From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Fast Fourier Transform

Computer algebra systems are now ubiquitous in all areas of science and engineering. This highly successful textbook, widely regarded as the “bible of computer algebra”, gives a thorough introduction to the algorithmic basis of the mathematical engine in computer algebra systems. Designed to accompany oneor two-semester courses for advanced undergraduate or graduate students in computer science or mathematics, its comprehensiveness and reliability has also made it an essential reference for professionals in the area. Special features include: detailed study of algorithms including time analysis; implementation reports on several topics; complete proofs of the mathematical underpinnings; and a wide variety of applications (among others, in chemistry, coding theory, cryptography, computational logic, and the design of calendars and musical scales). A great deal of historical information and illustration enlivens the text. In this third edition, errors have been corrected and much of the Fast Euclidean Algorithm chapter has been renovated.

Modern computer algebra

The Artificial Intelligence field continues to be plagued by what can only be described as ‘bold promises for the future syndrome’, often perpetrated by researchers who should know better. While impartial assessment can point to concrete contributions over the past 50 years (such as automated theorem proving, games strategies, the LISP and Prolog high-level computer languages, Automatic Speech Recognition, Natural Language Processing, mobile robot path planning, unmanned vehicles, humanoid robots, data mining, and more), the more cynical argue that AI has witnessed more than its fair share of ‘unmitigated disasters’ during this time – see, for example [3,58,107,125,186]. The general public becomes rapidly jaded with such ‘bold predictions’ that fail to live up to their original hype, and which ultimately render the zealots’ promises as counter-productive.

Computational Intelligence: An Introduction

It seems obvious that the massively parallel computations inherent in artificial neural networks (ANNs) can only be realized by massively parallel hardware. However, the vast majority of the many ANN applications simulate their ANNs on sequential computers which, in turn, are not resource-efficient. The increasing availability of parallel standard hardware such as FPGAs, graphics processors, and multi-core processors offers new scopes and challenges in respect to resource-efficiency and real-time applications of ANNs. Within this paper we will discuss some key issues for parallel ANN implementation on these standard devices compared to special purpose ANN implementations.

Parallel Neural Hardware: The Time is Right

Technologieplanung in der Mikroelektronik – von Moore's Law zur Nanotechnologie-Roadmap

Trotz ihrer hohen Leistungsfahigkeit leiden digitale Hardware-Losungen fur Re-gelungen in mechatronischen Systemen oft darunter, dass fur ihren Einsatz detaillierte Kenntnisse im Entwurf digitaler Schaltungen erforderlich sind. Zusatzlich sind sie im Vergleich zu den meist favorisierten Mikrokontroller- oder DSP-Losungen wenig flexibel. In diesem Beitrag wird eine Systemarchitektur vorgestellt, die mit Hilfe dynamisch rekonfigurierbarer Hardware ein ahnliches Mas an Flexibilitat wie DSPs erreicht, ohne dabei an Leistungsfahigkeit einzubusen. Daruber hinaus beschreiben wir einen Entwurfsprozess, der es einem Entwickler auch ohne tiefgehende Hardware-Kenntnisse ermoglicht, mit gewohnten Entwicklungsumgebungen Hardware-Regelungen zu realisieren.

Rekonfigurierbare Hardware zur Regelung mechatronischer Systeme

Intelligent VLSI-Memories For Robotics

A multiplier-free phase estimation algorithm for coherent QPSK detection is presented. The barycenter approach is modified by a selectivity mechanism that improves performance. In addition to recently published measurement results, theoretical derivations and simulation results are provided.

Ulrich Rückert

Papers

Parallel Neural Hardware: The Time is Right

Technologieplanung in der Mikroelektronik – von Moore's Law zur Nanotechnologie-Roadmap

Rekonfigurierbare Hardware zur Regelung mechatronischer Systeme

Intelligent VLSI-Memories For Robotics

Realtime phase tracking with multiplier-free barycenter approximation in digital synchronous QPSK receiver for coherent detection