Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
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Article Secrecy performance of full-duplex space-air integrated networks in the presence of active/passive eavesdropper, and friendly jammer(Wiley, 2024) Buyuksar, Ayse Betul; Erdoğan, Eylem; Altunbas, IbrahimIn this paper, a full-duplex (FD) space-air ground integrated network (SAGIN) system with passive and active eavesdroppers (PE/AE) and a friendly jammer (FJ) is investigated. The shadowing side information (SSI)-based unmanned aerial vehicle relay node (URN) selection strategy is considered to improve signal-to-interference plus noise power ratio (SINR) at the ground destination unit. To quantify the secrecy performance of the considered scenario, outage probability (OP), interception probability (IP), and transmission secrecy outage probability (TSOP) are investigated in the presence of FJ and PE/AE. The results have shown that aerial AE is an important threat since it can severely degrade the OP of the main transmission link. Furthermore, the FJ can decrease the IP of the eavesdropper by causing interference with the cost of power consumption of URNs. Simulations are performed to verify the theoretical findings.Article Citation - WoS: 5Citation - Scopus: 6A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification(MDPI, 2024) Gürkan Kuntalp, D.; Özcan, N.; Düzyel, Okan; Kababulut, F.Y.; Kuntalp, M.The correct diagnosis and early treatment of respiratory diseases can significantly improve the health status of patients, reduce healthcare expenses, and enhance quality of life. Therefore, there has been extensive interest in developing automatic respiratory disease detection systems. Most recent methods for detecting respiratory disease use machine and deep learning algorithms. The success of these machine learning methods depends heavily on the selection of proper features to be used in the classifier. Although metaheuristic-based feature selection methods have been successful in addressing difficulties presented by high-dimensional medical data in various biomedical classification tasks, there is not much research on the utilization of metaheuristic methods in respiratory disease classification. This paper aims to conduct a detailed and comparative analysis of six widely used metaheuristic optimization methods using eight different transfer functions in respiratory disease classification. For this purpose, two different classification cases were examined: binary and multi-class. The findings demonstrate that metaheuristic algorithms using correct transfer functions could effectively reduce data dimensionality while enhancing classification accuracy. © 2024 by the authors.Article Citation - WoS: 3Citation - Scopus: 4Liquid Metal-Controlled Dual-Band Doppler Radar for Enhanced Velocity Measurement(IEEE, 2024) Karatay, Anıl; Yaman, FatihDoppler radars, which are critical instruments for velocity measurement, may need to be reconfigured to adapt to different environmental conditions or for ease of use. However, conventional electrical, optical, and physical reconfiguration methods often come with several disadvantages such as deteriorated radiation pattern, reduced radiation efficiency, and high cost. Therefore, the aim of this article is to integrate microwave components that can be controlled using liquid metal (LM) displacement into a Doppler radar to adjust its main lobe direction and operating frequency to the desired values and enhance the measurement capacity of the respective radar. Through this study, multiple parameters of an operational Doppler radar have been simultaneously adjusted using LM displacement exploitation for the first time, thus avoiding the shortcomings associated with conventional reconfiguration methods. To achieve this objective, initially, a back-to-back Vivaldi antenna operating at 2.45 GHz is designed, and beam switching ability is imparted to the structure using the LM displacement method. Subsequently, various techniques are used to convert the structure into a dual-band antenna capable of simultaneous operation at 2.45 and 5.8 GHz, ensuring the desired beam switching feature at both the frequencies. In addition, a power divider capable of switching between the two operating frequencies through LM assistance is proposed, and its integration into the radar system enables the control of both main lobe direction and frequency using the proposed method.Article Citation - WoS: 4Citation - Scopus: 4A New Shapley-Based Feature Selection Method in a Clinical Decision Support System for the Identification of Lung Diseases(MDPI, 2023) Kababulut, Fevzi Yasin; Kuntalp, Damla Gurkan; Düzyel, Okan; Özcan, Nermin; Kuntalp, MehmetThe aim of this study is to propose a new feature selection method based on the class-based contribution of Shapley values. For this purpose, a clinical decision support system was developed to assist doctors in their diagnosis of lung diseases from lung sounds. The developed systems, which are based on the Decision Tree Algorithm (DTA), create a classification for five different cases: healthy and disease (URTI, COPD, Pneumonia, and Bronchiolitis) states. The most important reason for using a Decision Tree Classifier instead of other high-performance classifiers such as CNN and RNN is that the class contributions of Shapley values can be seen with this classifier. The systems developed consist of either a single DTA classifier or five parallel DTA classifiers each of which is optimized to make a binary classification such as healthy vs. others, COPD vs. Others, etc. Feature sets based on Power Spectral Density (PSD), Mel Frequency Cepstral Coefficients (MFCC), and statistical characteristics extracted from lung sound recordings were used in these classifications. The results indicate that employing features selected based on the class-based contribution of Shapley values, along with utilizing an ensemble (parallel) system, leads to improved classification performance compared to performances using either raw features alone or traditional use of Shapley values.Conference Object Detection Scheme for Pnc-Based Cell-Free Mimo Systems(IEEE, 2023) Cumali, Irem; Ozbek, Berna; Kurt, Gunes KarabulutCell-free multiple-input multiple-output (cell-free MIMO) is a promising concept to overcome inter-cell interference and avoid non-uniform data rates among users by combining the best features of ultra-dense networks and MIMO. Hence, cell-free MIMO can fulfill the increasing demand on data rate with uniformly good coverage for the sixth-generation (6G) wireless communications. In addition to that, physical-layer network coding (PNC) reduces the transmission delay since it requires only two time slots instead of four time slots to exchange information between two users. In this paper, we propose a novel scheme called PNC-based cell-free MIMO to improve reliability further while reducing the transmission delay. We demonstrate the effectiveness of the proposed scheme regarding the bit error rate in different system configurations. The proposed PNC-based cell-free MIMO achieves significantly lower error probability than the conventional cell-free MIMO.Article Citation - WoS: 6Citation - Scopus: 10Using Chemosensory-Induced Eeg Signals To Identify Patients With <i>de Novo</I> Parkinson's Disease(Elsevier Sci Ltd, 2024) Olcay, Orkan; Onay, Fatih; Ozturk, Guliz Akin; Oniz, Adile; Ozgoren, Murat; Hummel, Thomas; Guducu, CagdasObjective: Parkinson's disease (PD) patients generally exhibit an olfactory loss. Hence, psychophysical or electrophysiological tests are used for diagnosis. However, these tests are susceptible to the subjects' behavioral response bias and require advanced techniques for an accurate analysis. Proposed Approach: Using well-known feature extraction methods, we characterized chemosensory-induced EEG responses of the participants to classify whether they have PD. The classification was performed for different time intervals after chemosensory stimulation to see which temporal segment better separates healthy controls and subjects with de novo PD. Results: The performances show that entropy and connectivity features discriminate effectively PD and HC participants when olfactory-induced EEG signals were used. For these methods, discrimination is over 80% for segments 100-700 and 200-800 milliseconds after stimulus onset. Comparison with Existing Methods: We compared the performance of our framework with linear predictive coding, bispectrum, wavelet entropy-based methods, and TDI score-based classification. While the entropy- and connectivity-based methods elicited the highest classification performances for olfactory stimuli, the linear predictive coding-based method elicited slightly higher performance than our framework when the trigeminal stimuli were used. Conclusion: This is one of the first studies that use chemosensory-induced EEG signals along with different feature extraction methods to classify healthy subjects and subjects with de novo PD. Our results show that entropy and functional connectivity methods unravel the chemosensory-induced neural dynamics encapsulating critical information about the subjects' olfactory performance. Furthermore, time- and frequency-resolved feature analysis is beneficial for capturing disease-affected neural patterns.Article Citation - WoS: 27Citation - Scopus: 34A New Method for Gan-Based Data Augmentation for Classes With Distinct Clusters(Pergamon-Elsevier Science Ltd, 2024) Kuntalp, Mehmet; Düzyel, OkanData augmentation is a commonly used approach for addressing the issue of limited data availability in machine learning. There are various methods available, including classical and modern techniques. However, when applying modern data augmentation methods, such as Generative Adversarial Neural Networks (GANs), to a class specific data, the resulting data can exhibit structural discrepancies. This study explores a different use of GANs as a data augmentation method that solves this problem using the electrocardiogram (ECG) signals in the MITBIH arrhythmia dataset as the example. We begin by examining the cluster structure of a specific class using tDistributed Stochastic Neighbor (t-SNE) method. Based on this cluster structure, we propose a new method for applying GANs to augment data for that class. We assess the effect of our method in a classification task using 1-D Convolutional Neural Network (CNN), Support Vector Machine (SVM), One vs one classifier (Ovo), K-Nearest Neighbors (KNN), and Random Forest as the classifiers. The results demonstrate that our proposed method could lead to better classification performance if a specific class has distinct clusters when compared to normal use of GANs.Article Citation - WoS: 5Citation - Scopus: 7Adaptive Resizer-Based Transfer Learning Framework for the Diagnosis of Breast Cancer Using Histopathology Images(Springer, 2023) Düzyel, Okan; Çatal, Mehmet Sergen; Kayan, Ceyhun Efe; Sevinç, Arda; Gümüş, AbdurrahmanBreast cancer is a major global health concern, and early and accurate diagnosis is crucial for effective treatment. Recent advancements in computer-assisted prediction models have facilitated diagnosis and prognosis using high-resolution histopathology images, which provide detailed information on cancerous tissue. However, these high-resolution images often require resizing, leading to potential data loss. In this study, we demonstrate the effect of a learnable adaptive resizer for breast cancer classification using the BreakHis dataset. Our approach incorporates the adaptive resizer with various convolutional neural network models, including VGG16, VGG19, MobileNetV2, InceptionResnetV2, DenseNet121, DenseNet201, and EfficientNetB0. Despite producing visually less appealing images, the learnable resizer effectively improves classification performance. DenseNet201, when jointly trained with the adaptive resizer, achieves the highest accuracy of 98.96% for input images of 448x448 resolution. Our experimental results demonstrate that the adaptive resizer performs better at a magnification factor of 40x compared to higher magnifications. While its effectiveness becomes less pronounced as image resolution increases to 100x, 200x, and 400x, the adaptive resizer still outperforms bilinear interpolation. In conclusion, this study highlights the potential of adaptive resizers in enhancing performance for medical image classification. By outperforming traditional image resizing methods, our work contributes to the advancement of deep neural networks in the field of breast cancer diagnostics.Article Enhancing Thickness Determination of Nanoscale Dielectric Films in Phase Diffraction-Based Optical Characterization Systems With Radial Basis Function Neural Networks(IOP Publishing, 2023) Ataç, Enes; Karatay, Anıl; Dinleyici, Mehmet SalihAccurate determination of the optical properties of ultra-thin dielectric films is an essential and challenging task in optical fiber sensor systems. However, nanoscale thickness identification of these films may be laborious due to insufficient and protracted classical curve matching algorithms. Therefore, this experimental study presents an application of a radial basis function neural network in phase diffraction-based optical characterization systems to determine the thickness of nanoscale polymer films. The non-stationary measurement data with environmental and detector noise were subjected to a detailed analysis. The outcomes of this investigation are benchmarked against the linear discriminant analysis method and further verified by means of scanning electron microscopy. The results show that the neural network has reached a remarkable accuracy of 98% and 82.5%, respectively, in tests with simulation and experimental data. In this way, rapid and precise thickness estimation may be realized within the tolerance range of 25 nm, offering a significant improvement over conventional measurement techniques.Correction Corrections To “massive Mimo-Noma Based Mec in Task Offloading for Delay Minimization”(IEEE, 2023) Yılmaz, Saadet Simay; Özbek, Berna[No abstract available]