[HTML][HTML] Emotion recognition based on EEG feature maps through deep learning network
A Topic, M Russo - Engineering Science and Technology, an International …, 2021 - Elsevier
Emotion recognition using electroencephalogram (EEG) signals is getting more and more
attention in recent years. Since the EEG signals are noisy, non-linear and have non …
attention in recent years. Since the EEG signals are noisy, non-linear and have non …
[HTML][HTML] Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series
Assessing land-use following deforestation is vital for reducing emissions from deforestation
and forest degradation. In this paper, for the first time, we assess the potential of spatial …
and forest degradation. In this paper, for the first time, we assess the potential of spatial …
Hybrid CNN-LSTM approaches for identification of type and locations of transmission line faults
A Moradzadeh, H Teimourzadeh… - International Journal of …, 2022 - Elsevier
Timely and accurate detection of transmission line faults is one of the most important issues
in the reliability of the power systems. In this paper, in order to assess the effects of …
in the reliability of the power systems. In this paper, in order to assess the effects of …
Continuous human activity recognition with distributed radar sensor networks and CNN–RNN architectures
Unconstrained human activities recognition with a radar network is considered. A hybrid
classifier combining both convolutional neural networks (CNNs) and recurrent neural …
classifier combining both convolutional neural networks (CNNs) and recurrent neural …
A 2D CNN-LSTM hybrid algorithm using time series segments of EEG data for motor imagery classification
J Wang, S Cheng, J Tian, Y Gao - Biomedical Signal Processing and …, 2023 - Elsevier
Motor imagery-based brain–computer interaction (MI-BCI) converts human neural activity
into computational information, often used as commands, by recognizing …
into computational information, often used as commands, by recognizing …
Deep CNN hyperparameter optimization algorithms for sensor-based human activity recognition
S Raziani, M Azimbagirad - Neuroscience Informatics, 2022 - Elsevier
Human activity recognition (HAR) is an active field of research for the classification of human
movements and applications in a wide variety of areas such as medical diagnosis, health …
movements and applications in a wide variety of areas such as medical diagnosis, health …
Human activity classification based on point clouds measured by millimeter wave MIMO radar with deep recurrent neural networks
Y Kim, I Alnujaim, D Oh - IEEE Sensors Journal, 2021 - ieeexplore.ieee.org
We investigate the feasibility of classifying human activities measured by a MIMO radar in
the form of a point cloud. If a human subject is measured by a radar system that has a very …
the form of a point cloud. If a human subject is measured by a radar system that has a very …
Scattering centers to point clouds: A review of mmWave radars for non-radar-engineers
Recently, mmWave radars have been gaining popularity, thanks to their low cost, ease of
use and high-resolution sensing. In this paper, we provide a review of the mmWave radar …
use and high-resolution sensing. In this paper, we provide a review of the mmWave radar …
Prediction of the remaining useful life of cutting tool using the Hurst exponent and CNN-LSTM
To enhance production quality, productivity and energy consumption, it is paramount to
predict the remaining useful life (RUL) of a cutting tool accurately and efficiently. Deep …
predict the remaining useful life (RUL) of a cutting tool accurately and efficiently. Deep …