Abstract
The design and testing of a "dry" active electrode for electroencephalographic recording is described. A comparative study between the EEG signals recorded in human volunteers simultaneously with the classical Ag-AgCl and "dry" active electrodes was carried out and the reported preliminary results are consistent with a better performance of these devices over the conventional Ag-AgCl electrodes.

Abstract
The integration of different robot automation technologies, with the aim for reusing available production solutions, is a major obstacle for deployment of low-cost components into productive (high-performance) systems. Technologies demanding high processing power, like machine vision or voice recognition systems, are normally easy to program but require proprietary languages and platforms, which constitutes an important problem during communications and setup. Instead of the current need for trained specialist, in particular flexible manufacturing in SMEs call for solutions that are easy easy to use and (re)configure. One attempt in that direction is the service-oriented architecture (SOA) approach, which here is accomplished by the use of Universal Plug-and-Play (UPnP) technologies and confronted with real robot application demand represented by an experimental manufacturing cell. Contributions include the way of building software applications to program manufacturing cells whose building blocks are represented by UPnP devices. Such devices encapsulate both manufacturing equipment and interaction methods. The latter is exemplified by a speech recognition system, for which a tool for automatic generation of UPnP devices based on the information contained in speech recognition XML grammars is presented. Experiences form experiments confirms the desired efficiency and simplicity when setting up advanced manufacturing equipment. © 2007 IFAC.

Abstract
A process of Knowledge Discovery in Databases (KDD) involving large amounts of data requires a considerable amount of computational power. The process may be done on a dedicated and expensive machinery or, for some tasks, one can use distributed computing techniques on a network of affordable machines. In either approach it is usual the user to specify the workflow of the sub-tasks composing the whole KDD process before execution starts. In this paper we propose a technique that we call Distributed Generative Data Mining. The generative feature of the technique is due to its capability of generating new sub-tasks of the Data Mining analysis process at execution time. The workflow of sub-tasks of the DM is, therefore, dynamic. To deploy the proposed technique we extended the Distributed Data Mining system HARVARD and adapted an Inductive Logic Programming system (IndLog) used in a Relational Data Ming task. As a proof-of-concept, the extended system was used to analyse an artificial dataset of a credit scoring problem with eighty million records.

Abstract

Abstract
This paper describes methods used to construct an interactive installation using human motion to animate both a visual and aural particle system. It outlines the rotoscoping, meta-motion processing, aural and visual rendering systems. It goes into detailed explanation of the "particle flow" systems which lend form to the virtual characters. The paper finishes with a description of the tracking system and "inverse interaction", used by the installation. Copyright 2007 ACM.

Abstract
A structured vocabulary of terms, such as a textual thesaurus, provides a way to conceptually describe visual information. The retrieval model described in this paper combines a conceptual and a visual layer as a first step towards the integration of ontologies and content-based image retrieval. Terms are related with image regions through a weighted association. This model allows the execution of concept-level queries, fulfilling user expectations and reducing the so-called semantic gap. Region-based relevance feedback is used to improve the quality of results in each query session and to help in the discovery of associations between text and image. The learning mechanism, whose function is to discover existing term-region associations, is based on a clustering algorithm applied over the features space and on propagation functions, which acts in each cluster where new information is available from user interaction. This approach is validated with the presentation of promising results obtained using the VOIR - Visual Object Information Retrieval system.