This paper main goal is to detect anomalies in photovoltaic panels installed in power generation systems using thermographic images. It was used image processing techniques and pattern recognition by means of artificial neural network with supervised and adaptive training. In order to implement the anomaly detection system it was selected from the tutorial "Solar Cell Development" by FLIR systems, thermographic images of photovoltaic cells with defects. The language of technical computing, MATLAB©, was used to make the preprocessing of all the collected images and it was also used for the training of an ABNET network, a PSOM network and a MLP network. Through the cross-validation method, the hit rate of the ABNET’s network and PSOM’s network was 87.5% and for the MLP the hit rate was 96.0%. The results corroborate for the use of these neural networks on a bank of images that will be created through a Research and Development Project (R&D) in progress. Este trabalho tem como objetivo principal propor um método baseado em Redes Neurais Artificiais (RNA) para detectar anomalias em painéis fotovoltaicos instalados em sistemas de geração elétrica, tendo como base imagens termográficas. Assim, foram utilizadas técnicas de processamento de imagem e reconhecimento de padrões por meio de RNA com treinamento supervisionado e adaptativo. Para a implementação do sistema de detecção de anomalias, foram selecionadas imagens do tutorial “Solar Cell Development” da FLIR systems, sendo que estas imagens termográficas apresentam pontos de aquecimento em células específicas de determinado painel fotovoltaico. A ferramenta computacional Matlab© foi utilizada para realizar o pré-processamento do banco de imagens e para o treinamento de uma rede ABNET, uma rede PSOM e uma rede MLP. Por meio de validação cruzada, a taxa de acertos para a classificação dos padrões da rede ABNET e PSOM foi de 87,5% e para a rede MLP foi de 96%. Os resultados corroboram para utilização dessas redes em um banco de imagens que será construído por meio do Projeto de Pesquisa e Desenvolvimento (P&D) em andamento.

Suitable techniques for reading out large arrays of metallic magnetic calorimeters are very challenging and demand optimal performance in each system stage to preserve intrinsic fast signal rise time, excellent energy resolution, large energy dynamic range, and highly linear detector response. A multiplexer based on superconducting quantum interference device is the key component in achieving this goal. Linearity of the response requires a technique called flux-ramp modulation, which generates a phase-modulated complex waveform according to the sensor signal. At the end-cap of the signal processing chain it is currently recovered by a demodulation process referred to as Fourier measurement. In this paper, we prove that the mentioned method is equivalent to a maximum likelihood estimation, build an analogy to the sensor array-based direction of arrival estimation, propose an alternative approach based on the eigenvalue decomposition of a correlation matrix, present the mathematical formulation, discuss the results, and compare both methods. Our method is robust against transients due to flux ramp returning point and allows proper estimation in large phase ranges.

This paper proposes a user-centric clustering formation algorithm for both AP and RIS based on the greedy algorithm in the weighted sum-rate maximization problem for indoor RIS-aided cell-free scenarios, where each user is matched with a group of APs and a group of RISs. For this, a non-convex joint precoding problem is solved following methods available in the literature, where we add a two-timescale approach to reduce overall signaling overhead.