Compared with thin‐film morphology, 1D perovskite structures such as micro/nanowires with fewer grain boundaries and lower defect density are very suitable for high‐performance photodetectors with higher stability. Although the stability of perovskite microwire‐based photodetectors has been substantially enhanced in comparison with that of photodetectors based on thin‐film morphology, practical applications require further improvements to the stability before implementation. In this study, a template‐assisted method is developed to prepare methylammonium lead bromide (MAPbBr3) micro/nanowire structures, which are encapsulated in situ by a protective hydrophobic molecular layer. The combination of the protective layer, high crystalline quality, and highly ordered microstructures significantly improve the stability of the MAPbBr3 single‐crystal microwire arrays. Consequently, these MAPbBr3 single‐crystal microwire‐array‐based photodetectors exhibit significant long‐term stability, maintaining 96% of the initial photocurrent after 1 year without further encapsulation. The lifetime of such photodetectors is hence approximately four times longer than that of the most stable previously reported perovskite micro/nanowire‐based photodetector; this is thought to be the most stable perovskite photodetector reported thus far. Furthermore, this work should contribute further toward the realization of perovskite 1D structures with long‐term stability.
The automatic detection of epileptic seizures from electroencephalography (EEG) signals is crucial for the localization and classification of epileptic seizure activity. However, seizure processes are typically dynamic and nonstationary, and thus, distinguishing rhythmic discharges from nonstationary processes is one of the challenging problems. In this paper, an adaptive and localized time-frequency representation in EEG signals is proposed by means of multiscale radial basis functions (MRBF) and a modified particle swarm optimization (MPSO) to improve both time and frequency resolution simultaneously, which is a novel MRBF-MPSO framework of the time-frequency feature extraction for epileptic EEG signals. The dimensionality of extracted features can be greatly reduced by the principle component analysis algorithm before the most discriminative features selected are fed into a support vector machine (SVM) classifier with the radial basis function (RBF) in order to separate epileptic seizure from seizure-free EEG signals. The classification performance of the proposed method has been evaluated by using several state-of-art feature extraction algorithms and other five different classifiers like linear discriminant analysis, and logistic regression. The experimental results indicate that the proposed MRBF-MPSO-SVM classification method outperforms competing techniques in terms of classification accuracy, and shows the effectiveness of the proposed method for classification of seizure epochs and seizure-free epochs.
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