A Multi-agent Architecture Based on the BDI Model for Data Fusion in Visual Sensor Networks

The newest surveillance applications is attempting more complex tasks such as the analysis of the behavior of individuals and crowds. These complex tasks may use a distributed visual sensor network in order to gain coverage and exploit the inherent redundancy of the overlapped field of views. This article, presents a Multi-agent architecture based on the Belief-Desire-Intention (BDI) model for processing the information and fusing the data in a distributed visual sensor network. Instead of exchanging raw images between the agents involved in the visual network, local signal processing is performed and only the key observed features are shared. After a registration or calibration phase, the proposed architecture performs tracking, data fusion and coordination. Using the proposed Multi-agent architecture, we focus on the means of fusing the estimated positions on the ground plane from different agents which are applied to the same object. This fusion process is used for two different purposes: (1) to obtain a continuity in the tracking along the field of view of the cameras involved in the distributed network, (2) to improve the quality of the tracking by means of data fusion techniques, and by discarding non reliable sensors. Experimental results on two different scenarios show that the designed architecture can successfully track an object even when occlusions or sensor??s errors take place. The sensor??s errors are reduced by exploiting the inherent redundancy of a visual sensor network with overlapped field of views.     

Application of machine learning techniques to analyse student interactions and improve the collaboration process

In e-learning environments that use the collaboration strategy, providing participants with a set of communication services may not be enough to ensure collaborative learning. It is thus necessary to analyse collaboration regularly and frequently. Using machine learning techniques is recommended when analysing environments where there are a large number of participants or where they control the collaboration process. This research studied two approaches that use machine learning techniques to analyse student collaboration in a long-term collaborative learning experience during the academic years 2006–2007, 2007–2008 and 2008–2009. The aims were to analyse collaboration during the collaboration process and that it should be domain independent. Accordingly, the intention was to be able to carry out the analysis regularly and frequently in different collaborative environments. One of the two approaches classifies students according to their collaboration using unsupervised machine learning techniques, clustering, while the other approach constructs metrics that provide information on collaboration using supervised learning techniques, decision trees. The research results suggest that collaboration can be analysed in this way, thus achieving the aims set out with two different machine learning techniques.     

Polyelectrolytes based on poly(<Emphasis Type="Italic">p</Emphasis>-acryloyloxybenzaldehyde) with arsonic acid groups useful in the colloidal synthesis of silver nanoparticles

Several methods have been developed for synthesis of Ag nanoparticles. However, Ag nanoparticles are unstable materials, and they tend to agglomerate losing their properties. Polymers are commonly employed for the stabilization of Ag nanoparticles in colloidal solutions. Polymers with ionic groups such polyelectrolytes can stabilize metallic nanoparticles through electrostatic and steric effects. In this work we employed poly(p-acryloyloxybenzaldehyde) and their derivates containing arsonic acid groups in ortho and para positions as reducing and stabilizing agents in the synthesis of Ag nanoparticles. Polyelectrolytes containing arsonic acid groups in ortho position were better reducing agents than the poly(p-acryloyloxybenzaldehyde) and the polyelectrolyte with arsonic acid group in para position, leading to the reduction of Ag⁺ ions in short reaction time. The polyelectrolyte with arsonic acid groups in para position was the best stabilizing agent leading to obtaining Ag nanoparticles with the smallest average size.     

Functionalization in copolymers based on 4VP:DVB for the removal of Cr(VI) ions from aqueous solution

Adsorption capacity for Cr(VI) ions has been evaluated on two ion‐exchange resins. A gel‐type precursor resin was obtained by suspension polymerization of 4‐vinylpyridine and divinylbenzene monomers with 40% crosslinking degree. It was quaternized with different functional groups to give two ion‐exchange resins. The R2 resin contained sulfobetaine groups, and R3 methyl groups. The resins were characterized by Fourier transform infrared and solid‐state ¹³C CP/MAS NMR spectroscopy and by elemental analysis. An adsorption experiment was carried out by a batch equilibrium procedure. Langmuir and Freundlich isotherm models were used to determine the adsorption capacity. R2 and R3 resins exhibited maximum adsorption capacity q_max = 75.8 and 56.2 mg/g, respectively. The resins achieve equilibrium in 60 min. The R3 and R2 resins showed a retention capacity of 95% and 80% for the Cr(VI) ions, respectively. The behaviors of both resins were explained well by a pseudo‐second‐order kinetics model. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45548.     

Comparative study of dental enamel loss after debonding braces by analytical scanning electron microscopy (SEM)

Clinical procedures when shear forces are applied to brackets suggest adhesion forces between 2.8 and 10.0 MPa as appropriate. In this study dental enamel was evaluated by scanning electron microscopy (SEM) before and after removing the brackets. Thirty bicuspids (previous prophylaxis) with metallic brackets (Roth Inovation 0.022 GAC), Transbond Plus SEP 3M Unitek adhesive and Transbond XT 3M resin were used. The samples were preserved to 37°C during 24 hr and submited to tangential forces with the Instron Universal machine 1.0 mm/min speed load strength resistance debonding. Also the Adhesive Remanent Index (ARI) test was made, evaluating the bracket base and the bicuspid surface. All the bracket SEM images were processed with AutoCAD to determine the enamel detached area. The average value was 6.86 MPa (SD ± 3.2 MPa). ARI value 1= 63.3%, value 2= 20%, value 3= 13.3% and 33% presented value 0. All those samples with dental enamel loss, presented different situations as fractures, ledges, horizontal, and vertical loss in some cases, and some scratch lines. There is no association between the debonding resistance and enamel presence. Less than half of the remanent adhesive on the dental enamel was present in most of the samples when the ARI test was applied. When the resin area increases, the debonding resistance also increases, and when the enamel loss increases, the resin free metallic area of the bracket base decreases in the debonding.     

Modularization of model transformations through a phasing mechanism

In recent years a great effort has been devoted to understanding the nature of model transformations. As a result, several mechanisms to improve model transformation languages have been proposed. Phasing has been mentioned in some works as a rule scheduling or organization mechanism, but without any detail. In this paper, we present a phasing mechanism in the context of rule-based transformation languages. We explain the structure and the behavior of the mechanism, and how it can be integrated in a language. We also analyze how the mechanism promotes modularity, internal transformation composition and helps to solve usual transformation problems. Besides, we show several examples of application to illustrate the usefulness of the mechanism.     

Control of two Electrical Plants

In this paper, a controller is recommended for the regulation of two electrical plants. Since electrical plants generate electricity all the time, the regulation to get that all the plant states reach constant behaviors is important. Two main characteristics of the introduced method are: (i) it is based in the separation of the plant model equations, only some model equations are chosen for the regulation while the other model equations are ignored, it avoids the difficulty in the consideration of the full plant model; (ii) the Lyapunov strategy is employed to analyze the stability of the selected model equations in the electrical plant, it lets to ensure the regulation purpose. The advised method is applied in a gas turbine and a wind turbine for the electricity generation.     

Monitoring for Awareness and Reflection in Ubiquitous Learning Environments

Despite the educational affordances that ubiquitous learning has shown, it is still hampered by several orchestration difficulties. One of these difficulties is that teachers lose awareness of what the students perform across the multiple technologies and spaces involved. Monitoring can help in such awareness, and it has been highly explored in face-to-face and blended learning. Nevertheless, in ubiquitous learning environments, monitoring has been usually limited to activities taking place in a specific type of space (e.g., outdoors). In this article, we propose a monitoring system for ubiquitous learning, which was evaluated in three authentic studies, supporting the participants in the affordable monitoring of learning situations involving web, augmented-physical, and 3D virtual world spaces. The work carried out also helped identify a set of guidelines, which are expected to be useful for researchers and technology developers aiming to provide participants’ support in ubiquitous learning environments.     

On the comparison of different kernel functionals and neighborhood geometry for nonlocal means filtering

The present work proposes a review and comparison of different Kernel functionals and neighborhood geometry for Nonlocal Means (NLM) in the task of digital image filtering. Some different alternatives to change the classical exponential kernel function used in NLM methods are explored. Moreover, some approaches that change the geometry of the neighborhood and use dimensionality reduction of the neighborhood or patches onto principal component analysis (PCA) are also analyzed, and their performance is compared with respect to the classic NLM method. Mainly, six approaches were compared using quantitative and qualitative evaluations, to do this an homogeneous framework has been established using the same simulation platform, the same computer, and same conditions for the initializing parameters. According to the obtained comparison, one can say that the NLM filtering could be improved when changing the kernel, particularly for the case of the Tukey kernel. On the other hand, the excellent performance given by recent hybrid approaches such as NLM SAP, NLM PCA (PH), and the BM3D SAPCA lead to establish that significantly improvements to the classic NLM could be obtained. Particularly, the BM3D SAPCA approach gives the best denoising results, however, the computation times were the longest.