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

With more and more event-based neuromorphic hardware systems being developed at universities and in industry, there is a growing need for assessing their performance with domain specific measures. In this work, we use the methodology of converting pre-trained non-spiking to spiking neural networks to evaluate the performance loss and measure the energy-per-inference for three neuromorphic hardware systems (BrainScaleS, Spikey, SpiNNaker) and common simulation frameworks for CPU (NEST) and CPU/GPU (GeNN). For analog hardware we further apply a re-training technique known as hardware-in-the-loop training to cope with device mismatch. This analysis is performed for five different networks, including three networks that have been found by an automated optimization with a neural architecture search framework. We demonstrate that the conversion loss is usually below one percent for digital implementations, and moderately higher for analog systems with the benefit of much lower energy-per-inference costs.

Benchmarking Deep Spiking Neural Networks on Neuromorphic Hardware

In this paper, we present the prototypical implementation of the scalable GigaNetIC chip multiprocessor architecture. We use an FPGA-based rapid prototyping system to verify the functionality of our architecture in a network application scenario before we are going to fabricate the ASIC in a modem CMOS standard cell technology. The rapid prototyping environment gives us the opportunity to test our multiprocessor architecture with Ethernet-based data streams in a real network scenario. Our system concept is based on a massively parallel processor structure. Due to its regularity, our architecture can be easily scaled to accommodate a wide range of packet processing applications with disparate performance and throughput requirements at high reliability. Furthermore, the composition from predefined building blocks guarantees fast design cycles and simplifies system verification. We present standard cell synthesis results as well as a performance analysis for a firewall application with various couplings of hardware accelerators.

GigaNetIC : A scalable embedded on-chip multiprocessor architecture for network applications

The invention concerns a method for distributing data blocks among data storage systems in computer systems and/or storage networks and/or a data network, preferably an intranet and/or the Internet, a number of data block sets D<sub

Method and arrangement for distributing sets of data blocks in storage networks and/or a data network as well as computer programme product and computer-readable storage medium

Ulrich Rückert

Papers

Benchmarking Deep Spiking Neural Networks on Neuromorphic Hardware

GigaNetIC : A scalable embedded on-chip multiprocessor architecture for network applications

Method and arrangement for distributing sets of data blocks in storage networks and/or a data network as well as computer programme product and computer-readable storage medium