Special Issue on Advances in Deep Learning

Gragnaniello, Diego; Bottino, A.; Cumani, Sandro; Kim, Wonjoon

doi:10.3390/app10093172

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…In addition, deep learning (DL) researches [ 31 , 32 ] have recently shown notable progress in biomedical signal analysis especially classification-based anomaly detection. However, DL [ 33 ] is now the fastest sub-field of ML technology [ 34 ] based on the artificial neural networks (ANNs) [ 35 ]. Interestingly, DL networks offer great potential for biomedical signals analysis through the simplification of raw input signals (i.e., through various steps including feature extraction, denoising, and feature selection) and the improvement of the classification results.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

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Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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“…Recently, machine learning and deep learning proved their talents in optimizing radio frequency designs and they can be a good solution for modeling and optimizing MIMO antennas [14]. They can model the complex designs by considering the relationships between the input and output data that in turn can be design parameters and/or design specifications, respectively [15]. This paper presents an automatic methodology for designing and optimizing implanted MIMO antennas; to the best of authors' knowledge, the proposed method is for the very first time reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Assisted Automatic Methodology for Implanted MIMO Antenna Designs on Large Ground Plane

2021

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This paper provides a novel methodology for designing implanted multiple-input and multiple-output (MIMO) antennas in the automatic fashion. The proposed optimization consists of two sequential phases for firstly configuring the geometry of an implanted MIMO antenna and then sizing the design parameters through the hierarchy top-down optimization (TDO) and regression deep neural network (DNN), respectively. It tackles the difficulty in constructing the structure of antennas and also provides optimal values for the determined variables, sufficiently. This methodology results in valid electromagnetic (EM)-verified post-layout generation that is ready-to-fabricate. The effectiveness of the proposed optimization-oriented method is verified by designing and optimizing the implanted MIMO antenna in the frequency band of 4.34–4.61 GHz and 5.86–6.64 GHz suitable for medical applications at the emerging wireless band. For our design, we employ the actual biological tissues as bone, liquid (%1 sodium chloride, %40 sugar in distilled water), and plexiglass surroundings with a bio-compatible substrate, as aluminium oxide on a large ground plane, that is suitable to be used in a particular biomedical applications involving smart implants.

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Special Issue on Advances in Deep Learning

Cited by 2 publications

References 37 publications

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Deep Learning Assisted Automatic Methodology for Implanted MIMO Antenna Designs on Large Ground Plane

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