Capture device identification from digital images using Kullback-Leibler divergence

Multimedia Tools and Applications - It is proposed a forensic method for the capture device identification from digital images, which requires two elements: i) a digital image subject to...     

Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers

The main purpose of a gene interaction network is to map the relationships of the genes that are out of sight when a genomic study is tackled. DNA microarrays allow the measure of gene expression of thousands of genes at the same time. These data constitute the numeric seed for the induction of the gene networks. In this paper, we propose a new approach to build gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling. The interactions induced by the Bayesian classifiers are based both on the expression levels and on the phenotype information of the supervised variable. Feature selection and bootstrap resampling add reliability and robustness to the overall process removing the false positive findings. The consensus among all the induced models produces a hierarchy of dependences and, thus, of variables. Biologists can define the depth level of the model hierarchy so the set of interactions and genes involved can vary from a sparse to a dense set. Experimental results show how these networks perform well on classification tasks. The biological validation matches previous biological findings and opens new hypothesis for future studies.     

Resilient overlay network for real-time interactive multimedia sessions in corporate networks

Real-time interactive multimedia communications are becoming increasingly useful for education, business, e-commerce and e-government, providing an enriched user experience in teleconferencing, e-meetings, distance training and product demonstrations. Large corporations are usually located at several sites, so real-time multipoint sessions within corporations are especially difficult. IP multicast is available or feasible within each site of an organization. Thus, corporate networks can be considered as various multicast-capable networks interconnected through a wide area network without multicast connectivity. This paper proposes a resilient self-managed overlay network to support real-time multipoint interactive sessions within corporate networks. The proposed overlay takes advantage of the configuration of corporate networks to self-organize and provide an efficient media delivery service, making use of multicast communications wherever available. Various self-healing techniques are implemented allowing for the continuity of ongoing sessions in spite of network disruptions and entity failures. Extensive simulations and tests have been carried out to assess the performance and resilience of the overlay facing several types of disruptions.     

Control of attendance applied in higher education through mobile NFC technologies

This paper presents an improved attendance control system, required for continuous evaluation which has become compulsory following the Bologna Process. It provides a solution based on NFC technology and is based on a real project developed and pilot tested at the “Universidad Pontificia de Salamanca, Campus Madrid”.     

Parastrephia quadrangularis: A Possible Alternative to Inhibit the Microbial Effect on the Generation of Acid Mine Drainage

Mine Water and the Environment - The role of iron- and sulfur- oxidizing microorganisms in the generation of acid mine drainage (AMD) from sulfide ores and tailings is widely recognized. The...     

A Non-Invasive Technique for Online Defect Detection on Steel Strip Surfaces

During the production of steel strips, a large amount of surface defects can be generated, due to harsh environmental conditions. A high number of surface defects can lead to rejection by the customer, which represents significant economic losses to the production plant. Thus, it is very important to detect the presence and type of the defects generated during the production of each steel strip. Using this information, it is possible to determine whether a strip is suitable for sale, and it may also be useful to determine the origin of defects and, if possible, prevent them from being generated in subsequent strips. To perform these tasks, non-invasive inspection techniques are usually used, carried out automatically by artificial vision systems. Although the inspection conducted by humans is more accurate, they become fatigued quickly, or may even be unable to carry out the inspection correctly when the forward speed of the strip is high. In this paper, a new detection technique is proposed, based on the division of an image into a set of overlapping areas. The optimum values for the configuration parameters of the detection technique are automatically determined using a genetic algorithm. After the detection phase, all the defects are classified using a neural network. A very satisfactory success rate has been achieved in both detection and classification phases.     

Statistical analysis of a multi-objective optimization algorithm based on a model of particles with vorticity behavior

In this paper, a strategy for multi-objective optimization based upon the behavior of a particle swarm with rotational and linear motion is presented. The strategy for multi-objective optimization is based upon the emulation of the linear and circular movements of a swarm (flock). Thus emerges the physical basis for the cognitive model, which in conjunction with exploration–exploitation results in the proposal of a cognitive algorithm, which is tested through several multi-objective optimization functions. The algorithm proposed is compared with standard particle swarm optimization multi-objective via statistical analysis.