AcquaSmart: An Environment Big Data Analytics and Internet of Things to Education and Research

Being an interdisciplinary area, Internet of Things presents great challenges to learning. However, it already is and will continue to be part of the daily life and thus requires qualified professionals to advance projects in this area. Apart from acquiring theoretical concepts, students need to put knowledge into practice. This practical learning aims to provide a means of easy assimilation to the student and that can mirror real situations of implementation. This work presents an Internet of Things learning methodology based on the development of environments that enable the student to put theoretical knowledge into practice in a scenario of easy assimilation. It is expected that the student will be able to understand the process of developing Internet of Things projects and the technologies involved in it. The proposed methodology is composed of 5 steps. The student analyzes the development environment, defines the type of implementation to be carried out, develops the hardware, the software and documents of the project. The data architecture together with the methodology allow the student to use and propose various types of development environments, controllers and web applications, being very flexible for learning. The implementation of temperature control was carried out in an aquarium environment. The proposed methodology proved to be efficient for the development of this project, so it can be applied in Internet of Things learning in educational institutions

Desenvolvimento de uma interface computacional natural para pessoas com deficiência motora baseada em visão computacional

O desenvolvimento de novas formas de interação com o computador, como telas touchscreen e óculos de realidade virtual, tornaram mais intuitiva e natural a sua utilização. O presente trabalho apresenta conceitos e técnicas utilizados para o desenvolvimento de um sistema que permite aos usuários utilizarem o computador pessoal de maneira simples e eficiente por meio dos movimentos da face. Foram utilizadas técnicas de visão computacional como segmentação, detecção facial e detecção dos pontos da face, bem como técnicas de programação e matemáticas para o desenvolvimento desse sistema. Trata-se de uma interface humano-computador do tipo natural que utiliza os movimentos da face, dos olhos e da boca para executar funções de controle como movimentação do cursor do mouse e acionamento de cliques e teclas. Diversos testes mostraram que o sistema desenvolvido apresentou desempenho superior em relação a sistemas similares, desempenho de processamento de 100 fps, throughput de 1; 20 bits=s para movimentações com o cursor do mouse e 1,15 teclas acionadas pela face por segundo, é de fácil utilização, rápido aprendizado e pode ser utilizado em um grande número de aplicações, como navegação na internet, redes sociais e jogos. O sistema desenvolvido pode ser utilizado por pessoas com deficiências motoras como uma tecnologia assistiva, promovendo a inclusão social, além de proporcionar maiores oportunidades educacionais e profissionais para esse público.New interaction devices such as touchscreens and virtual reality googles has made human computer interaction more intuitive and natural. This work presents concepts and techniques used for the development of a system that allows users to interact with personal computer in a simple and e cient way through face movements. Computer vision techniques such as segmentation, facial detection and landmarks detection were used, as well as programming and mathematical techniques for the development of this system. It is a natural human-computer interface that uses face, eye and mouth movements to perform control functions such as mouse cursor movement and triggering of clicks and keys. Several tests have shown that the developed system has superior performance compared to similar systems, 100 fps processing performance, throughput of 1:20 bits=s for mouse cursor movements, and 1.15 keystrokes with the face per second, it easy to use, fast learning and it can be used in a large number of applications such as internet browsing, social networking and games. The developed system can be used by people with motor disabilities as an assistive technology, promoting social inclusion, as well as providing greater educational and professional opportunities for this public.Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Prediction of Motor Failure Time Using An Artificial Neural Network

Industry is constantly seeking ways to avoid corrective maintenance so as to reduce costs. Performing regular scheduled maintenance can help to mitigate this problem, but not necessarily in the most efficient way. In the context of condition-based maintenance, the main contributions of this work were to propose a methodology to treat and transform the collected data from a vibration system that simulated a motor and to build a dataset to train and test an Artificial Neural Network capable of predicting the future condition of the equipment, pointing out when a failure can happen. To achieve this goal, a device model was built to simulate typical motor vibrations, consisting of a computer cooler fan and several magnets. Measurements were made using an accelerometer, and the data were collected and processed to produce a structured dataset. The neural network training with this dataset converged quickly and stably, while the tests performed, k-fold cross-validation and model generalization, presented excellent performance. The same tests were performed with other machine learning techniques, to demonstrate the effectiveness of neural networks mainly in their generalizability. The results of the work confirm that it is possible to use neural networks to perform predictive tasks in relation to the conditions of industrial equipment. This is an important area of study that helps to support the growth of smart industries