01. Araştırma Çıktıları | TR-Dizin | WoS | Scopus | PubMed

Permanent URI for this communityhttps://hdl.handle.net/20.500.14365/1

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Consumer Preference Estimation Using Eeg Signals and Deep Learning
(Institute of Electrical and Electronics Engineers Inc., 2024) Ceylan, B.; Çekiç, Y.; Akan, Aydın
Emotion estimation is an extremely critical and current research topic for human-computer interaction. In this study, a liking estimation method using electroencephalogram (EEG) signals is proposed to be used in neuromarketing studies. EEG data recorded while participants watch the advertisement videos of two different automobile brands are processed with deep learning techniques to estimate their liking status. After watching the videos, participants were presented with selected image sections from the advertisements (front view, console, side view, rear view, stop lamp, brand logo and front grille) and were asked to rate their liking by scoring from 1 to 5. EEG signals corresponding to these regions were converted into a two dimensional and RGB colored image using the short-time Fourier transform (STFT) method, and liking status was estimated using Deep Learning. The successful results obtained show that the proposed method can be used in neuromarketing studies. © 2024 IEEE.
Citation - Scopus: 3
Detection of Attention Deficit Hyperactivity Disorder by Using Eeg Feature Maps and Deep Learning
(European Signal Processing Conference, EUSIPCO, 2023) Akbuğday, Burak; Bozbas, O. A.; Cura, O.K.; Pehlivan, Sude; Akan, Aydın
Attention deficit hyperactivity disorder (ADHD) is a mental disorder that affects the behavior of the persons, and usually onsets in childhood. ADHD generally causes impulsivity, hyperactivity, and inattention which impairs day-to-day life even in the adulthood if left undiagnosed and untreated. Although various guidelines for diagnosis of ADHD exist, a universally accepted objective diagnostic procedure is not established. Since current diagnosis of ADHD heavily relies on the expertise of healthcare providers, an EEG Topographic Feature Map (EEG-FM) based method is proposed in this study which aims to objectively diagnose ADHD. 6 different features extracted from EEG recordings acquired from 33 participants, 15 ADHD patients and 18 control subjects, converted into EEG-FM images and fed into a convolutional neural network (CNN) based classifier. Results indicate that the proposed method can accurately classify ADHD patients with up to 99% accuracy, precision, and recall. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.
Citation - WoS: 15
Citation - Scopus: 31
Development of a High Gain Fss Reflector Backed Monopole Antenna Using Machine Learning for 5g Applications
(Electromagnetics Academy, 2021) Nakmouche M.F.; Allam A.M.M.A.; Fawzy D.E.; Lin D.-B.
—This work is devoted to the development of a high gain Frequency Selective Surface (FSS) reflector backed monopole antenna using Machine Learning (ML) techniques for 5G applications. It analyzes and solves the complexity of the determination of the optimum position of the FSS reflector and the ground dimension of the monopole in this composite antenna structure since there are no solid and standard formulations for the computation of these two parameters. ML modelling is involved in the development process for the sake of gain enhancement. It is applied to get the optimum position of the FSS reflector layer and the ground dimension of the monopole antenna. The proposed antenna structure is 50 mm × 50 mm, implemented on a Rogers 5880 substrate (thickness = 1.6 mm). Two different patch antenna structures, with and without FSS, are developed and considered in the current work. The antenna performance in terms of operating frequency, return loss, and gain is analysed using the finite element methods. The design is optimized for a targeting frequency band operating at 6 GHz (5.53 GHz to 6.36 GHz), which is suitable for 5G Sub-6 GHz applications. The obtained results show that the integration of the FSS layer below the antenna structure provides a simple and efficient method to obtain a low-profile and high-gain antenna. Finally, the proposed design is fabricated and measured, and a good agreement between the simulated and measured results is obtained. A comparison with similar studies in the literature is presented and shows that the current design is more compact in size, and the obtained radiation efficiency and gain are higher than other designs. © 2021, Electromagnetics Academy. All rights reserved.
Citation - WoS: 1
Citation - Scopus: 2
Development of Ultra-Wideband Textile-Based Metamaterial Absorber for Mm-Wave Band Applications
(Institute of Electrical and Electronics Engineers Inc., 2022) Akarsu G.; Taher H.; Zengin E.B.; Nakmouche M.F.; Fawzy D.E.; Allam A.M.M.A.; Cleary F.
This work presents a state-of-the-art development of an ultrawide absorber for wearable smart electronic textile applications. The design is based on a novel cell geometry that is previously developed and applied for RF energy harvesting applications. Different textile types were considered in this study, namely, Felt, Denim and Polyester and the achieved-10 dB reflective fractional bandwidths are about 42.828%, 43.65%, and 42.834% respectively. A comparison with traditional counterparts (FR-4 and Rogers dielectrics) shows that the bandwidth exhibited by textile materials is greatly wider. The bending effect of the textile materials is considered in this study and found that the-10 dB bandwidth is inversely proportional with the decrease in the surface curvature of the material. Compared to the currently developed absorbers and similar structures reported in the literature show that the current design is more compact, lighter, and more efficient in terms of the absorptivity. The current results can be considered as starting promising steps for the development of ultra-wideband electronic textiles-based applications such as energy harvesting, health monitoring, smart materials, sensors, and infrared camouflage. © 2022 European Association for Antennas and Propagation.

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