Wireless dynamometers are an adequate solution to measure mechanical loads where the use of cables is not practicable. Proprietary solutions are applied in most of the cases found out in the literature on wireless dynamometers. However, these solutions have a high market price, and they are black-box systems, which limit their application in customized conditions. In this context, this study presents the development of a wireless dynamometer based on low market price components and with an open-source technology for real-time monitoring with a suitable sampling rate of 400 Hz (or 32.8 kbps). The proposed dynamometer used a complete Wheatstone bridge configuration and an amplifier circuit with a static gain of 99.8 V/V. Calibration tests and analysis of the performance were carried out according to ISO 376 standard. The proposed wireless dynamometer presented performance indicators of 0.76% for hysteresis, 1.34% for linearity, 4.83 mV/N for sensitivity, and 5.22% for repeatability. These results show that the low market price components and open-source technology can be used to build reliable wireless dynamometers able to comply with customized industrial demands.

Com o incremento em números de núcleos em sistemas em chip, arquiteturas de barramento tem sofrido com algumas limitações. Os requisitos das aplicações demandam mais largura de banda e baixas latências. Em face a esse cenário, redes em chip emergiram como uma opção para superar essas limitações. Redes em chip são compostas por um conjunto de roteadores e enlaces de comunicação. Nesse trabalho, nós propomos o uso de técnicas de inteligência artificial para otimizar a arquitetura das redes em chip. A ferramenta explora o espaço de projeto em termos de predição de área, latência e potência para diferentes configurações. Os resultados têm demonstrado a validade dessa proposta e a adequação as restrições impostas pelo projetista.Palavras-chave: Redes em chip, inteligência artificial, sistemas em chip.

Threats such as Botnets have become very popular in the current usage of the Internet, such as attacks like distributed denial of services (DoS) which can cause a significant impact on the use of technology. One way to mitigate such issues can be a focus on using intelligent models that can attempt to identify the existence of Botnets in the network traffic early. Thus, this work aims to evaluate the current state of the art on threats related to Botnets and how intelligent technology has been used in real-world restrictions such as real-time deadlines and increased network traffic. From our findings, we have indications that Botnet detection in real-time still is a more significant challenge because the computation power has not grown at the same rate that Internet traffic. This has pointed out other restrictions that must be considered, like privacy legislation and employing cryptography methods for all communications. In this context, we discuss the following steps to deal with the identified issues.