The neurological disorder which is associated with the abnormal electrical activity generated from the brain causing seizures is typically termed as epilepsy. The automated detection and classification of epilepsy based on the analysis of the electroencephalogram (EEG) signal are highly required for its early diagnosis. In this paper, we have developed an EEG-rhythm specific Taylor-Fourier filter-bank implemented with O-splines for the detection and classification of epilepsy from the EEG signal. The energy features are evaluated from the Taylor-Fourier sub-band signals of the EEG signal. The classifiers such as K-nearest neighbor (KNN) and least square support vector machine (SVM) are employed for the classification of normal, seizure-free and seizure from the Taylor-Fourier EEG-band energy (TFEBE) features. The experimental results demonstrate that, for the classification of normal, seizure-free, and seizure classes, the least square SVM classifier has an overall accuracy value of 94.88% using the EEG signals from the Bonn university database. The proposed EEG rhythm specific Taylor-Fourier filter-bank with O-splines can be implemented in real-time for the detection of epileptic seizures from EEG signals.

In this study, a novel algorithm for detecting and classifying faults in high‐voltage transmission lines is proposed. The algorithm is based on the discrete wavelet transform (DWT) and singular value decomposition (SVD). The DWT is used for extracting the currents’ high‐frequency components under fault conditions. Signals under each fault condition are scaled in frequency, in order to build a wavelet matrix. By means of the SVD, the maximum singular value is calculated and employed in this proposal. The attained results exhibit that the maximum singular value represents a good indicator for the issue. This novel approach for detecting and classifying faults in power systems is called maximum wavelet singular value. Phase‐to‐ground, two‐phase to ground, and three‐phase faults’ simulations under different fault impedances are carried out by DIgSILENT Power Factory. The analysed fault conditions are evaluated demonstrating that the proposal reduces the computational burden and the time detection.

The digital Taylor-Fourier transform (DTFT) is used to identify low-frequency electromechanical modes in power systems. The identification process is based on the time-frequency analysis of nonlinear signals that arise after a large disturbance. The DTFT creates a signal decomposition, from which mono-component signals are extracted by spectral analysis using a filter bank. This analysis is accomplished through sliding-window data, which is updated each sample, yielding estimates of the reconstructed signal and providing information of its instantaneous damping and frequency. Results demonstrate the applicability of the proposition.