Many damage detection strategies have been developed within the field of structural health monitoring, showing promising results in real-world applications. Most of them rely on the use of modal parameters to locate damage in structures. However, they are highly dependent on the process of extracting such characteristics, which contains uncertainties and needs to be adapted specifically for each structure. Recently, a special focus is being given to techniques based only on the use of raw structural acceleration measurements due to their relatively lower computational complexity manipulation. Thus, this work proposes an efficient feature extraction methodology for structural damage localization based on raw vibration signals. Furthermore, its ability to indicate damage quantification is also investigated. The proposed approach consists in extracting sensitive features from time, frequency, and quefrency domains. Then, percentile intervals are defined for each feature regarding the structures' healthy state. Finally, a damage index is estimated based on the number of outlier features of the structures' damaged state. In this sense, the sensor with the highest number of outlier features indicates the damage location. To assess this methodology, four different applications are studied: a numerical two-dimensional frame, a simply supported beam tested in laboratory, a three-dimensional frame also tested in laboratory, and the Tianjin Yonghe Bridge in China. Results show that the proposed approach is not only able to correctly indicate damage locations but also to give insights about their magnitudes.

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Epilepsy is a neurological disorder, where there is a cluster of brain cells that behave in a hyperexcitable manner, the individual can promote injuries, trauma or, in more severe cases, sudden death. Electroencephalogram (EEG) is the most used way to detect epileptic seizures. Therefore, more simplified methods of analysis of the EEG can help in the diagnosis and treatment of these individuals more quickly. In this study, we extracted pertinent EEG characteristics to assess the epileptic seizure period. We use Perceptron Multilayer artificial neural networks to classify the period of the crisis, obtaining a more efficient diagnosis. The multilayer neural network obtained an accuracy of 98%. Thus, the strategy of extracting characteristics and the architecture of the assigned network were sufficient for a rapid and accurate diagnosis of epilepsy.