Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/11

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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, Ibrahim; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
In 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.
Citation - WoS: 3
Citation - Scopus: 3
A 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.; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
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.
Adaptive Limited Feedback Scheme for Stream Selection Based Interference Alignment in Heterogeneous Networks
(IEEE, 2016) Beyazıt, Esra Aycan; Özbek, Berna; Le Ruyet,D.; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
This paper presents a stream selection based interference alignment approach with imperfect channel state information for heterogeneous networks. The proposed algorithm performs the selection of a stream sequence among a predetermined set of sequences. Those selected sequences are the ones that mostly contribute to the sum rate when performing the exhaustive search. These stream sequences form a regular structure where the first stream is associated to a pico user. The effect of imperfect channel state information on the proposed algorithm is analyzed and a bit allocation scheme is proposed by deriving an upper bound on the rate loss due to quantization. © 2016 IEEE.
Citation - WoS: 2
Citation - Scopus: 1
Liquid Metal-Controlled Dual-Band Doppler Radar for Enhanced Velocity Measurement
(IEEE, 2024) Karatay, Anıl; Yaman, Fatih; 01. Izmir Institute of Technology; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering
Doppler 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.
Carrier Frequency Offset Based Shared Randomness for Secure Transmission in M-Psk Noma
(IEEE, 2023) Göztepe, Caner; Karabulut Kurt, Güneş; Özbek, Berna; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
Power domain non-orthogonal multiple access (NOMA) enhances spectral efficiency by superposing multiple users in the same time-frequency resource block at the expense of exposing the users' data. However, current approaches to improve the secrecy levels of users are limited to rate reduction. This paper proposes a secure NOMA system based on the shared randomness extracted from the reciprocal carrier frequency offsets (CFOs) between the transmitter-receiver pairs for M-ary phase-shift keying. As multiple users will have physically separated oscillators, it will result in independent CFOs among users. This randomness is used to introduce a constellation rotation in the transmitted symbols. We show that under ideal CFO estimates, the proposed approach achieves perfect secrecy among all NOMA users without introducing any rate reduction. We also demonstrate the practical applicability of the proposed approach by using a software-defined radio-based test bed. © 2023 IEEE.
Citation - WoS: 3
Citation - Scopus: 3
A 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, Mehmet; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
The 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.
Detection Scheme for Pnc-Based Cell-Free Mimo Systems
(IEEE, 2023) Cumalı, İrem; Özbek, Berna; Karabulut Kurt, Güneş; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
Cell-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. © 2023 IEEE.
Citation - Scopus: 2
Compact Proton Accelerator in Uhf Band at Kahvelab
(JACoW Publishing, 2022) Esen, S.; Adıgüzel, A.; Koçer, O.; Çağlar, A.; Çelebi, E.; Öz, S.; Özcan, V.E.; Karatay, Anıl; Yaman, Fatih; Yılmaz, Hasan Önder; 01. Izmir Institute of Technology; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering
Proton Test Beam at KAHVELab (Kandilli Detector, Accelerator and Instrumentation Laboratory) project aims to design and produce a radio frequency quadrupole (RFQ) operating at 800 MHz in Istanbul, Turkey using the local resources. The beamline consists of a proton source, a low energy beam transport (LEBT) line including the beam diagnostic section and the RFQ cavity itself. This RFQ is 4-vane, 1-meter-long cavity to accelerate the 20 keV beam extracted from plasma ion source to 2 MeV. Its engineering prototype is already produced and subjected to mechanical, low power RF and vacuum tests. In this study, the results of the first test production, especially the bead-pull test setup will be discussed. © 2022 Proceedings - Linear Accelerator Conference, LINAC. All rights reserved.
Citation - Scopus: 2
Rf Measurements and Tuning of the Test Module of 800 Mhz Radio-Frequency Quadrupole
(JACoW Publishing, 2022) Kılıçgedik, A.; Adıgüzel, A.; Esen, S.; Baran, B.; Çağlar, A.; Çelebi, E.; Özcan, V. E.; Kaya, U.; Türemen, G.; Ünel, N. G.; Yaman, Fatih; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
The 800 MHz RFQ (radio-frequency quadrupole), developed and built at KAHVElab (Kandilli Detector, Accelerator and Instrumentation Laboratory) at Bogazici University in Istanbul, Turkey, has been designed to provide protons that have an energy of 2 MeV within only 1 m length. The RFQ consists of two modules and the test module of RFQ was constructed. The algorithm developed by CERN, based on the measurements generated by the tuner settings estimated through the response matrix [1, 2, 3], has been optimized for a single module and 16 tuners. The desired field consistent with the simulation was obtained by bead-pull measurements. In this study, we present low-power rf measurements and field tuning of the test module. © 2022 Proceedings - Linear Accelerator Conference, LINAC. All rights reserved.
Citation - WoS: 5
Citation - Scopus: 8
Using Chemosensory-Induced Eeg Signals To Identify Patients With de Novo Parkinson's Disease
(Elsevier Sci Ltd, 2024) Olcay, Orkan; Onay, Fatih; Ozturk, Guliz Akin; Oniz, Adile; Ozgoren, Murat; Hummel, Thomas; Guducu, Cagdas; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
Objective: 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.
Görgül kip ayrıştırması kullanılarak optik faz kırınımında hassasiyet iyileştirilmesi
(IEEE, 2023) Ataç, Enes; Dinleyici, Mehmet Salih; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
Phase diffraction is a potent property used in transparent dielectric film characterization. The measured diffraction pattern on the camera is evaluated by matching numerically computed diffraction patterns to determine the optical properties of the ultra-thin films (refractive index, thickness, etc.). However, the obtained diffraction data is not only a nonlinear and non-stationary signal but also exhibits micron-scale variations, thus limiting the measurement accuracy. Therefore, it is challenging to identify shifts in minima and deviations in amplitude on diffraction data to extract information about the optical properties of phase objects. In this study, it is aimed to improve the thickness sensitivity of the system by applying Empirical Mode Decomposition (EMD) to plane wave-based near-field phase diffraction data. Since EMD is very sensitive to abrupt changes in the signal due to the spatial frequency components, the nanoscale variations in the film thickness become more observable and detectable. Experimental outputs and numerical simulations show that the decomposition increases the thickness sensitivity comparing the classical matching technique.
Dalgacık gürültü giderme yöntemiyle mikrodalga bileşen karakterizasyonunun iyileştirilmesi
(IEEE, 2023) Karatay, Anıl; Olcay, Bilal Orkan; Yaman, Fatih; 01. Izmir Institute of Technology; 01.01. Units Affiliated to the Rectorate; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering
In this study, an efficient approach is presented to improve the characterization of various microwave components commonly used in communication and radar applications, such as antennas and power dividers. The components were initially simulated and then fabricated using the Computer Simulation Technology (CST) software. Vector Network Analyzer (VNA) measurements of the fabricated components were performed using a low-cost but noisy coaxial cable, and the measurement results were processed using a wavelet-based noise reduction method. For comparison purposes, the Haar and Daubechies-4 (DB4) wavelet functions were applied separately, and the results were examined. It was observed that the correlation and root mean square error between the ideal and measurement results improved in a positive direction with the noise reduction application. This approach provides significant cost and labor advantages, particularly when expensive elements such as gold and silver are used in coaxial cables that are physically free from noise. The experimental and numerical results show good agreement between the ideal simulation results and the filtered measurement results.
Algıda gecikme ve kısa-ömürlü senkronizasyon temelli yeni bir hayali motor aktivite tanıma yaklaşımı
(IEEE, 2023) Olcay, B. Orkan; Karaçalı, Bilge; 01.01. Units Affiliated to the Rectorate; 03.05. Department of Electrical and Electronics Engineering; 01. Izmir Institute of Technology; 03. Faculty of Engineering
This study proposes a novel approach for investigating a brain-computer interface that considers the temporal organization of brain activity, explicitly accounting for perception latency. To this end, we aligned the onset of task periods with the concurrence of left parietal and parieto-occipital electrodes to obtain the timings of perception latencies. Then, activity-specific synchronization timings between channel pairs were calculated using the time-aligned task periods. The perception latency and activity-specific synchronization timings were subsequently used for feature extraction and classification. The proposed approach achieved significantly better performance when comparing the proposed approach with the method that did not account for the perception latency
Citation - Scopus: 2
Parkinson hastalığı sınıflandırmasına yönelik ivmeölçer tabanlı zamanlama analizi
(IEEE, 2023) Karaçalı, Bilge; Onay, Fatih; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
Parkinson's disease is a neurodegenerative disorder caused by dopamine deficiency in the basal ganglia, resulting in cognitive and motor impairments. In this study, accelerometer signals were used to estimate the delay time between the command to start pedaling and the actual movement onset in three groups: healthy individuals (n=13), Parkinson's disease patients (n=13), and patients with freezing of gait symptoms (n=13). Features were extracted from the delay time distributions for each participant and subjected to a triple classification. Linear support vector machine achieved a classification accuracy of 69.2% for all participants. Notably, the average time to start pedaling was found to be significantly different among the three groups, and accelerometer-based timing analysis could be used as a diagnostic tool to assist clinical tests.
Citation - WoS: 21
Citation - Scopus: 25
A New Method for Gan-Based Data Augmentation for Classes With Distinct Clusters
(Pergamon-Elsevier Science Ltd, 2024) Kuntalp, Mehmet; Düzyel, Okan; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
Data 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.
Citation - WoS: 2
Citation - Scopus: 5
Adaptive 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, Abdurrahman; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
Breast 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.
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 Salih; 03.05. Department of Electrical and Electronics Engineering; 01. Izmir Institute of Technology; 03. Faculty of Engineering
Accurate 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.
Corrections To “massive Mimo-Noma Based Mec in Task Offloading for Delay Minimization”
(IEEE, 2023) Yılmaz, Saadet Simay; Özbek, Berna; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
[No abstract available]
A Framework for Physical Layer Network Coding With Multiple Antennas for Bpsk
(IEEE, 2023) İlgüy, Mert; Özbek, Berna; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
Physical layer network coding (PNC) is combined with multiple antennas to increase the spectral efficiency of wireless communication systems. In this work, we present a PNC framework including both uplink and downlink for binary phase shift keying (BPSK). In the uplink, we propose a scheme for detecting network-coded symbol (NCS) with reduced complexity. For the downlink, we propose a transmission scheme of NCS through maximum ratio transmission (MRT) by defining the precoding vector as an average of users' channels. The bit-error-rate (BER) performances and the comparison results with the conventional scheme in both downlink and uplink are provided for the proposed low-complexity PNC framework.
Citation - WoS: 2
Citation - Scopus: 2
Subwavelength Thickness Characterization of Curved Dielectric Films Exploiting Spatially Structured Entangled Photons
(Optica Publishing Group, 2023) Ataç, Enes; Dinleyici, Mehmet Salih; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
Precise determination of thin dielectric film optical properties is a critical issue for fiber optic sensor technologies. However, conventional methods for the optical characterization of these films not only are generally complex and tedious processes on curved surfaces but also require well-calibrated and overly sophisticated devices. We, on the other hand, propose a novel and practical quantum-based phase diffraction scheme to characterize the thickness of ultra-thin transparent dielectric films coated on an optical fiber beyond the classical diffraction limits in this paper. The approach is implemented by evaluating the effect of thickness variations on the highly visible two-photon diffraction pattern's zero crossings and amplitudes. The mathematical model and numerical simulations con-tribute to a better understanding of how the spatially structured entangled photons improve thickness precision with the help of intensity correlations and a confocal aperture. To prove the impact of the proposed system, it is compared with the classical phase diffraction method in the literature via simulations. According to the results, the thickness of the transparent dielectric films can be accurately estimated below one-twentieth of the wavelength of interest. & COPY; 2023 Optica Publishing Group

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