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.

Technology has been promoting a great transformation in farming. The introduction of robotics; the use of sensors in the field; and the advances in computer vision; allow new systems to be developed to assist processes, such as phenotyping, of crop’s life cycle monitoring. This work presents, which we believe to be the first time, a system capable of generating 3D models of non-rigid corn plants, which can be used as a tool in the phenotyping process. The system is composed by two modules: an terrestrial acquisition module and a processing module. The terrestrial acquisition module is composed by a robot, equipped with an RGB-D camera and three sets of temperature, humidity, and luminosity sensors, that collects data in the field. The processing module conducts the non-rigid 3D plants reconstruction and merges the sensor data into these models. The work presented here also shows a novel technique for background removal in depth images, as well as efficient techniques for processing these images and the sensor data. Experiments have shown that from the models generated and the data collected, plant structural measurements can be performed accurately and the plant’s environment can be mapped, allowing the plant’s health to be evaluated and providing greater crop efficiency.

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.

The growing of the population with disabilities in the world must be accompanied by the growing of research and development of tools that help these users on basic computer activities. This paper presents the development of a system that allows the use of personal computers using only face movements. The system can be used by people with motor disabilities who still have head movements, such as superior members amputees and tetraplegic. For the development of the proposed system, the most efficient techniques in previous works were collected and analyzed, and new ones were developed in order to build a system with high performance and precision, ensuring the digital and social inclusion of the target public. Tests have shown that the tool is easy to learn, has a good performance and can be used in everyday computer applications.

A Universidade Presbiteriana Mackenzie (UPM) é um ambiente acadêmico movimentado, com uma grande variedade de estabelecimentos alimentícios. A gestão eficiente das filas de atendimento a esses locais é fundamental para melhorar a experiência dos usuários e, ao mesmo tempo, otimizar a operação dos estabelecimentos. Atualmente, a UPM carece de uma solução eficaz para esse problema. O projeto chamado “Off-Line”, tem a finalidade de desenvolver um aplicativo para otimizar filas de atendimento dos estabelecimentos comerciais dentro do campus da universidade. Foram realizadas sessões de entrevistas para identificar os problemas e compreender as necessidades e o comportamento dos usuários; verificação de validade do protótipo na ferramenta Figma, construção de modelagem de negócio, estratégias de marketing e a criação de uma aplicação funcional, com base nos protótipos construídos.