Due to the emergence of new microbreweries in the Brazilian market, there is a need to construct equipment to quickly and accurately identify the alcohol content in beverages, together with a reduced marketing cost. Towards this purpose, the electronic noses prove to be the most suitable equipment for this situation. In this work, a prototype was developed to detect the concentration of ethanol in a high spectrum of beers presents in the market. It was used cheap and easy-to-acquire 13 gas sensors made with a metal oxide semiconductor (MOS). Samples with 15 predetermined alcohol contents were used for the training and construction of the models. For validation, seven different commercial beverages were used. The correlation (R2) of 0.888 for the MLR (RMSE = 0.45) and the error of 5.47% for the ELM (RMSE = 0.33) demonstrate that the equipment can be an effective tool for detecting the levels of alcohol contained in beverages.

This work proposes an environment for real-time testing of heterogeneous embedded systems through cosimulation. The verification occurs on real-time between the system software and hardware platform using the High Level Architecture (HLA) as a middleware between the hardware device and the simulated model. The novelty of this approach is not only providing support for simulations, but also allowing the synchronous integration with any physical hardware devices. In this paper we use the Ptolemy framework as a simulation platform. The integration of HLA with Ptolemy and the hardware models open a vast set of applications, like the test of many devices at the same time, running the same, or different applications or modules, the usage of Ptolemy for real-time control of embedded systems and the distributed execution of different embedded devices for performance improvement or collaborative execution. A case study is presented to prove the concept, showing the successful integration between the Ptolemy framework with an implementation using Atmel and ARM microcontrollers.

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A robotic system is a reconfigurable element, and inits programming, an algorithm can be implemented in order todetect and classify failures. This is an important step to ensurethat errors in actions do not cause damage or bring risks.Considering this, a Neural Network Multi Layer Perceptron(MLP) was used, in order to classify a set of failures in robotactuators, present in a database. This purpose is to analyze ifrobotic failures could be classified by MLP. The raw data aredivided in a temporal progression manner and torque in x, y andz axes. In total, five MLP neural networks were implemented foreach type of failure classification, using two different topologies.The number of neurons in the hidden layer is in accord with thecriteria of Kolmogorov and Weka, being the latter the besttopology for such application. In comparison to an algorithm(SKIL) using the same set of data, the MLP obtained the bestperformance in any topology of classification, with hit rates in80 to 90%.

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