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Publicações

2020

Intensive summer course in robotics – Robotcraft

Autores
Fonseca Ferreira, NM; Araujo, A; Couceiro, MS; Portugal, D;

Publicação
Applied Computing and Informatics

Abstract

2020

Evaluation of Lightweight Convolutional Neural Networks for Real-Time Electrical Assets Detection

Autores
Barbosa, J; Dias, A; Almeida, J; Silva, E;

Publicação
Advances in Intelligent Systems and Computing

Abstract
The big growth of electrical demand by the countries required larger and more complex power systems, which have led to a greater need for monitoring and maintenance of these systems. To overcome this problem, UAVs equipped with appropriated sensors have emerged, allowing the reduction of the costs and risks when compared with traditional methods. The development of UAVs together with the great advance of the deep learning technologies, more precisely in the detection of objects, allowed to increase the level of automation in the process of inspection. This work presents an electrical assets monitoring system for detection of insulators and structures (poles and pylons) from images captured through a UAV. The proposed detection system is based on lightweight Convolutional Neural Networks and it is able to run on a portable device, aiming for a low cost, accurate and modular system, capable of running in real time. © 2020, Springer Nature Switzerland AG.

2020

Usability and Sense of Presence in Virtual Worlds for Distance Education: A Case Study with Virtual Reality Experts

Autores
Krassmann, A; Mazzuco, A; Melo, M; Bessa, M; Bercht, M;

Publicação
Proceedings of the 12th International Conference on Computer Supported Education

Abstract

2020

Minha: Large-scale distributed systems testing made practical

Autores
Machado, N; Maia, F; Neves, F; Coelho, F; Pereira, J;

Publicação
Leibniz International Proceedings in Informatics, LIPIcs

Abstract
Testing large-scale distributed system software is still far from practical as the sheer scale needed and the inherent non-determinism make it very expensive to deploy and use realistically large environments, even with cloud computing and state-of-the-art automation. Moreover, observing global states without disturbing the system under test is itself difficult. This is particularly troubling as the gap between distributed algorithms and their implementations can easily introduce subtle bugs that are disclosed only with suitably large scale tests. We address this challenge with Minha, a framework that virtualizes multiple JVM instances in a single JVM, thus simulating a distributed environment where each host runs on a separate machine, accessing dedicated network and CPU resources. The key contributions are the ability to run off-the-shelf concurrent and distributed JVM bytecode programs while at the same time scaling up to thousands of virtual nodes; and enabling global observation within standard software testing frameworks. Our experiments with two distributed systems show the usefulness of Minha in disclosing errors, evaluating global properties, and in scaling tests orders of magnitude with the same hardware resources. © Nuno Machado, Francisco Maia, Francisco Neves, Fábio Coelho, and José Pereira; licensed under Creative Commons License CC-BY 23rd International Conference on Principles of Distributed Systems (OPODIS 2019).

2020

Black-box inter-application traffic monitoring for adaptive container placement

Autores
Neves, F; Vilaça, R; Pereira, J;

Publicação
Proceedings of the 35th Annual ACM Symposium on Applied Computing

Abstract

2020

Wireless Sensor Network for Ignitions Detection: An IoT approach

Autores
Brito, T; Pereira, AI; Lima, J; Valente, A;

Publicação
Electronics

Abstract
Wireless Sensor Networks (WSN) can be used to acquire environmental variables useful for decision-making, such as agriculture and forestry. Installing a WSN on the forest will allow the acquisition of ecological variables of high importance on risk analysis and fire detection. The presented paper addresses two types of WSN developed modules that can be used on the forest to detect fire ignitions using LoRaWAN to establish the communication between the nodes and a central system. The collaboration between these modules generate a heterogeneous WSN; for this reason, both are designed to complement each other. The first module, the HTW, has sensors that acquire data on a wide scale in the target region, such as air temperature and humidity, solar radiation, barometric pressure, among others (can be expanded). The second, the 5FTH, has a set of sensors with point data acquisition, such as flame ignition, humidity, and temperature. To test HTW and 5FTH, a LoRaWAN communication based on the Lorix One gateway is used, demonstrating the acquisition and transmission of forest data (simulation and real cases). Even in internal or external environments, these results allow validating the developed modules. Therefore, they can assist authorities in fighting wildfire and forest surveillance systems in decision-making.

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