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Demand-side management comprises a portfolio of actions on the consumers' side to ensure reliable power indices from the electrical system. The home energy management system (HEMS) is used to manage the consumption and production of energy in smart homes. However, the technology of HEMS architecture can be used for the detection and classification of power quality disturbances. This paper presents low-voltage metering hardware that uses an ARM Cortex M4 and real-time operating system to detect and classify power quality disturbances. In the context of HEMS, the proposed metering infrastructure can be used as a smart meter, which provides the service of power quality monitoring. For this type of application, there is a need to ensure that the development of this device has an acceptable cost, which is one of the reasons for the choice of an ARM microprocessor. However, managing a wide range of operations (data acquisition, data preprocessing, disturbance detection and classification, energy consumption, and data exchange) is a complex task and, consequently, requires the optimization of the embedded software. To overcome this difficulty, the use of a real-time operating system provided by Texas Instruments (called TI-RTOS) is proposed with the objective of managing operations at the hardware level. Thus, a methodology with low computational cost has been defined and embedded. The proposed approach uses a preprocessing stage to extract some features that are used as inputs to detect and classify disturbances. In this way, it was possible to evaluate and demonstrate the performance of the embedded algorithm when applied to synthetic and real power quality signals. Consequently, it is noted that the results are

Routing, Modulation Level and Spectrum Assignment (RMLSA) é um dos principais problemas estudados nas redes ópticas elásticas. Este trabalho concentra-se no estudo da seleção da banda de guarda, um ou mais slots livres entre os circuitos, que é usada nas soluções do problema RMLSA. Neste contexto, uma nova abordagem, chamada de GUARDIAN, que usa uma rede neural multi layer perceptron para selecionar a banda de guarda de forma adaptativa é proposta. O desempenho da proposta é comparado com outras propostas adaptativas: AGBA e GBUN. A proposta alcança uma redução na probabilidade de bloqueio de banda de pelo menos 54,01% em relação ao AGBA e 51,26% em relação ao GBUN.

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