Atenção temporal e análise da interferência da característica do estímulo na percepção

Attention is a phenomenon that allows the selection of relevant stimuli in order to prioritize them and improve their processing. This modulation could occur in any step of the process: in an early stage or in a late stage, more precisely, in a perceptive or motor stage. However, even with a rich literature about attention in time, there are still some divergences about how this modulation occurs. A hypothesis about it says that temporal attention would only be able to prepare the motor system to respond. The perceptual modulation would only occur when the temporal expectation is in combination with another expectation of a property with neuronal receptive field. In this situation, the receptive field's pre-activation is the explanation of how temporal attention would be capable to modulate perceptual process. The crucial objective was to test this hypothesis. In other words, it was to verify if the feature expectation of a stimulus (Gabor orientation) and its temporal expectancy interferes in perception quality. Two experiments were made: the first one tested the voluntary temporal expectation, and the second one tested the automatic temporal expectation. Our data shows that both Feature-based Attention and Temporal Attention improve the process of perception. Temporal expectation effects just occur in situations of competitive environment. Hypothesis verification was not conclusive, because of methodological problems

New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Development of a methodology for cost determination of wastewater treatment based on functional diagram

This work describes a methodology developed for determination of costs associated to products generated in a small wastewater treatment station for sanitary wastewater from a university campus. This methodology begins with plant component units identification, relating their fluid and thermodynamics features for each point marked in its process diagram. Following, its functional diagram is developed and its formulation is elaborated, in exergetic base, describing all equations for these points, which are the constraints for exergetic production cost problem and are used in equations to determine the costs associated to products generated in SWTS. This methodology was applied to a hypothetical system based on SWTS former parts and presented consistent results when compared to expected values based on previous exergetic expertise. (C) 2008 Elsevier Ltd. All rights reserved.

A cost optimization-based design of precast concrete floors using genetic algorithms

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Dosimetry and cost of imaging osseointegrated implants with film-based and computed tomography

Thermoluminescent dosimeters were used to measure radiation doses at craniofacial sites in a tissue-equivalent phantom during film-based multidirectional tomography with the Tomax Ultrascan (Incubation Industries, Ivyland, Pa.) and during computed tomography with the Elscint Excel 2400 (Elscint Corp., Tel Aviv, Israel). Mean absorbed doses for presurgical mandibular and maxillary canine and molar implant assessments were converted to equivalent doses, which were then multipied by published weighting factors and summed to give effective doses. The computed tomgraphy device consistently delivered higher doses than the Tomax Ultrascan to all anatomic locations; the differences were most pronounced when only one or two implant sites were evaluated. The reasons for the dose disparities are considered both anatomically and procedurally. A survey of examination cost revealed film-based multidirectional tomography to be less expensive than computed tomography.

Improving Parkinson's disease identification through evolutionary-based feature selection

Parkinson's disease (PD) automatic identification has been actively pursued over several works in the literature. In this paper, we deal with this problem by applying evolutionary-based techniques in order to find the subset of features that maximize the accuracy of the Optimum-Path Forest (OPF) classifier. The reason for the choice of this classifier relies on its fast training phase, given that each possible solution to be optimized is guided by the OPF accuracy. We also show results that improved other ones recently obtained in the context of PD automatic identification. © 2011 IEEE.

Optimal low-cost transfer to L4 and L5 lagrangian points based on G-family of periodic orbits

An alternative transfer strategy to send spacecrafts to stable orbits around the Lagrangian equilibrium points L4 and L5 based in trajectories derived from the periodic orbits around LI is presented in this work. The trajectories derived, called Trajectories G, are described and studied in terms of the initial generation requirements and their energy variations relative to the Earth through the passage by the lunar sphere of influence. Missions for insertion of spacecrafts in elliptic orbits around L4 and L5 are analysed considering the Restricted Three-Body Problem Earth- Moon-particle and the results are discussed starting from the thrust, time of flight and energy variation relative to the Earth. Copyright© (2012) by the International Astronautical Federation.

ECG arrhythmia classification based on optimum-path forest

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e.; cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e.; support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e.; there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. © 2012 Elsevier Ltd. All rights reserved.

A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest

Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness. © 2013 Elsevier Ltd. All rights reserved.

Supervised variational relevance learning, an analytic geometric feature selection with applications to omic datasets

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)