A quality evaluation methodology for health-related websites based on a 2-tuple fuzzy linguistic approach

Nowadays, the patients and physicians use the health-related websites as an important information source and, therefore, it is critical the quality evaluation of health- related websites. The quality assessment of health-related websites becomes especially relevant because their use imply the existence of a wide range of threats which can affect people’s health. Additionally, website quality evaluation can also contribute to maximize the exploitation of invested resources by organizations in the development of user-perceived quality websites. But there is not yet a clear and unambiguous definition of the concept of website quality and the debate about quality evaluation on the Web remains open. In this paper, we present a qualitative and user-oriented methodology for assessing quality of health-related websites based on a 2-tuple fuzzy linguistic approach. To identify the quality criteria set, a qualitative research has been carried out using the focus groups technique. The measurement method generates linguistic quality assessments considering the visitors’ judgements with respect to those quality criteria. The combination of the linguistic judgements is implemented without a loss of information by applying a 2-tuple linguistic weighted average operator. This methodology means an improvement on quality evaluation of health websites through the commitment to put users first.     

Differential evolution solution to the SLAM problem

A new solution to the Simultaneous Localization and Modelling problem is presented in this paper. The algorithm is based on the stochastic search for solutions in the state space to the global localization problem by means of a differential evolution algorithm. This non linear evolutive filter, called Evolutive Localization Filter (ELF), searches stochastically along the state space for the best robot pose estimate. The set of pose solutions (the population) focuses on the most likely areas according to the perception and up to date motion information. The population evolves using the log-likelihood of each candidate pose according to the observation and the motion errors derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model.The proposed SLAM algorithm operates in two steps: in the first step the ELF filter is used at local level to re-localize the robot based on the robot odometry, the laser scan at a given position and a local map where only a low number of the last scans have been integrated. In the second step, the aligned laser measures and the corrected robot poses are used to detect whether the robot is revisiting a previously crossed area (i.e., a cycle in the robot trajectory exists). Once a cycle is detected, the Evolutive Localization Filter is used again to estimate the accumulated residual drift in the detected loop and then to re-estimate the robot poses in order to integrate the sensor measures in the global map of the environment.The algorithm has been tested in different environments to demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.     

Three-dimensional facial surface modeling applied to recognition

Applications related to game technology, law-enforcement, security, medicine or biometrics are becoming increasingly important, which, combined with the proliferation of three-dimensional (3D) scanning hardware, have made that 3D face recognition is now becoming a promising and feasible alternative to two-dimensional (2D) face methods. The main advantage of 3D data, when compared with traditional 2D approaches, is that it provides information that is invariant to rigid geometric transformations and to pose and illumination conditions. One key element for any 3D face recognition system is the modeling of the available scanned data. This paper presents new 3D models for facial surface representation and evaluates them using two matching approaches: one based on support vector machines and another one on principal component analysis (with a Euclidean classifier). Also, two types of environments were tested in order to check the robustness of the proposed models: a controlled environment with respect to facial conditions (i.e. expressions, face rotations, etc.) and a non-controlled one (presenting face rotations and pronounced facial expressions). The recognition rates obtained using reduced spatial resolution representations (a 77.86% for non-controlled environments and a 90.16% for controlled environments, respectively) show that the proposed models can be effectively used for practical face recognition applications.     

Discontinuous gradient algorithm for finite-time estimation of time-varying parameters

ABSTRACT

In this work, we present a discontinuous algorithm capable of estimating time-varying parameters in finite time. The measured output is assumed to be linear in the parameters, i.e. it corresponds to a linear parametric model. It is further assumed that the parameter variation is uniformly bounded, and that the regressor is sufficiently exciting as to make the parameters identifiable.     

Robotics in Education

Collision avoidance is essential for safe robot manipulation. Especially with humans around, robots should work only when safety can be robustly guaranteed. In this paper, we propose using virtual impedance control for reactive, smooth, and consistent collision avoidance that interferes minimally with the original task. The virtual impedance control operates in the risk space, a vector space describing the possibilities of all forthcoming collisions, and is designed to elude all risks in a consistent response in order to create assuring human-robot interaction experiences. The proposed scheme intrinsically handles kinematic singularity and the activation of avoidance using a boundary layer defined on the spectrum of Jacobian. In cooperation with the original controller, the proposed avoidance scheme provides a proof of convergence if the original controller is stable with and without projection. In simulations and experiments, we verified the characteristics of the proposed control scheme and integrated the system with Microsoft Kinect to monitor the workspace for real-time collision detection and avoidance. The results show that the proposed approach is suitable for robot operation with humans nearby.     

On improving the efficiency of tensor voting

This paper proposes two alternative formulations to reduce the high computational complexity of tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. The first scheme consists of numerical approximations of the votes, which have been derived from an in-depth analysis of the plate and ball voting processes. The second scheme simplifies the formulation while keeping the same perceptual meaning of the original tensor voting: The stick tensor voting and the stick component of the plate tensor voting must reinforce surfaceness, the plate components of both the plate and ball tensor voting must boost curveness, whereas junctionness must be strengthened by the ball component of the ball tensor voting. Two new parameters have been proposed for the second formulation in order to control the potentially conflictive influence of the stick component of the plate vote and the ball component of the ball vote. Results show that the proposed formulations can be used in applications where efficiency is an issue since they have a complexity of order O(1). Moreover, the second proposed formulation has been shown to be more appropriate than the original tensor voting for estimating saliencies by appropriately setting the two new parameters.     

Catalytic oxidation with Al-Ce-Fe-PILC as a post-treatment system for coffee wet processing wastewater

The effluent from the anaerobic biological treatment of coffee wet processing wastewater (CWPW) contains a non-biodegradable compound that must be treated before it is discharged into a water source. In this paper, the wet hydrogen peroxide catalytic oxidation (WHPCO) process using Al-Ce-Fe-PILC catalysts was researched as a post-treatment system for CWPW and tested in a semi-batch reactor at atmospheric pressure and 25 °C. The Al-Ce-Fe-PILC achieved a high conversion rate of total phenolic compounds (70%) and mineralization to CO(2) (50%) after 5 h reaction time. The chemical oxygen demand (COD) of coffee processing wastewater after wet hydrogen peroxide catalytic oxidation was reduced in 66%. The combination of the two treatment methods, biological (developed by Cenicafé) and catalytic oxidation with Al-Ce-Fe-PILC, achieved a 97% reduction of COD in CWPW. Therefore, the WHPCO using Al-Ce-Fe-PILC catalysts is a viable alternative for the post-treatment of coffee processing wastewater.