Signal Processing

This paper presents a dataset of brain Electroencephalogram (EEG) signals created when Malayalam vowels and consonants are spoken. The dataset was created by capturing EEG signals utilizing the OpenBCI Cyton device while a volunteer spoke Malayalam vowels and consonants. It includes recordings obtained from both sub-vocal and vocal. The creation of this dataset aims to support individuals who speak Malayalam and suffer from neurodegenerative diseases.

This study presents a method for detecting arc faults by combining load identification and MLP-SVM. The method addresses the issue of interfering loads on arc fault detection and the lack of significant arc fault features in some loads. Initially, the eigenvalues of the line currents for single and mixed loads are extracted in the time domain, both during arc fault and normal operation. Subsequently, load identification is performed using a complex matrix calculation method. After identification, an eigenmatrix and history matrix are created for each load.

 The timely and accurate diagnosis of severe faults in the high-speed train air compressor is crucial due to the potential for significant safety issues. In response to this problem, this paper proposes a high-speed train air compressor fault diagnosis method based on an improved complete ensemble empirical mode decomposition adaptive noise (ICEEMDAN) and t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm.

ABSTRACT Analysis of stock prices has been widely studied because of the strong demand among private investors and financial institutions. However, it is difficult to accurately capture the factors that cause fluctuations in stock prices, as they are affected by a variety of factors. Therefore, we used non-harmonic analysis, a frequency technique with at least  to more accurately than conventional analysis methods, to visualize the periodicity of the Nasdaq Composite Index stock price from January 4, 2010 to September 8, 2023.