Three-phase current-source shunt active power filter with solar photovoltaic grid interface

This paper presents the proposal of a three phase current source shunt active power filter (CS-SAPF) with photovoltaic grid interface. The proposed system combines the compensation of reactive power and harmonics with the injection of energy from a solar photovoltaic array into the electrical power grid. The proposed equipment presents the advantage of giving good use to the current source inverter, even when the solar photovoltaic array is not producing energy. The paper describes the control system of the CS SAPF, the energy injection control strategy, and the current harmonics and power factor compensation strategy. Simulation results to assess the performance of the proposed system are also presented.

From a NoSQL data source to a business intelligence solution: An experiment

We are living in the era of Big Data. A time which is characterized by the continuous creation of vast amounts of data, originated from different sources, and with different formats. First, with the rise of the social networks and, more recently, with the advent of the Internet of Things (IoT), in which everyone and (eventually) everything is linked to the Internet, data with enormous potential for organizations is being continuously generated. In order to be more competitive, organizations want to access and explore all the richness that is present in those data. Indeed, Big Data is only as valuable as the insights organizations gather from it to make better decisions, which is the main goal of Business Intelligence. In this paper we describe an experiment in which data obtained from a NoSQL data source (database technology explicitly developed to deal with the specificities of Big Data) is used to feed a Business Intelligence solution.

Neural correlates of face familiarity in institutionally reared children with distinctive, atypical social behavior

Although the impact of early adverse experience on neural processing of face familiarity has been studied, research has not taken into account disordered child behavior. This work compared the neural processing of familiar versus strangers' faces in 47 institutionalized children with a mean age of 54 months to determine the effects of (a) the presence versus absence of atypical social behavior and (b) inhibited versus indiscriminant atypical behavior. Results revealed a pattern of cortical hypoactivation in institutionalized children manifesting atypical social behavior and that inhibited children displayed larger neural response to a caregiver's face than to the stranger's, while indiscriminant children did not discriminate between stimuli. These findings suggest that neural correlates of face familiarity are associated with social functioning in institutionalized children.

Abstract computation in schizophrenia detection through artificial neural network based systems

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

Thrombophilia screening: An artificial neural network approach

Thrombotic disorders have severe consequences for the patients and for the society in general, being one of the main causes of death. These facts reveal that it is extremely important to be preventive; being aware of how probable is to have that kind of syndrome. Indeed, this work will focus on the development of a decision support system that will cater for an individual risk evaluation with respect to the surge of thrombotic complaints. The Knowledge Representation and Reasoning procedures used will be based on an extension to the Logic Programming language, allowing the handling of incomplete and/or default data. The computational framework in place will be centered on Artificial Neural Networks.

Artificial neural networks in diagnosis of liver diseases

Liver diseases have severe patients’ consequences, being one of the main causes of premature death. These facts reveal the centrality of one`s daily habits, and how important it is the early diagnosis of these kind of illnesses, not only to the patients themselves, but also to the society in general. Therefore, this work will focus on the development of a diagnosis support system to these kind of maladies, built under a formal framework based on Logic Programming, in terms of its knowledge representation and reasoning procedures, complemented with an approach to computing grounded on Artificial Neural Networks.

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

Validation of a method to measure light distortion surrounding a source of glare

Our objective was to validate a new device dedicated to measure the light disturbances surrounding bright sources of light under different sources of potential variability. Twenty subjects were involved in the study. Light distortion was measured using an experimental prototype (light distortion analyzer, CEORLab, University of Minho, Portugal) comprising twenty-four LED arrays panel at 2 m. Sources of variability included: intrasession and intersession repeated measures, pupil size (3 versus 6 mm), defocus (þ0.50) correction for the working distance, angular resolution (15 deg versus 30 deg), temporal stimuli presentation, and pupil size. Size, shape, location, and irregularity parameters have been obtained. At a low speed of presentation of the stimuli, changes in angular resolution did not have an effect on the results of the parameters measured. Results did not change with pupil size. Intensity of the central glare source significantly influenced the outcomes. Examination time was reduced by 30% when a 30 deg angular resolution was explored instead of 15 deg. Measurements were fast and repeatable under the same experimental conditions. Size and shape parameters showed the highest consistency, whereas location and irregularity parameters showed lower consistency. The system was sensitive to changes in the intensity of the central glare source but not to pupil changes in this sample of healthy subjects.

[Letter to the editor] Sustained effects of a neural-based intervention in a refractory case of Tourette syndrome

There are only a few treatments available for Tourette syndrome (TS). These treatments frequently do notwork in patients with moderate to severe TS [1]. Neuroimaging studies show a correlation between tics severity and increased activation over motor pathways, along with reduced activation over the control areas of the cortico-striato-thalamo-cortical circuits [2]. Moreover, the temporal pattern of tic generation suggests that cortical activation especially in the SMA precedes subcortical activation [3]. Following this assumption, here we explored the brain effects of 10-daily sessions of cathodal transcranial Direct Current Stimulation (tDCS) delivered over the pre-SMA in a patient with refractory and severe TS and also assessed whether those changes were long lasting (up to 6 months).

The impact of carbon source in Candida albicans virulence: participation of RLM1 in pathogen host interaction

Dissertação de mestrado em Genética Molecular

High-aspect-ratio tridimensional electrode arrays for neural applications

Tese de Doutoramento em Engenharia Biomédica.

A design of experiments to assess phosphorous removal and crystal properties in struvite precipitation of source separated urine using different Mg sources

Supplementary data associated with this article can be found, in the online version, at: http://dx.doi.org/10.1016/j.cej.2016.03.148.