Proposal of convergence of e-Learning systems for t-Learning

This article presents considerations about viability on reutilize existing web based e-Learning systems on Interactive Digital TV environment according to Digital TV standard adopted in Brazil. Considering the popularity of Moodle system in academic and corporative area, such system was chosen as a foundation for a survey into its properties to create a specification of an Application Programming Interface (API) for convergence to t-Learning characteristics that demands efforts in interface design area due the fact that computer and TV concepts are totally different. This work aims to present studies concerning user interface design during two stages: survey and detail of functionalities from an e-Learning system and how to adapt them for the Interactive TV regarding usability context and Information Architecture concepts.

Teaching robotics systems in a multimedia environment

This paper describes the development of a multimedia educational system to teach and learn robotic systems. Multimedia resources have been used to build a virtual laboratory where users are able to utilize functions of a robotic arm, by moving and clicking the mouse without worrying about the detailed robot internal operation. The multimedia system is integrated with a real robotic arm, which was also developed at the university. Through robotic topic presentations and interactive capabilities provided by this system and its tools, students can devote themselves on the learning process just as they do in the traditional face-to-face classes. and the target public of this system are the engineering students themselves.

Conceptual model for adaptable and extensible visual data exploration

Interactive visual representations complement traditional statistical and machine learning techniques for data analysis, allowing users to play a more active role in a knowledge discovery process and making the whole process more understandable. Though visual representations are applicable to several stages of the knowledge discovery process, a common use of visualization is in the initial stages to explore and organize a sometimes unknown and complex data set. In this context, the integrated and coordinated - that is, user actions should be capable of affecting multiple visualizations when desired - use of multiple graphical representations allows data to be observed from several perspectives and offers richer information than isolated representations. In this paper we propose an underlying model for an extensible and adaptable environment that allows independently developed visualization components to be gradually integrated into a user configured knowledge discovery application. Because a major requirement when using multiple visual techniques is the ability to link amongst them, so that user actions executed on a representation propagate to others if desired, the model also allows runtime configuration of coordinated user actions over different visual representations. We illustrate how this environment is being used to assist data exploration and organization in a climate classification problem.

A new implementation of Population Based Incremental Learning method for optimization studies in electromagnetics

To enhance the global search ability of Population Based Incremental Learning (PBIL) methods, It Is proposed that multiple probability vectors are to be Included on available PBIL algorithms. As a result, the strategy for updating those probability vectors and the negative learning and mutation operators are redefined as reported. Numerical examples are reported to demonstrate the pros and cons of the newly Implemented algorithm. ©2006 IEEE.

A framework for learning in humanoid simulated robots

One of the most important characteristics of intelligent activity is the ability to change behaviour according to many forms of feedback. Through learning an agent can interact with its environment to improve its performance over time. However, most of the techniques known that involves learning are time expensive, i.e., once the agent is supposed to learn over time by experimentation, the task has to be executed many times. Hence, high fidelity simulators can save a lot of time. In this context, this paper describes the framework designed to allow a team of real RoboNova-I humanoids robots to be simulated under USARSim environment. Details about the complete process of modeling and programming the robot are given, as well as the learning methodology proposed to improve robot's performance. Due to the use of a high fidelity model, the learning algorithms can be widely explored in simulation before adapted to real robots. © 2008 Springer-Verlag Berlin Heidelberg.

Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground. ©2012 IEEE.

The assessment of the quality of sugar using electronic tongue and machine learning algorithms

The correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system along with established techniques of supervised learning leads to the correct classification of sugar samples according to their qualities. In this paper, we offer two new real, public and non-encoded sugar datasets whose attributes were automatically collected using an electronic tongue, with and without pH controlling. Moreover, we compare the performance achieved by several established machine learning methods. Our experiments were diligently designed to ensure statistically sound results and they indicate that k-nearest neighbors method outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison. © 2012 IEEE.

Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.

Guidelines for the integration of certifiable management systems in industrial companies

Organizations often operate in turbulent environments characterized by intense competitiveness, constant technological progress, new market requirements, and scarce natural resources. This scenario imposes the constant need for change in the operation and companies' management. The integration of certifiable management systems is an effective alternative in this sense. The objective of the present study is to propose guidelines for the integration of the ISO 9001 Quality Management System (QMS), ISO 14001 Environmental Management System (EMS) and OHSAS 18001 Occupational Health and Safety Management System (OHSMS) in industrial companies. These guidelines were developed based on a theoretical framework and on the results from fourteen case studies performed in Brazilian industrial companies. The proposed guidelines were divided into three phases: A) integration planning, b) integration development, and c) integration control and improvement.

Estudo do potencial do pacu (Piaractus mesopotamicus) como agente de controle biológico de Egeria densa, E. najas e Ceratophyllum demersum

Visando fornecer subsídios para elaboração de sistema de manejo integrado das grandes massas de plantas daninhas aquáticas submersas em lagos e represas, o presente trabalho teve como objetivo verificar a eficiência do pacu (Piaractus mesopotamicus) como agente de controle biológico de Egeria densa, E. najas e Ceratophyllum demersum. As espécies de plantas daninhas foram oferecidas individualmente, duas a duas e as três espécies juntas. Verificou-se que este peixe tem uma eficiência média de controle dessas plantas daninhas variando entre 28 e 100%, podendo eliminar uma massa verde dessas plantas, com a mesma quantidade de seu peso, em sete dias. A eficiência de controle diária aumentou com o tempo de predação. O pacu é mais seletivo para E. densa ou E. najas quando na presença de C. demersum. Não ocorreu alteração na eficiência de controle do pacu sobre E. densa ou E. najas em todos os tratamentos e nos três períodos estudados (três, cinco e sete dias).

Effects of chronic acetazolamide administration on gas exchange and acid-base control in pulmonary circulation in exercising horses

P>Reasons for performing study:Carbonic anhydrase (CA) catalyses the hydration/dehydration reaction of CO(2) and increases the rate of Cl- and HCO(3)- exchange between the erythrocytes and plasma. Therefore, chronic inhibition of CA has a potential to attenuate CO(2) output and induce greater metabolic and respiratory acidosis in exercising horses.Objectives:To determine the effects of Carbonic anhydrase inhibition on CO(2) output and ionic exchange between erythrocytes and plasma and their influence on acid-base balance in the pulmonary circulation (across the lung) in exercising horses with and without CA inhibition.Methods:Six horses were exercised to exhaustion on a treadmill without (Con) and with CA inhibition (AczTr). CA inhibition was achieved with administration of acetazolamide (10 mg/kg bwt t.i.d. for 3 days and 30 mg/kg bwt before exercise). Arterial, mixed venous blood and CO(2) output were sampled at rest and during exercise. An integrated physicochemical systems approach was used to describe acid base changes.Results:AczTr decreased the duration of exercise by 45% (P < 0.0001). During the transition from rest to exercise CO(2) output was lower in AczTr (P < 0.0001). Arterial PCO(2) (P < 0.0001; mean +/- s.e. 71 +/- 2 mmHg AczTr, 46 +/- 2 mmHg Con) was higher, whereas hydrogen ion (P = 0.01; 12.8 +/- 0.6 nEq/l AczTr, 15.5 +/- 0.6 nEq/l Con) and bicarbonate (P = 0.007; 5.5 +/- 0.7 mEq/l AczTr, 10.1 +/- 1.3 mEq/l Con) differences across the lung were lower in AczTr compared to Con. No difference was observed in weak electrolytes across the lung. Strong ion difference across the lung was lower in AczTr (P = 0.0003; 4.9 +/- 0.8 mEq AczTr, 7.5 +/- 1.2 mEq Con), which was affected by strong ion changes across the lung with exception of lactate.Conclusions:CO(2) and chloride changes in erythrocytes across the lung seem to be the major contributors to acid-base and ions balance in pulmonary circulation in exercising horses.

Neural networks training using the constructivism paradigms

The Backpropagation Algorithm (BA) is the standard method for training multilayer Artificial Neural Networks (ANN), although it converges very slowly and can stop in a local minimum. We present a new method for neural network training using the BA inspired on constructivism, an alphabetization method proposed by Emilia Ferreiro based on Piaget philosophy. Simulation results show that the proposed configuration usually obtains a lower final mean square error, when compared with the standard BA and with the BA with momentum factor.

Automation in fault detection using neural network and model updating

In this article, an implementation of structural health monitoring process automation based on vibration measurements is proposed. The work presents an alternative approach which intent is to exploit the capability of model updating techniques associated to neural networks to be used in a process of automation of fault detection. The updating procedure supplies a reliable model which permits to simulate any damage condition in order to establish direct correlation between faults and deviation in the response of the model. The ability of the neural networks to recognize, at known signature, changes in the actual data of a model in real time are explored to investigate changes of the actual operation conditions of the system. The learning of the network is performed using a compressed spectrum signal created for each specific type of fault. Different fault conditions for a frame structure are evaluated using simulated data as well as measured experimental data.

Revendo os códigos hamming e CRC para tratar com erros na transmissão de dados

This article has the purpose to review the main codes used to detect and correct errors in data communication specifically in the computer's network. The Hamming's code and the Ciclic Redundancy Code (CRC) are presented as the focus of this article as well as CRC hardware implementation. Each code is reviewed in details in order to fill the gaps in the literature and to make it accessible to the computer science and engineering students as well as to anyone who may be interested in learning the technique to treat error in data communication.

Environment mapping for mobile robots navigation using hierarchical neural network and omnivision

Autonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the robot uses a sensorial system composed for ultrasound sensors and a catadioptric vision system formed by a camera and a conical mirror. The mapping system is composed by three modules. Two of them will be presented in this paper: the classifier and the characterizer module. The first module uses a hierarchical neural network to do the classification; the second uses techiniques of extraction of attributes of images and recognition of invariant patterns extracted from the places images set. The neural network of the classifier module is structured in two layers, reason and intuition, and is trained to classify each place explored for the robot amongst four predefine classes. The final result of the exploration is the construction of a topological map of the explored environment. Results gotten through the simulation of the both modules of the mapping system will be presented in this paper. © 2008 IEEE.