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

Transistor sizing of sub-threshold standard cells for digital ultra-low power systems is a very challenging task because robustness has to be considered as an important design objective in addition to the competing resources power consumption and propagation delay. In this paper we regard this task as a multiobjective optimization problem (MOP) and show that the support of MOP algorithms is necessary and beneficial in the design process of sub-threshold CMOS logic standard cells. Optimization results are presented for an inverter, NAND gate, and NOR gate in a 65 nm process technology.

Multiobjective optimization for transistor sizing sub-threshold CMOSlogic standard cells

Robot development is a highly complex and interdisciplinary process. It comprises several phases: design, implementation, as well as test and validation to name some of them. In test and validation, simulation is commonly used. However, experiments with real robots still have a very important role since simulations cannot accurately model the real environment and, as a result, produce inconclusive results [1]. Performing robotic experiments, however, is considerably tedious. It is a repetitive process consisting of several steps: setup, execution, data logging, monitoring, and analysis. Moreover, it also requires a lot of resources especially in the case of experiments in multi-robotics.

Teleworkbench: A Teleoperated Platform for Experiments in Multi-robotics

The fast simulation of large networks of spiking neurons is a major task for the examination of biology inspired vision systems. Networks of this type are labelling features by synchronization of spikes and there is strong demand to simulate those effects in a real world environment. Because of the quite complex calculations for one model neuron the simulation of thousands or millions of these neurons is not efficient on existing hardware platforms. In order to simulate closer to the real time requirement, it is necessary to implement a dedicated hardware. Our aim is a hardware system mainly consisting of standard components which is as flexible as possible concerning the model neuron but as specialized as necessary to meet our performance requirements. Thus we decided to implement a parallel system with Digital Signal Processors (DSP) offering a large on-chip-memory. One main task of this work is the optimization of the simulation algorithm for the neurons distributed to the DSP which means the sequential part of simulation. This optimization benefits from the fact that there is only a very low percentage of simultaneously active neurons in vision networks. For communication between the nodes only spikes are distributed via a spike switching network. Processing of the network topology is realized by two different concepts. One idea is to compute the synapses autonomously on the processing node by representing a regular connection scheme with one connection mask for many neurons. Additional connections requiring adaptability and irregular connection schemes are stored in a shared memory. To avoid a bottleneck a synapse caching is used within each processing node. This paper describes the architecture of a DSP accelerator and shows the advantages with simulation results from a typical large vision network.

ParSPIKE-a parallel DSP-accelerator for dynamic simulation of large spiking neural networks

The design of resource efficient integrated circuits (IC) requires solving a minimization problem of more than one objective given as measures of available resources. This multiobjective optimization problem (MOP) can be solved on the smallest unit, the standard cells, to improve the performance of the entire IC. The traditional way of sizing the transistors of a standard logic cell does not focus on the resources directly. In this work transistor sizing is approached via an MOP and solved by set-oriented numerical techniques. A comparison of the Pareto optimal designs to elements of a commercial standard cell library indicates that for some gates the performance can even be significantly improved.

Multiobjective optimization for transistor sizing of CMOS logic standard cells using set-oriented numerical techniques

Neural networks are intended to be used in future nanoelectronics since these architectures seem to be robust against malfunctioning elements and noise. In this paper we analyze the robustness of radial basis function networks and determine upper bounds on the mean square error under noise contaminated weights and inputs.

Ulrich Rückert

Papers

Multiobjective optimization for transistor sizing sub-threshold CMOSlogic standard cells

Teleworkbench: A Teleoperated Platform for Experiments in Multi-robotics

ParSPIKE-a parallel DSP-accelerator for dynamic simulation of large spiking neural networks

Multiobjective optimization for transistor sizing of CMOS logic standard cells using set-oriented numerical techniques

Robustness of radial basis functions