Abstract
Multi-agent systems is being pointed as particularly suited to design and engineer a new class of control systems to operate at the factory plants addressing the current requirements of modularity, flexibility and re-configurability. This paper introduces the main principles of a multi-agent system approach to support the integration of production and quality control processes in washing machines production lines that is being developed under the EU FP7 GRACE project. © 2011 Springer-Verlag.

Abstract

Abstract
This paper introduces a new supervised technique to segment hyperspectral images: the Bayesian segmentation based on discriminative classification and on multilevel logistic (MLL) spatial prior. The approach is Bayesian and exploits both spectral and spatial information. Given a spectral vector, the posterior class probability distribution is modeled using multinomial logistic regression (MLR) which, being a discriminative model, allows to learn directly the boundaries between the decision regions and, thus, to successfully deal with high-dimensionality data. To control the machine complexity and, thus, its generalization capacity, the prior on the multinomial logistic vector is assumed to follow a componentwise independent Laplacian density. The vector of weights is computed via the fast sparse multinomial logistic regression (FSMLR), a variation of the sparse multinomial logistic regression (SMLR), conceived to deal with large data sets beyond the reach of the SMLR. To avoid the high computational complexity involved in estimating the Laplacian regularization parameter, we have also considered the Jeffreys prior, as it does not depend on any hyperparameter. The prior probability distribution on the class-label image is an MLL Markov-Gibbs distribution, which promotes segmentation results with equal neighboring class labels. The a-expansion optimization algorithm, a powerful graph-cut-based integer optimization tool, is used to compute the maximum a posteriori segmentation. The effectiveness of the proposed methodology is illustrated by comparing its performance with the state-of-the-art methods on synthetic and real hyperspectral image data sets. The reported results give clear evidence of the relevance of using both spatial and spectral information in hyperspectral image segmentation.

Abstract
Reconfigurable computing architectures are commonly used for accelerating applications and/or for achieving energy savings. However, most reconfigurable computing architectures suffer from computationally demanding placement and routing (P&R) steps. This problem may disable their use in systems requiring dynamic compilation (e.g., to guarantee application portability in embedded systems). Bearing in mind the simplification of P&R steps, this paper presents and analyzes a coarse-grained reconfigurable array (CGRA) extended with global multistage interconnect networks, specifically Omega Networks. We show that integrating one or two Omega Networks in a CGRA permits to simplify the P&R stage resulting in both low hardware resource overhead and low performance degradation (18% for an 8 x 8 array). We compare the proposed CGRA, which integrates one or two Omega Networks, with a CGRA based on a grid of processing elements with reach neighbor interconnections and with a torus topology. The execution time needed to perform the P&R stage for the two array architectures shows that the array using two Omega Networks needs a far simpler and faster P&R. The P&R stage in our approach completed on average in about 16 x less time for the 17 benchmarks used. Similar fast approaches needed CGRAs with more complex interconnect resources in order to allow most of the benchmarks used to be successfully placed and routed.

Abstract
In this paper, we approach the task of finding suitable images to illustrate text, from specific news stories to more generic blog entries. We have developed an automatic illustration system supported by multimedia information retrieval, that analyzes text and presents a list of candidate images to illustrate it. The system was tested on the SAPO-Labs media collection, containing almost two million images with short descriptions, and the MIRFlickr-25000 collection, with photos and user tags from Flickr. Visual content is described by the Joint Composite Descriptor and indexed by a Permutation-Prefix Index. Illustration is a three-stage process using textual search, score filtering and visual clustering. A preliminary evaluation using exhaustive and approximate visual searches demonstrates the capabilities of the visual descriptor and approximate indexing scheme used. © 2011 IEEE.

Abstract
Recently, considerable research has been carried out regarding three-dimensional object manipulation on multi-touch surfaces. However, most current solutions were developed having in mind scenarios with a camera perpendicular to a scene axis, and cannot be easily used to manipulate three-dimensional objects in unconstrained viewpoints. In this paper, we present and evaluate a set of object manipulation techniques. As a test bed for this study, we used an unconstrained virtual LEGO modeling tool, taking advantage of ongoing work and popularity of LEGO construction among people of all ages. From this evaluation we improved our understanding on how users prefer to manipulate 3D objects on multi-touch surfaces.