Denoising is a common pre-processing step prior to the analysis and interpretation of hyperspectral images (HSIs). However, the vast majority of methods typically adopted for HSI denoising exploit architectures originally developed for grayscale or RGB images, exhibiting limitations when processing highdimensional HSI data cubes. In particular, traditional methods do not take into account the high spectral correlation between adjacent bands in HSIs, which leads to unsatisfactory denoising performance as the rich spectral information present in HSIs is not fully exploited. To overcome this limitation, this paper considers deep learning models -such as convolutional neural networks (CNNs)-to perform spectral-spatial HSI denoising. The proposed model, called HSI single denosising CNN (HSI-SDeCNN), efficiently takes into consideration both the spatial and spectral information contained in HSIs. Experimental results on both synthetic and real data demonstrate that the proposed HSI-SDeCNN outperforms other state-of-the-art HSI denoising methods. Source code: https://github.com/mhaut/HSI-SDeCNN

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European electrical networks are evolving towards a distributed system where the number of power plants is growing and also the green plants based on renewable energy sources (RES) like wind and solar are increasing. Integration of RES leads to energy imbalance, due to the difficulty to predict their production. This paper proposes a two-time-scale Hierarchical Model Predictive Control (HMPC) strategy for real-time optimal control of Balance Responsible Parties (BRPs) in power systems with high penetration of renewable energy sources (RES). The proposed control strategy is able to handle ramp-rate constraints efficiently and results in reduced generation and imbalance costs due to real-time economic optimization of power setpoints.

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