numeric and symbolic data, ripe for various types of mining-data, text, multimedia, and web mining. It's an open laboratory for applying numerous forms of intelligentsia-neural networks, data mining, expert systems, intelligent agents, genetic algorithms, support vector machines, hidden Markov models, fuzzy logic, hybrid intelligent systems, and other techniques. I always felt that the marketing and finance domains are wonderful application areas for intelligent systems, and this book demonstrates the synergy between marketing and intelligent systems, especially soft computing. Interactive advertising is a complementary field to marketing where intelligent systems can play a role. I had the pleasure of working on a summer faculty f- lowship with R/GA in New York City-they have been ranked as the top inter- tive advertising agency worldwide. I quickly learned that interactive advertising also takes advantage of data visualization and intelligent systems technologies to help inform the Chief Marketing Officer of various companies. Having improved ways to present information for strategic decision making through use of these technologies is a great benefit.

This monograph, co-authored by three longtime collaborators, aims to promote the interdisciplinary field of mathematical biology by providing accessible new approaches to study natural systems. As there is currently scarce literature on the applications of mathematical modelling for biology research, this book presents a new way of studying interactions at the level of populations, societies, ecosystems, and biomes through open-sourced modeling platforms. It offers an interdisciplinary approach to analyzing natural phenomena-for example, by showing how master equations developed to describe electrical circuits can also describe biological systems mathematically. Ultimately it promotes a method of study based on modelling and mathematical principles, facilitating collaboration between mathematicians, biologists, engineers, and other researchers to enrich knowledge of the world's ecosystems.

This book studies electron resonant tunneling in two- and three-dimensional quantum waveguides of variable cross-sections in the time-independent approach. Mathematical models are suggested for the resonant tunneling and develop asymptotic and numerical approaches for investigating the models. Also, schemes are presented for several electronics devices based on the phenomenon of resonant tunneling. Compared to its first edition, this book includes four new chapters, redistributes the content between chapters and modifies the estimates of the remainders in the asymptotics of resonant tunneling characteristics. The book is addressed to mathematicians, physicists, and engineers interested in waveguide theory and its applications in electronics.

This book offers comprehensive coverage of all the mathematical tools needed by engineers in the field of processing and transport of all forms of information, data and images - as well as many other engineering disciplines. It provides essential theories, equations and results in probability theory and statistics, which constitute the basis for the presentation of signal processing, information theory, traffic and queueing theory, and reliability. The mathematical foundations of simulation are also covered.

The book's accessible style will enable students, engineers and researches new to this area to advance their knowledge of communication and other engineering technologies; however, it will also serve as a useful reference guide to anyone wishing to further explore this field.

Information and dynamics are key terms in many contemporary directions of research in numerous fields. Basic frarneworks in this regard are information theory and the theory of dynamical systems. The origin of both areas essentially dates back into the 20s of this century. An excellent presentation of the history of information theory from its early days (e.g, Nyquist, Hartley) until to the 70s has been given by J.R. Pierce et al. in the IEEE Transactions on Information Theory IT-19 (1973). System theory, the other branch mentioned above, also started with first publications in the 20s (e.g., Kohler, Lotka). A historical sketch can be found in 1. v. Bertalanffy's General Systems Theory (Braziller, Ncw York, 1968), the main body ofwhich presents a proper introduction into the main concepts and applications. At present, both branches, information theory and system theory, are hardly sepa- rable any more. Aspects of both appcar in cybernetics, contral theory, communication theory, computer science, game theory, cognitive science, and related fields. Combin- ing knowledge of various individual scientific disciplines, they are particularly useful to deal with problems of basically intcrdisciplinary character.

After reading this book, students should be able to analyze computational problems in linear algebra such as linear systems, least squares- and eigenvalue problems, and to develop their own algorithms for solving them. Since these problems can be large and difficult to handle, much can be gained by understanding and taking advantage of special structures. This in turn requires a good grasp of basic numerical linear algebra and matrix factorizations. Factoring a matrix into a product of simpler matrices is a crucial tool in numerical linear algebra, because it allows us to tackle complex problems by solving a sequence of easier ones. The main characteristics of this book are as follows: It is self-contained, only assuming that readers have completed first-year calculus and an introductory course on linear algebra, and that they have some experience with solving mathematical problems on a computer. The book provides detailed proofs of virtually all results. Further, its respective parts can be used independently, making it suitable for self-study. The book consists of 15 chapters, divided into five thematically oriented parts. The chapters are designed for a one-week-per-chapter, one-semester course. To facilitate self-study, an introductory chapter includes a brief review of linear algebra.

This textbook introduces students to mass and energy balances and focuses on basic principles for calculation, design, and optimization as they are applied in industrial processes and equipment. While written primarily for undergraduate programs in chemical, energy, mechanical, and environmental engineering, the book can also be used as a reference by technical staff and design engineers interested who are in, and/or need to have basic knowledge of process engineering calculation. Concepts and techniques presented in this volume are highly relevant within many industrial sectors including manufacturing, oil/gas, green and sustainable energy, and power plant design. Drawing on 15 years of teaching experiences, and with a clear understanding of students' interests, the authors have adopted a very accessible writing style that includes many examples and additional citations to research resources from the literature, referenced at the ends of chapters.

This hands-on textbook/reference presents an introduction to the fundamental aspects of modelling and simulation, both for those wishing to learn about this methodology and also for those who have a need to apply it in their work. The text is supported by illustrative examples, drawn from projects formulated within the domains of discrete-event dynamic systems (DEDS) and continuous-time dynamic systems (CTDS). This updated new edition has been enhanced with new illustrative case studies, and additional examples demonstrating some new features and the effectiveness of the ABCmod conceptual modelling framework. Changes that facilitate the development of simulation models with ABSmod/J are illustrated. New material includes a presentation of the experimentation strategy called "design of experiments" and three new chapters that explore the optimization-simulation interface. Topics and features: presents a goal-based and project-oriented perspective of modelling and simulation; describes the ABCmod framework, an activity-based conceptual modelling framework for DEDS; examines the simulation-optimization interface in both the CTDS and DEDS domains; provides numerous illustrative examples, case studies and useful algorithms, as well as exercises and projects at the end of most chapters; includes appendices on probability and statistics, the GPSS programming environment, and relevant MATLAB features; provides supplementary software and teaching support material at an associated website, including lecture slides and a methodology for organizing student projects. Serving as an essential guide to the foundations of modelling and simulation, this practical primer is ideal for senior undergraduate and junior graduate-level students. Also suitable for self-study, the book will be of great benefit to professionals seeking insight into the vast potential of this rapidly evolving problem-solving paradigm.

Overview Scilab is a scientific software package that provides a powerful open com- puting environment for engineering and scientific applications. Distributed freely via the Internet since 1994, Scilab is currently being used in educa- tional and industrial environments around the world. This book contains all the information needed to master Scilab: how to use it interactively as a super calculator, how to write programs, how to de- velop complex applications, and more. The authors, Carey Bunks (BBN1), Jean-Philippe Chancelier (ENPC2), Fran~ois Delebecque, Claude Gomez, 3 Maurice Goursat, Ramine Nikoukhah, and Serge Steer (INRlA ), have not only been involved in the development of Scilab, but have used it for teach- ing and industrial applications for many years. A CD-ROM, containing the entire Scilab soUrce code as well as a set of precompiled binary executables for a variety of computing platforms, is included with this book. The objective here is to give a thorough description of Scilab's use, in- cluding how to master its environment and programming language, the use of the integrated graphics, the incorporation of user-provided func- tions, and a tour of the numerous application toolboxes. The purpose is to provide students and professionals with an introduction to Scilab and its use in engineering and scientific problem solving. The numerous practical examples serve as a framework that can be used as a basis for developing other applications.

Optimization methods have been considered in many articles, monographs, and handbooks. However, experts continue to experience difficulties in correctly stating optimization problems in engineering. These troubles typically emerge when trying to define the set of feasible solutions, i.e. the constraints imposed on the design variables, functional relationships, and criteria. The Parameter Space Investigation (PSI) method was developed specifically for the correct statement and solution of engineering optimization problems. It is implemented in the MOVI 1.0 software package, a tutorial version of which is included in this book. The PSI method and MOVI 1.0 software package have a wide range of applications. The PSI method can be successfully used for the statement and solution of the following multicriteria problems: design, identification, design with control, the optional development of prototypes, finite element models, and the decomposition and aggregation of large-scale systems.

Audience: The PSI method will be of interest to researchers, graduate students, and engineers who work in engineering, mathematical modelling and industrial mathematics, and in computer and information science.

This book uses Ludwig Wittgenstein's philosophical methodology to solve a problem that has perplexed thinkers for thousands of years: 'how come (abstract) mathematics applies so wonderfully well to the (concrete, physical) world?' The book is distinctive in several ways. First, it gives the reader a route into understanding important features of Wittgenstein's writings and lectures by using his methodology to tackle this long-standing and seemingly intractable philosophical problem. More than this, though, it offers an outline of important (sometimes little-known) aspects of the development of mathematical thought through the ages, and an engagement of Wittgenstein's philosophy with this and with contemporary philosophy of mathematics on its own terms. A clear overview of all this in the context of Wittgenstein's philosophy of mathematics is interesting in its own right; it is also just what is needed to solve the problem of mathematics and world.

Discrete-Time and Discrete-Space Dynamical Systems provides a systematic characterization of the similarities and differences of several types of discrete-time and discrete-space dynamical systems, including: Boolean control networks; nondeterministic finite-transition systems; finite automata; labelled Petri nets; and cellular automata. The book's perspective is primarily based on topological properties though it also employs semitensor-product and graph-theoretic methods where appropriate. It presents a series of fundamental results: invertibility, observability, detectability, reversiblity, etc., with applications to systems biology. Academic researchers with backgrounds in applied mathematics, engineering or computer science and practising engineers working with discrete-time and discrete-space systems will find this book a helpful source of new understanding for this increasingly important class of systems. The basic results to be found within are of fundamental importance for further study of related problems such as automated synthesis and safety control in cyber-physical systems using formal methods.