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

Neural networks are intended to be used in future nanoelectronic systems since neural architectures seem to be robust against malfunctioning elements and noise in their weights. In this paper we analyze the fault-tolerance of Radial Basis Function networks to Stuck-At-Faults at the trained weights and at the output of neurons. Moreover, we determine upper bounds on the mean square error arising from these faults.

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Tolerance of radial basis functions against stuck-at-faults

In the last decade there has been an increasing interest in the use of artificial neural networks in various applications. One of these application areas, where the analysis of the performance of a neural network approach is comparatively advanced, is associative memory. Artificial neural networks (ANNs) are well suited for the implementation of associative memories at least because the processing elements (artificial neurons) in an ANN operate in a highly parallel way and thus a considerable gain in speed is to be expected. This parallelism is one of the major reasons for investigating new computational models inspired by neurophysiological processing principles. The idea is that information is stored in terms of synaptic connectivities between artificial neurons while the activities of the neurons represent the stored patterns. Thus the information is stored distributed over many units and not anywhere in particular. Each unit participates in the encoding of several informations (patterns). ANNs combine both storing and processing information whereas conventional computers are based on sequential processors operating on the contents of a passive memory in which information is accessed by finding the right place in memory.

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VLSI Design of an Associative Memory Based on Distributed Storage of Information

This work presents the novel multi-modal Variational Autoencoder approach $\mathbf{M}^{\mathbf{2}}\mathbf{VAE}$ which is derived from the complete marginal joint log-likelihood. This allows the end-to-end training of Bayesian information fusion on raw data for all subsets of a sensor setup. Furthermore, we introduce the concept of in-place fusion – applicable to distributed sensing - where latent embeddings of observations need to be fused with new data. To facilitate in-place fusion even on raw data, we introduced the concept of a re-encoding loss that stabilizes the decoding and makes visualization of latent statistics possible. We also show that the $\mathbf{M}^{\mathbf{2}}\mathbf{VAE}$ finds a coherent latent embedding, such that a single naive Bayes classifier performs equally well on all permutations of a bi-modal Mixture-of-Gaussians signal. Finally, we show that our approach outperforms current VAE approaches on a bi-modal MNIST & fashion-MNIST data set and works sufficiently well as a preprocessing on a tri-modal simulated camera & LiDAR data set from the Gazebo simulator.

Jointly Trained Variational Autoencoder for Multi-Modal Sensor Fusion

Dynamic reconfiguration is a promising approach for resource efficient utilization of microelectronic systems. However, work on general approaches to model reconfigurable hardware is quite rare. Therefore, we have developed a new analytical model, which can be used for the analysis of the various approaches to dynamic reconfiguration. Based on our model we define metrics for partial reconfiguration to evaluate different system approaches. Our main objective is to analyze placement algorithms for partially reconfigurable architectures. The model is able to consider miscellaneous constraints and cost parameters for online placement. Thus, placement can be adapted to dynamically changing system environments.

Placement-oriented modeling of partially reconfigurable architectures

Hardware architectures based on a field of hardwareextended
processors can provide flexible computing power
for applications where parallelism can be exploited. For
multiprocessors, the assignment of functionality to execution
units can have a great impact on the performance.
Additionally, finding the optimal mapping can be a timeconsuming
task. We present a multiprocessor architecture
along with a suitable design method that includes an automated
solution to the mapping problem. Our hardware architecture
employs a network-on-chip (NoC) to achieve a
high degree of scalability for the application and for the
system in respect to future integration technologies.We also
show how to reduce the packet buffer requirements with a
proper scheduling strategy and present first estimates for
the resource consumption of an application targeted for mobile
networking.

Ulrich Rückert

Papers

Tolerance of radial basis functions against stuck-at-faults

VLSI Design of an Associative Memory Based on Distributed Storage of Information

Jointly Trained Variational Autoencoder for Multi-Modal Sensor Fusion

Placement-oriented modeling of partially reconfigurable architectures

A framework for design space exploration of resource efficient network processing on multiprocessor SoCs