Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
Browse
58 results
Search Results
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: 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.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.Article Citation - WoS: 8Citation - Scopus: 11Delay Minimization for Massive Mimo Based Cooperative Mobile Edge Computing System With Secure Offloading(IEEE, 2022) Mümtaz, Rao; Yılmaz, Simay; Özbek, BernaMobile edge computing (MEC) has been envisioned as a promising technology for enhancing the computational capacities of mobile devices by enabling task offloading. In this paper, we present a novel framework for a cooperative MEC system by employing Massive Multiple-Input Multiple-Output (MIMO) and non-orthogonal multiple access (NOMA) technologies, including security aspects. Specifically, in the proposed cooperative MEC system, there is no strong direct transmission link between the cell-edge user and the MEC server; consequently, the user sends their tasks to the MEC server through the helpers at the cell-centers. In the proposed framework, we minimize the overall delay, including secure offloading under the constraints of computing capability and transmit power. The proposed algorithm minimizes the overall delay in downlink and uplink transmission while satisfying security constraints to solve the formulated problem. The simulation results show that Massive MIMO based NOMA improves the performance of the secure MEC system by employing more than one helper.Article Citation - WoS: 7Citation - Scopus: 8The Resilience of Massive Mimo Pnc To Jamming Attacks in Vehicular Networks(Institute of Electrical and Electronics Engineers Inc., 2021) Okyere, Bismark; Musavian, Leila; Özbek, Berna; Busari, Sherif A.; Gonzalez, JonathanIn this article, we investigate the resilience of Massive MIMO Physical Layer Network Coding (PNC) to jamming attack in both sub-6 GHz and millimeter-Wave (mmWave) systems in vehicular networks. Massive MIMO generally is resilient to jamming attacks, and we investigate the impact that PNC has on this resilience, if combined with Massive MIMO. The combination of Massive MIMO and PNC has shown a significant improvement in the bit error rate (BER) in our previous investigation. The corresponding framework is analysed against a barraging attack from a jammer, where the jamming channel is not known to the base station (BS), and the jammer can use any number of transmit antennas. Over Rayleigh channel, our simulation results reveal that Massive MIMO PNC performs better in the lower signal-to-noise ratio (SNR) regions to jamming attacks and this is achieved at twice the spectral efficiency. A similar performance is observed over mmWave channel.Article Citation - WoS: 10Citation - Scopus: 13On the Characterization of Cognitive Tasks Using Activity-Specific Short-Lived Synchronization Between Electroencephalography Channels(Elsevier, 2021) Olcay, B. Orkan; Özgören, Murat; Karaçalı, BilgeAccurate characterization of brain activity during a cognitive task is challenging due to the dynamically changing and the complex nature of the brain. The majority of the proposed approaches assume stationarity in brain activity and disregard the systematic timing organization among brain regions during cognitive tasks. In this study, we propose a novel cognitive activity recognition method that captures the activity-specific timing parameters from training data that elicits maximal average short-lived pairwise synchronization between electroencephalography signals. We evaluated the characterization power of the activity-specific timing parameter triplets in a motor imagery activity recognition framework. The activity-specific timing parameter triplets consist of latency of the maximally synchronized signal segments from activity onset Delta t, the time lag between maximally synchronized signal segments t, and the duration of the maximally synchronized signal segments w. We used cosine-based similarity, wavelet bi-coherence, phase-locking value, phase coherence value, linearized mutual information, and cross-correntropy to calculate the channel synchronizations at the specific timing parameters. Recognition performances as well as statistical analyses on both BCI Competition-III dataset IVa and PhysioNet Motor Movement/Imagery dataset, indicate that the interchannel short-lived synchronization calculated using activity-specific timing parameter triplets elicit significantly distinct synchronization profiles for different motor imagery tasks and can thus reliably be used for cognitive task recognition purposes. (C) 2021 Elsevier Ltd. All rights reserved.Article Citation - WoS: 10Citation - Scopus: 13User Selection for Noma Based Mimo With Physical Layer Network Coding in Internet of Things Applications(Institute of Electrical and Electronics Engineers Inc., 2022) Yılmaz, Saadet Simay; Özbek, Berna; İlgüy, Mert; Okyere, Bismark; Musavian, Leila; Gonzalez, JonathanNon-orthogonal multiple access (NOMA) based multiple-input multiple-output (MIMO), which has the potential to provide both massive connectivity and high spectrum efficiency, is considered as one of the efficient techniques for sixth generation (6G) wireless systems. In massive Internet of Things (IoT) networks, user-set selection is crucial for enhancing the overall performance of NOMA based systems when compared with orthogonal multiple access (OMA) techniques. In this paper, we propose a user-set selection algorithm for IoT uplink transmission to improve the sum data rate of the NOMA based MIMO systems. In order to exchange data between the selected IoT pairs, we propose to employ wireless physical layer network coding (PNC) to further improve the spectral efficiency and reduce the delay to fulfill the requirements of future IoT applications. Performance evaluations are provided based on both sum data rate and bit error rate for the proposed NOMA based MIMO with PNC in the considered massive IoT scenarios. IEEEArticle Citation - WoS: 39Citation - Scopus: 43Self-Adjusting Fuzzy Logic Based Control of Robot Manipulators in Task Space(Institute of Electrical and Electronics Engineers Inc., 2021) Yılmaz, Bayram Melih; Tatlıcıoğlu, Enver; Savran, Aydoğan; Alcı, MusaEnd effector tracking control of robot manipulators subject to dynamical uncertainties is the main objective of this work. Direct task space control that aims minimizing the end effector tracking error directly is preferred. In the open loop error system, the vector that depends on uncertain dynamical terms is modeled via a fuzzy logic network and a self-adjusting adaptive fuzzy logic component is designed as part of the nonlinear proportional derivative based control input torque. The stability of the closed loop system is investigated via Lyapunov based arguments and practical tracking is proven. The viability of the proposed control strategy is shown with experimental results. Extensions to uncertain Jacobian case and kinematically redundant robots are also presented. IEEEArticle Citation - WoS: 2Citation - Scopus: 2Analytical Improvement on the Electromagnetic Scattering From Deformed Spherical Conducting Objects(Institute of Electrical and Electronics Engineers, 2021) Ateş, Barış; Kuştepeli, Alp; Çetin, ZebihIn this paper, electromagnetic scattering from con-ducting deformed spheres is considered analytically by employing the perturbation method and utilizing Debye potentials. To be able to analyze a wide variety of scattering problems, azimuthal variation is indispensable and therefore the geometries of the scatterers considered in this study do not have rotational symmetry, hence they are dependent on the θ and φ angles in spherical coordinates. Analyses are carried up to the second order explicitly to obtain more accurate results and thus scattered fields are obtained with second order corrections. The coefficients used to determine the scattered field are expressed in terms of Clebsch-Gordan coefficients, which enables one to obtain the results for new geometries only by simple algebraic manipulations. Numerical results and their comparisons are also presented for various deformation functions and parameters. IEEEArticle Citation - WoS: 28Citation - Scopus: 28Multi-Helper Noma for Cooperative Mobile Edge Computing(Institute of Electrical and Electronics Engineers, 2022) Yılmaz, Saadet Simay; Özbek, BernaThe next-generation wireless networks are expected to support a number of computation-intensive and delay-sensitive applications such as virtual reality (VR), autonomous driving, telesurgery and unmanned aerial vehicles (UAVs). Since many devices are computation and power limited, mobile edge computing (MEC) has been deemed as a promising way to enhance computation service. In this paper, we propose a novel cooperative MEC that exploits the combination of non-orthogonal multiple access (NOMA) and multiple helpers. In the proposed system featuring a user, multiple helpers and a base station (BS), the user can simultaneously offload its computation-intensive tasks to the helpers using NOMA when there is no strong direct transmission link between the user and the BS. Then, the helpers can compute and offload these tasks through NOMA. Thus, in the proposed scheme, the computation and offloading modes at the helpers are determined with respect to the optimized task offloading decision factor. The simulation results show that the proposed NOMA-based cooperative MEC significantly increases the total offloading data under the latency constraints compared to the benchmark schemes featuring one helper with strong direct transmission link. IEEE