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Volcanic eruptions are spectacular but dangerous phenomena. Depending on their magnitude and location, they also have the potential for becoming major social and economic disasters. Some of the most important volcanic events include ash fallout, lava flows, and related phenomena, such as volcanic debris avalanches and tsunamis. The ongoing demographic congestion around volcanic structures, such as Mount Etna, increases the potential risks and costs that volcanic eruptions represent and leads to a growing demand for implementing effective risk mitigation measures. To fully evaluate the potential damage and losses that a volcanic eruption disaster may cause, the distribution and characterization of all the exposed elements must be considered. Over the past decades, advances in satellite remote sensing and geographic information system techniques have greatly assisted the collection of land cover data. However, assessment of the elements at risk is a lengthy and time-consuming process. In fact, usually data including all exposed elements and land uses are gathered from several Institutional web portals and very high-resolution satellite imagery, not freely available, manipulated by operators. Here, we propose a deep learning approach to automatically identify the elements at risk in high spatial resolution satellite images. In particular, a Convolutional Neural Network (CNN) model is adopted to classify land use and land cover in volcanic areas thus allowing to carefully assess the total exposure by using freely available satellite images. A retrospective analysis is conducted on Mount Etna highlighting changes in the exposure over the last decade.

Myoelectric pattern recognition is a useful tool for identifying the user’s intended motion. However, the inherent nonstationary properties of Electromyography (EMG) signals usually limited the use of real time commercial prostheses. These variations cause the degradation of myoelectric control performance and make it unstable over time, across subjects and sessions. In this study, this challenge is overcome by combining the use of robust spatial features and the supervised adaptive learning method to improve the myoelectric performance. Three types of spatial features are proposed based on histogram oriented gradient (HOG) algorithm and intensity features namely H, HI, and AIH features. H features correspond to extracting HOG features from the HD-sEMG map. HI feature is obtained by concatenating the H features with scalar intensity feature that calculated from HD-sEMG map. Finally, the hybrid AIH features are produced by combining the H features with the intensity features matrix (AI) that obtained from the segmented maps. Three sub-databases are used for evaluation. The proposal feature sets are compared with time-domain (TD) and a combination of intensity and center of gravity features (ICG) to show the powerful of these features. The offline results report the superiority of the classifier’s performance in term of precision and sensitivity based on AIH features than other feature sets (i.e. H, HI, TD, ICG) with improvement 4.1%, 3.5%, 2.24%, 5.3% and 6%, 5%, 2.2%, 6.9% respectively. The adaptive classifier based on AIH features outperforms adaptive myoelectric control based on other feature sets and the original version. The adaptive classifier utilized testing data that update the original dataset which in turn has a significant effect on improving the myoelectric performance in the presence of the variation of EMG signal properties.

In recent years, the number of researches in the field of artificial limbs has increased significantly in order to improve the performance of the use of these limbs by amputees. During this period, High-Density surface Electromyography (HD-sEMG) signals have been employed for hand gesture identification, in which the performance of the classification process can be improved by using robust spatial features extracted from HD-sEMG signals. In this paper, several algorithms of spatial feature extraction have been proposed to increase the accuracy of the SVM classifier, while the histogram oriented gradient (HOG) has been used to achieve this mission. So, several feature sets have been extracted from HD-sEMG signals such as; features extracted based on HOG denoted by (H); features have been generated by combine intensity feature with H features denoted as (HI); features have been generated by combine average intensity with H features denoted as (AIH). The proposed system has been simulated by MATLAB to calculate the accuracy of the classification process, in addition, the proposed system is practically validated in order to show the ability to use this system by amputees. The results show the high accuracy of the classifier in real-time which leads to an increase in the possibility of using this system as an artificial hand.