EEG signals for real-time emotion identification are crucial for affective computing and human-computer interaction. The current emotion recognition models, which rely on a small number of emotion classes and stimuli ...
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EEG signals for real-time emotion identification are crucial for affective computing and human-computer interaction. The current emotion recognition models, which rely on a small number of emotion classes and stimuli like music and images in controlled lab conditions, have poor ecological validity. Furthermore, identifying relevant EEG signal features is crucial for efficient emotion identification. According to the complexity, non-stationarity, and variation nature of EEG signals, which make it challenging to identify relevant features to categorize and identify emotions, a novel approach for feature extraction and classification concerning EEG signals is suggested based on invariant wavelet scattering transform (WST) and support vector machine algorithm (SVM). The WST is a new time-frequency domain equivalent to a deep convolutional network. It produces scattering feature matrix representations that are stable against time-warping deformations, noise-resistant, and time-shift invariant existing in EEG signals. So, small, difficult-to-measure variations in the amplitude and duration of EEG signals can be captured. As a result, it addresses the limitations of the previous feature extraction approaches, which are unstable and sensitive to time-shift variations. In this paper, the zero, first, and second order features from DEAP datasets are obtained by performing the WST with two deep layers. Then, the PCA method is used for dimensionality reduction. Finally, the extracted features are fed as inputs for different classifiers. In the classification step, the SVM classifier is utilized with different classification algorithms such as k-nearest neighbours (KNN), random forest (RF), and AdaBoost classifier. This research employs a principal component analysis (PCA) approach to reduce the high dimensionality of scattering characteristics and increase the computational efficiency of our classifiers. The proposed method is performed across four different emotional classific
Nowadays, terrestrial broadcasting enables to receive content anytime and everywhere. People can obtain information both with a portable or desktop receiver, which include pocket-sized devices as well as high-end Hi-F...
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In the game of basketball in general and in the game of 3x3 basketball, the emphasis is on general physical training and not on specific training, thus allowing the players to have a multilateral training. In order to...
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This letter proposes a cavity-backed substrate-integrated-waveguide dual-feed self-diplexing multiple-input–multiple-output (MIMO) antenna for Internet of Things applications. To implement a self-diplexing operation,...
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To pursue a move towards sustainability in electronics manufacturing, recently we presented a biodegradable printed circuit board (PCB) alternative substrate, where polylactic-Acid (PLA) and flax-Textile reinforcement...
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The effect of the doping of TiO2 nanoparticles on the electrochemical migration (ECM) characteristics of low-Ag lead-free SAC0807 solder alloy was examined by the means of water drop (WD) tests in 0.1 M NaCl solution ...
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In this paper we propose an autoencoder-based End-to-End (E2E) Deep Learning for symbol decoding in fiber optical communications systems. The autoencoder was trained with data encoded in two different ways. First, one...
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This publication reviews asynchronous radiolocation methods, emphasizing their analytical descriptions. Radiolocation systems evolve through the integration of advancements from multiple scientific disciplines, includ...
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In this article a method for detecting the presence of trains on tracks by analyzing the changes in the cell-specific reference symbols parameters in the downlink signal from nearby LTE base station operating in trans...
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In this article a method for detecting the presence of trains on tracks by analyzing the changes in the cell-specific reference symbols parameters in the downlink signal from nearby LTE base station operating in transmit diversity mode is proposed. The signal processing method to obtain waveforms showing correlation with the presence of trains is described with experimental results on trains detection using real LTE downlink signals from commercial networks. The main outcome of the investigation is that even in case of signal reception using receiver with limited long term stability, which doesn’t allow to track absolute phase values of reference symbols, difference between received phases of symbols transmitted using different antenna ports in base station may be highly correlated with presence of trains on tracks. Proposed reference symbols analysis may be alternative to detection of objects in the environment using bistatic passive radar principle, which couldn’t be used in proposed measurement setup due to low performance of bistatic radar detection using single antenna receiver.
This study is motivated by two key considerations: the significant benefits mobile applications offer individuals and businesses, and the limited empirical research on usability challenges. To address this gap, we con...
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