This paper proposes a new method for signal classification based on combination of deep-learning (DL) image classifiers and recently introduced nonlinear spectral analysis technique called quantile-frequency (QFA). The QFA converts one-dimensional into two-dimensional representation quantile periodograms (QPER) which represent the signal’s oscillatory behavior in frequency domain at different q...

Mental fatigue is a major public health issue worldwide that common among both healthy and sick people. In the literature, various modern technologies, together with artificial intelligence techniques, have been proposed. Most techniques consider complex biosignals, such as electroencephalogram, electro-oculogram or classification of basic heart rate variability parameters. Additionally, most s...

In autonomous driving vehicles, the emergency braking system uses lidar or radar sensors to recognize surrounding environment and prevent accidents. The conventional classifiers based on data using deep learning are single input structures range–Doppler maps micro-Doppler. Deep with a structure has limitations in improving classification performance. this paper, we propose multi-input classifie...

Abstract This paper proposes a novel convolutional neural network (CNN) fusion method for electroencephalography (EEG) motor imagery (MI) signal classification. The is named MFBF, which stands multifrequency band fusion. MFBF relies on filtering the input with different frequency bands and feeding each to duplicate of CNN model; then, all duplicates are concatenated form model. also introduces ...