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

The SPADE (spatio-temporal Spike PAttern Detection and Evaluation) method was developed to find reoccurring spatio-temporal patterns in neuronal spike activity (parallel spike trains). However, depending on the number of spike trains and the length of recording, this method can exhibit long runtimes. Based on a realistic benchmark data set, we identified that the combination of pattern mining (using the FP-Growth algorithm) and the result filtering account for 85 to 90 % of the method's total runtime. Therefore, in this paper, we propose a customized FP-Growth implementation tailored to the requirements of SPADE, which significantly accelerates pattern mining and result filtering. Our version allows for parallel and distributed execution, and due to the improvements made, an execution on heterogeneous and low-power embedded devices is now also possible. The implementation has been evaluated using a traditional workstation based on an Intel Broadwell Xeon E5-1650 v4 as a baseline. Furthermore, the heterogeneous microserver platform RECS|Box has been used for evaluating the implementation on two HiSilicon Hi1616 (Kunpeng 916), an Intel Coffee Lake-ER Xeon E-2276ME, an Intel Broadwell Xeon D-D1577, and three NVIDIA Tegra devices (Jetson AGX Xavier, Jetson Xavier NX, and Jetson TX2). Depending on the platform, our implementation is between 27 and 200 times faster than the original implementation. At the same time, the energy consumption was reduced by up to two orders of magnitude.

/pdf/acceleration-of-the-spade-method-using-a-custom-tailored-fp-1b5w42xhs9.pdf

Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation.

The Autonomous Mini Robot (AMiRo) is a modular and extensible mini robot platform, designed for scientific research and education. Its decentralized architecture enables to easily add or remove functionalities as required for any application. A well defined physical and electrical interface offers the possibility to design new modules with minimal effort. The open-source software framework for the AMiRo is already growing, since the robot is commonly used for research, education, and competitions. Several demonstrations of the system are given, which present its capabilities. Starting with a fuzzy controller for line following, these demonstrations include remote controlling as well as an implementation of an artificial neural network running on the platform.

AMiRo: A Mini Robot for Scientific Applications

Power Control in Directional Mobile Ad Hoc Networks

For image processing a lot of algorithms have been developed in the past that are well suited for specific tasks. These algorithms are often optimized for high performance computers with high power consumption, thus, it is not possible to use these algorithms for mobile applications. In this paper, a massive parallel vision system is partly discussed which is biologically inspired and requires less power. This system consists of several layers of pulsed neural networks, e.g. some layers form specific features detectors to decompose the input image in several features. The features are completed or bound by an associative memory that needs a synchronized input pulse pattern. In this paper, we focus on the synchronization stage of the system that is combined with the associative memory. The discussed synchronization unit detects correlated pulses in the network, collects them and activates a synchrones output pattern.

Pattern synchronization for associative memory in pulse coded neural networks

Ultrasonic enables a mobile autonomous system to obtain information about obstacles in
large environments. In the presented work, taking the limited energy resources of a mobile robot into
account, only one transmitter is used. The employment of two receivers permits the calculation of
the distance to and the orientation of a wall, using only time-of-flight measurements. The evaluation
procedure consists of basic geometrical calculations, which do not strain the system's calculating
power too excessively. The results show that it is possible to place the transmitter and the receivers
close together without losing much of the performance. Hence the introduced arrangement of
ultrasonic sensors can be used for small systems, e.g. the mini-robot Khepera.

Ulrich Rückert

Papers

Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation.

AMiRo: A Mini Robot for Scientific Applications

Power Control in Directional Mobile Ad Hoc Networks

Pattern synchronization for associative memory in pulse coded neural networks

Discrete Ultrasonic Sensors for Mobile Autonomous Systems