"Introduction to Embedded System Design Using Field Programmable Gate Arrays" provides a starting point for the use of field programmable gate arrays in the design of embedded systems. The text considers a hypothetical robot controller as an embedded application and weaves around it related concepts of FPGA-based digital design. The book details: use of FPGA vis-à-vis general purpose processor and microcontroller; design using Verilog hardware description language; digital design synthesis using Verilog and Xilinx® SpartanTM 3 FPGA; FPGA-based embedded processors and peripherals; overview of serial data communications and signal conditioning using FPGA; FPGA-based motor drive controllers; and prototyping digital systems using FPGA.

Books on computation in the marketplace tend to discuss the topics within specific fields. Many computational algorithms, however, share common roots. Great advantages emerge if numerical methodologies break the boundaries and find their uses across disciplines. Interdisciplinary Computing In Java Programming Language introduces readers of different backgrounds to the beauty of the selected algorithms. Serious quantitative researchers, writing customized codes for computation, enjoy cracking source codes as opposed to the black-box approach. Most C and Fortran programs, despite being slightly faster in program execution, lack built-in support for plotting and graphical user interface. This book selects Java as the platform where source codes are developed and applications are run, helping readers/users best appreciate the fun of computation.

On May 1, 2004, the world of theoretical computer science su?ered a stunning loss: Shimon Even passed away. Few computer scientists have had as long, s- tained, and in?uential a career as Shimon. Shimon Even was born in Tel-Aviv in 1935. He received a B.Sc. in Elect- cal Engineering from the Technion in 1959, an M.A. in Mathematics from the University of Northern Carolina in 1961, and a Ph.D. in Applied Mathematics from Harvard University in 1963. He held positions at the Technion (1964–67 and 1974–2003), Harvard University (1967–69), the Weizmann Institute (1969– 74), and the Tel-Aviv Academic College (2003-04). He visited many universities and research institutes, including Bell Laboratories, Boston University, Cornell, Duke, Lucent Technologies, MIT, Paderborn, Stanford, UC-Berkeley, USC and UT-Dallas. Shimon Even played a major role in establishing computer science education in Israel and led the development of academic programs in two major insti- tions: the Weizmann Institute and the Technion. In 1969 he established at the Weizmann the ?rst computer science education program in Israel, and led this program for ?ve years. In 1974 he joined the newly formed computer science department at the Technion and shaped its academic development for several decades. These two academic programs turned out to have a lasting impact on the evolution of computer science in Israel.

It is true that there exist many books dedicated to linear algebra and some­ what fewer to multilinear algebra, written in several languages, and perhaps one can think that no more books are needed. However, it is also true that in algebra many new results are continuously appearing, different points of view can be used to see the mathematical objects and their associated structures, and different orientations can be selected to present the material, and all of them deserve publication. Under the leadership of Juan Ramon Ruiz-Tolosa, Professor of multilin­ ear algebra, and the collaboration of Enrique Castillo, Professor of applied mathematics, both teaching at an engineering school in Santander, a tensor textbook has been born, written from a practical point of view and free from the esoteric language typical of treatises written by algebraists, who are not interested in descending to numerical details. The balance between follow­ ing this line and keeping the rigor of classical theoretical treatises has been maintained throughout this book. The book assumes a certain knowledge of linear algebra, and is intended as a textbook for graduate and postgraduate students and also as a consultation book. It is addressed to mathematicians, physicists, engineers, and applied scientists with a practical orientation who are looking for powerful tensor tools to solve their problems.

Our goal is to develop automated methods for the segmentation of thr- dimensional biomedical images. Here, we describe the segmentation of c- focal microscopy images of bee brains (20 individuals) by registration to one or several atlas images. Registration is performed by a highly parallel imp- mentation of an entropy-based nonrigid registration algorithm using B-spline transformations. We present and evaluate different methods to solve the cor- spondence problem in atlas based registration. An image can be segmented by registering it to an individual atlas, an average atlas, or multiple atlases. When registering to multiple atlases, combining the individual segmentations into a ?nalsegmentationcanbeachievedbyatlasselection,ormulticlassi?erdecision fusion. Wedescribeallthesemethodsandevaluatethesegmentationaccuracies that they achieve by performing experiments with electronic phantoms as well as by comparing their outputs to a manual gold standard. The present work is focused on the mathematical and computational t- ory behind a technique for deformable image registration termed Hyperelastic Warping, and demonstration of the technique via applications in image regist- tion and strain measurement. The approach combines well-established prin- ples of nonlinear continuum mechanics with forces derived directly from thr- dimensional image data to achieve registration. The general approach does not require the de?nition of landmarks, ?ducials, or surfaces, although it can - commodate these if available. Representative problems demonstrate the robust and ?exible nature of the approach. Three-dimensional registration methods are introduced for registering MRI volumes of the pelvis and prostate. The chapter ?rst reviews the applications, xi xii Preface challenges,and previous methods of image registration in the prostate.

In the last few decades, multiscale algorithms have become a dominant trend in large-scale scientific computation. Researchers have successfully applied these methods to a wide range of simulation and optimization problems. This book gives a general overview of multiscale algorithms; applications to general combinatorial optimization problems such as graph partitioning and the traveling salesman problem; and VLSICAD applications, including circuit partitioning, placement, and VLSI routing. Additional chapters discuss optimization in reconfigurable computing, convergence in multilevel optimization, and model problems with PDE constraints.

The history of computing has its origins at the outset of civilization. As towns and communities evolved there was a need for increasingly sophisticated calculations. This book traces the evolution of computation, from early civilisations 3000 B.C. to the latest key developments in modern times.
This useful and lively text provides a comprehensive introduction to the key topics in the history of computing, in an easy-to-follow and concise manner. It covers the significant areas and events in the field - from the ancient Egyptians through to the present day - and both gives the reader a flavour of the history and stimulates further study in the subject.

This volume presents an exhaustive treatment of computation and algorithms for finite fields.
Topics covered include polynomial factorization, finding irreducible and primitive polynomials, distribution of these primitive polynomials and of primitive points on elliptic curves, constructing bases of various types, and new applications of finite fields to other araes of mathematics. For completeness, also included are two special chapters on some recent advances and applications of the theory of congruences (optimal coefficients, congruential pseudo-random number generators, modular arithmetic etc.), and computational number theory (primality testing, factoring integers, computing in algebraic number theory, etc.) The problems considered here have many applications in computer science, coding theory, cryptography, number theory and discrete mathematics.
The level of discussion presuppose only a knowledge of the basic facts on finite fields, and the book can be recommended as supplementary graduate text.
For researchers and students interested in computational and algorithmic problems in finite fields.

This book constitutes the refereed proceedings of the Third International Workshop on Ant Algorithms, ANTS 2002, held in Brussels, Belgium in September 2002. The 17 revised full papers, 11 short papers, and extended poster abstracts presented were carefully reviewed and selected from 52 submissions. The papers deal with theoretical and foundational aspects and a variety of new variants of ant algorithms as well as with a broad variety of optimization applications in networking and operations research. All in all, this book presents the state of the art in research and development in the emerging field of ant algorithms

During the last few years, we have seen quite spectacular progress in the area of approximation algorithms: for several fundamental optimization problems we now actually know matching upper and lower bounds for their approximability. This textbook-like tutorial is a coherent and essentially self-contained presentation of the enormous recent progress facilitated by the interplay between the theory of probabilistically checkable proofs and aproximation algorithms. The basic concepts, methods, and results are presented in a unified way to provide a smooth introduction for newcomers. These lectures are particularly useful for advanced courses or reading groups on the topic.

People are facing more and more NP-complete or NP-hard problems of a combinatorial nature and of a continuous nature in economic, military and management practice. There are two ways in which one can enhance the efficiency of searching for the solutions of these problems. The first is to improve the speed and memory capacity of hardware. We all have witnessed the computer industry's amazing achievements with hardware and software developments over the last twenty years. On one hand many computers, bought only a few years ago, are being sent to elementary schools for children to learn the ABC's of computing. On the other hand, with economic, scientific and military developments, it seems that the increase of intricacy and the size of newly arising problems have no end. We all realize then that the second way, to design good algorithms, will definitely compensate for the hardware limitations in the case of complicated problems. It is the collective and parallel computation property of artificial neural net­ works that has activated the enthusiasm of researchers in the field of computer science and applied mathematics. It is hard to say that artificial neural networks are solvers of the above-mentioned dilemma, but at least they throw some new light on the difficulties we face. We not only anticipate that there will be neural computers with intelligence but we also believe that the research results of artificial neural networks might lead to new algorithms on von Neumann's computers.

The quadratic assignment problem (QAP) was introduced in 1957 by Koopmans and Beckmann to model a plant location problem. Since then the QAP has been object of numerous investigations by mathematicians, computers scientists, ope- tions researchers and practitioners. Nowadays the QAP is widely considered as a classical combinatorial optimization problem which is (still) attractive from many points of view. In our opinion there are at last three main reasons which make the QAP a popular problem in combinatorial optimization. First, the number of re- life problems which are mathematically modeled by QAPs has been continuously increasing and the variety of the fields they belong to is astonishing. To recall just a restricted number among the applications of the QAP let us mention placement problems, scheduling, manufacturing, VLSI design, statistical data analysis, and parallel and distributed computing. Secondly, a number of other well known c- binatorial optimization problems can be formulated as QAPs. Typical examples are the traveling salesman problem and a large number of optimization problems in graphs such as the maximum clique problem, the graph partitioning problem and the minimum feedback arc set problem. Finally, from a computational point of view the QAP is a very difficult problem. The QAP is not only NP-hard and - hard to approximate, but it is also practically intractable: it is generally considered as impossible to solve (to optimality) QAP instances of size larger than 20 within reasonable time limits.

The algorithmic solution of problems has always been one of the major concerns of mathematics. For a long time such solutions were based on an intuitive notion of algorithm. It is only in this century that metamathematical problems have led to the intensive search for a precise and sufficiently general formalization of the notions of computability and algorithm. In the 1930s, a number of quite different concepts for this purpose were pro­ posed, such as Turing machines, WHILE-programs, recursive functions, Markov algorithms, and Thue systems. All these concepts turned out to be equivalent, a fact summarized in Church's thesis, which says that the resulting definitions form an adequate formalization of the intuitive notion of computability. This had and continues to have an enormous effect. First of all, with these notions it has been possible to prove that various problems are algorithmically unsolvable. Among of group these undecidable problems are the halting problem, the word problem theory, the Post correspondence problem, and Hilbert's tenth problem. Secondly, concepts like Turing machines and WHILE-programs had a strong influence on the development of the first computers and programming languages. In the era of digital computers, the question of finding efficient solutions to algorithmically solvable problems has become increasingly important. In addition, the fact that some problems can be solved very efficiently, while others seem to defy all attempts to find an efficient solution, has called for a deeper under­ standing of the intrinsic computational difficulty of problems.

We are to admit no more causes of natural things (as we are told by New- ton) than such as are both true and sufficient to explain their appearances. This central theme is basic to the pursuit of science, and goes back to the principle known as Occam's razor: "if presented with a choice between indifferent alternatives, then one ought to select the simplest one". Uncon- sciously or explicitly, informal applications of this principle in science and mathematics abound.

People, problems, and proofs are the lifeblood of theoretical computer science. Behind the computing devices and applications that have transformed our lives are clever algorithms, and for every worthwhile algorithm there is a problem that it solves and a proof that it works. Before this proof there was an open problem: can one create an efficient algorithm to solve the computational problem? And, finally, behind these questions are the people who are excited about these fundamental issues in our computational world.