Common spatial pattern (CSP) is shown to be an effective pre-processing algorithm in order to discriminate different classes of motor-based EEG signals by obtaining suitable spatial filters. The performance of these filters can be improved by regularized CSP, in which available prior information is added in terms of regularization terms into the objective function of conventional CSP. Variety of prior information can be used in this way. In this paper, we used time correlation between different classes of EEG signal as the prior information, which is clarified similarity between different classes of signal for regularizing CSP. Furthermore, the proposed objective function can be easily extended to more than two-class problems. We used three different standard datasets to evaluate the performance of the proposed method. Correlation-based CSP (CCSP) outperformed original CSP as well as the existing regularized CSP, Principle Component Cnalysis (PCA) and Fisher Discriminate Analysis (FDA) in both two-class and multi-class scenarios. The simulation results showed that the proposed method outperformed conventional CSP by 6.9% in 2-class and 2.23% in multi-class problem in term of mean classification accuracy.
Transition metal chalcogenides (TMCs) have gained worldwide interest owing to their outstanding renewable energy conversion capability. However, the poor mechanical flexibility of most existing TMCs limits their practical commercial applications. Herein, triggered by the recent and imperative synthesis of highly ductile α-Ag2S, an effective approach based on evolutionary algorithm and ab initio total-energy calculations for determining stable, ductile phases of bulk and two-dimensional Ag x Se1-x and Ag x Te1-x compounds was implemented. The calculations correctly reproduced the global minimum bulk stoichiometric P212121-Ag8Se4 and P21/c-Ag8Te4 structures. Recently reported metastable AgTe3 was also revealed but it lacks dynamical stability. Further single-layered screening unveiled two new monolayer P4/nmm-Ag4Se2 and C2-Ag8Te4 phases. Orthorhombic Ag8Se4 crystalline has a narrow, direct band gap of 0.26 eV that increases to 2.68 eV when transforms to tetragonal Ag4Se2 monolayer. Interestingly, metallic P21/c-Ag8Te4 changes to semiconductor when thinned down to monolayer, exhibiting a band gap of 1.60 eV. Present findings confirm their strong stability from mechanical and thermodynamic aspects, with reasonable Vickers hardness, bone-like Young's modulus (E) and high machinability observed in bulk phases. Detailed analysis of the dielectric functions ε(ω), absorption coefficient α(ω), power conversion efficiency (PCE) and refractive index n(ω) of monolayers are reported for the first time. Fine theoretical PCE (SLME method ∼11-28%), relatively high n(0) (1.59-1.93), and sizable α(ω) (104-105 cm-1) that spans the infrared to visible regions indicate their prospects in optoelectronics and photoluminescence applications. Effective strategies to improve the temperature dependent power factor (PF) and figure of merit (ZT) are illustrated, including optimizing the carrier concentration. With decreasing thickness, ZT of p-doped Ag-Se was found to rise from approximately 0.15-0.90 at 300 K, leading to a record high theoretical conversion efficiency of ∼12.0%. The results presented foreshadow their potential application in a hybrid device that combines the photovoltaic and thermoelectric technologies.
In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.