Broken bars detection on induction motors has been a topic of interest in recent years. Its detection is important due to the fact that the failure is silent and the consequences it produces as power consumption increasing, vibration, introduction of spurious frequencies in the electric line, among others, can be catastrophic. In this paper, the use of motor current signature analysis and mathematical morphology to detect broken bars on induction motors under different mechanical load condition is analyzed. The proposed algorithm first identifies the motor load and then the motor condition. The statistical analysis of several tests under different motor loads (100%, 75%, 50%, and 25%) and motor condition (healthy, one broken bar, and two broken bars) is presented. The proposed method has been implemented in a field programmable gate array, to be used in real-time online applications. The algorithm obtained in average a 95% accuracy of failure detection.

Electroencephalograms (EEG) have shown to be a useful approach to measure the cognitive load in tasks where mental effort is involved. However, EEG signals present a high variability among subjects as well as a non-stationary behavior, so that distributions among samples of different subjects are mismatched. Methods based on Unsupervised Domain Adaptation (UDA) have been used as an effective solution to reduce such discrepancy, while the ones leveraged by deep learning (D-UDA) have improved the classification results over shallow approaches. However, most D-UDA methods assume that even though there are differences in marginal distributions between source and target domains, their conditional distributions remain fixed, which does not hold in many EEG databases. To address this problem, we propose a new D-UDA method, named Custom Domain Adaptation (CDA), which integrates Adaptive Batch Normalization (AdaBN) and Maximum Mean Discrepancy (MMD) into two independent deep neural networks in order to reduce the marginal and conditional distribution differences. CDA was compared with six popular D-UDA methods using a free-available dataset of cognitive loads and obtained an accuracy of $98.2\pm 2.67\%$, which outperformed these state-of-the-art methods.

An essential part of cloud computing, IoT, and in general the broad field of digital systems, is constituted by the mechanisms which provide access to a number of services or applications. Biometric techniques aim to manage the access to such systems based on personal data; however, some biometric traits are openly exposed in the daily life, and in consequence, they are not secret, e.g., voice or face in social networks. In many cases, biometric data are non-cancelable and non-renewable when compromised. This document examines the vulnerabilities and proposes hardware and software countermeasures for the protection and confidentiality of biometric information using randomly created supplementary information. Consequently, a taxonomy is proposed according to the operating principle and the type of supplementary information supported by protection techniques, analyzing the security, privacy, revocability, renewability, computational complexity, and distribution of biometric information. The proposed taxonomy has five categories: (1) biometric cryptosystems, (2) cancelable biometrics, (3) protection schemes based on machine learning or deep learning, (4) hybrid protection schemes, and (5) multibiometric protection schemes. Furthermore, this document proposes quantitative evaluation measures to compare the performance of protection techniques. Likewise, this research highlights the advantages of injective and linear mapping for the protection of authentication and identification systems, allowing the non-retraining of these systems when the protected biometric information is canceled and renewed. Finally, this work mentions commercial products for cancelable biometric systems and proposes future directions for adaptive and cancelable biometric systems in low-cost IoT devices.