Elektrik-Elektronik Mühendisliği Bölümü Koleksiyonu

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https://hdl.handle.net/20.500.14034/36

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Design of fractional/integer order PID controller using single DVCC and Its cardiac pacemaker application
(Springer Birkhauser, 2024) Yürdem, Betül; Aksu, Mustafa Furkan; Sağbaş, Mehmet
Cardiac failures and other cardiovascular diseases are a primary cause of death worldwide. Cardiovascular disorders, including heart failure, are the primary cause of death globally. Controller design for pacemakers is a crucial area of research. In this study, a novel proportional-integral-derivative (PID) controller design that can be used to efficiently regulate the heart rate of the cardiac pacemaker is presented. The proposed PID controller employs only one Differential Voltage Current Conveyor (DVCC) active element, two resistors, and two capacitors. The proposed PID controller circuit has voltage differential inputs, thus eliminating the need for the extra differential amplifier (DA) circuit required for closed-loop systems such as the cardiac pacemaker heart rate control system. A fractional-order (FO) PID controller (PI lambda D mu) circuit is created from the proposed integer order (IO) PID controller circuit. A cardiac pacemaker application example is given to demonstrate the workability of the proposed FO and IO PID controller circuits. Using 0.18 mu m CMOS parameters and the PSPICE simulation program, both the proposed FO and IO PID controller circuits in a closed-loop system are performed to verify the theoretical behavior.
Serverless federated learning: Decentralized spectrum sensing in heterogeneous networks
(Elsevier, 2025) Catak, Ferhat Ozgur; Kuzlu, Murat; Dalveren, Yaser; Ozdemir, Gokcen
Federated learning (FL) has gained more popularity due to the increasing demand for robust and efficient mechanisms to ensure data privacy and security during collaborative model training in the concept of artificial intelligence/machine learning (AI/ML). This study proposes an advanced version of FL without the central server, called a serverless or decentralized federated learning framework, to address the challenge of cooperative spectrum sensing in non-independent and identically distributed (non-IID) environments. The framework leverages local model aggregation at neighboring nodes to improve robustness, privacy, and generalizability. The system incorporates weighted aggregation based on distributional similarity between local datasets using Wasserstein distance. The results demonstrate that the proposed serverless federated learning framework offers a satisfactory performance in terms of accuracy and resilience.
A simplified method based on RSSI fingerprinting for IoT device localization in smart cities
(Ieee-Inst Electrical Electronics Engineers Inc, 2024) Dogan, Deren; Dalveren, Yaser; Kara, Ali; Derawi, Mohammad
The Internet of Things (IoT) has significantly improved location-based services in smart cities, such as automated public transportation and traffic management. Estimating the location of connected devices is a critical problem. Low Power Wide Area Network (LPWAN) technologies are used for localization due to their low power consumption and long communication range. Recent advances in Machine Learning have made Received Signal Strength Indicator (RSSI) fingerprinting with LPWAN technologies effective. However, this requires a connection between devices and gateways or base stations, which can increase network deployment, maintenance, and installation costs. This study proposes a cost-effective RSSI fingerprinting solution using IQRF technology for IoT device localization. The region of interest is divided into grids to provide training locations, and measurements are conducted to create a training dataset containing RSSI fingerprints. Pattern matching is performed to localize the device by comparing the fingerprint of the end device with the fingerprints in the created database. To evaluate the efficiency of the proposed solution, measurements were conducted in a short-range local area ( $80\times 30$ m) at 868 MHz. In the measurements, four IQRF nodes were utilized to receive the RSSIs from a transmitting IQRF node. The performances of well-known ML classifiers on the created dataset are then comparatively assessed in terms of test accuracy, prediction speed, and training time. According to the results, the Bagged Trees classifier demonstrated the highest accuracy with 96.87%. However, with an accuracy of 95.69%, the Weighted k-NN could also be a reasonable option for real-world implementations due to its faster prediction speed (37615 obs/s) and lower training time (28.1 s). To the best of the authors' knowledge, this is the first attempt to explore the feasibility of the IQRF networks to develop a RSSI fingerprinting-based IoT device localization in the literature. The promising results suggest that the proposed method could be used as a low-cost alternative for IoT device localization in short-range location-based smart city applications.
Flexible and lightweight mitigation framework for distributed denial-of-service attacks in container-based edge networks using kubernetes
(Ieee-Inst Electrical Electronics Engineers Inc, 2024) Koksal, Sarp; Catak, Ferhat Ozgur; Dalveren, Yaser
Mobile Edge Computing (MEC) has a significant potential to become more prevalent in Fifth Generation (5G) networks, requiring resource management that is lightweight, agile, and dynamic. Container-based virtualization platforms, such as Kubernetes, have emerged as key enablers for MEC environments. However, network security and data privacy remain significant concerns, particularly due to Distributed Denial-of-Service (DDoS) attacks that threaten the massive connectivity of end-devices. This study proposes a defense mechanism to mitigate DDoS attacks in container-based MEC networks using Kubernetes. The mechanism dynamically scales Containerized Network Functions (CNFs) with auto-scaling through an Intrusion Detection and Prevention System (IDPS). The architecture of the proposed mechanism leverages distributed edge clusters and Kubernetes to manage resources and balance the load of IDPS CNFs. Experiments conducted in a real MEC environment using OpenShift and Telco-grade MEC profiles demonstrate the effectiveness of the proposed mechanism against Domain Name System (DNS) flood and Yo-Yo attacks. Results also verify that Kubernetes efficiently meets the lightweight, agile, and dynamic resource management requirements of MEC networks.
Radar emitter localization based on multipath exploitation using machine learning
(Ieee-Inst Electrical Electronics Engineers Inc, 2024) Catak, Ferhat Ozgur; Al Imran, Md Abdullah; Dalveren, Yaser; Yildiz, Beytullah; Kara, Ali
In this study, a Machine Learning (ML)-based approach is proposed to enhance the computational efficiency of a particular method that was previously proposed by the authors for passive localization of radar emitters based on multipath exploitation with a single receiver in Electronic Support Measures (ESM) systems. The idea is to utilize a ML model on a dataset consisting of useful features obtained from the priori-known operational environment. To verify the applicability and computational efficiency of the proposed approach, simulations are performed on the pseudo-realistic scenes to create the datasets. Well-known regression ML models are trained and tested on the created datasets. The performance of the proposed approach is then evaluated in terms of localization accuracy and computational speed. Based on the results, it is verified that the proposed approach is computationally efficient and implementable in radar detection applications on the condition that the operational environment is known prior to implementation.
Activity-aware electrocardiogram biometric verification utilising deep learning on wearable devices
(Springer Int Publ Ag, 2025) Yeşilkaya, Hazal Su Bıcakçı; Guest, Richard
With the advancement of technology and the increasing use of wearable devices, information security have become a necessity. Although many biometrics authentication methods have been studied on these devices to ensure information security, an activity-aware deep learning (DL) model that is compatible with different device types and uses only electrocardiogram signals has not been studied. Our objective is to investigate DL models that exclusively use ECG signals during several physical activities, facilitating their implementation on various devices. Through this research, we aim to contribute to the advancement of wearable devices for the purpose of biometric verification. In this context, this study investigates the application of adaptive techniques that rely on prior activity classification to potentially improve biometric performance using DL models. In this study, we compare three time-frequency representations to generate images for activity classification using GoogleNet, ResNet50 and DenseNet201, and for biometric verification using ResNet50 and DenseNet201. Despite employing various convolutional neural network (CNN) models, we could not achieve high accuracy in activity classification. Consequently, manually classified samples were used for activity-aware biometric verification. We also provide a detailed comparison of various DL parameters. We use a public dataset simultaneously collected from both medical and wearable devices to offer a cross-device comparison. The results demonstrate that our method can be applied to both wearable and medical devices for activity classification and biometric verification. Besides, although it is known that DL requires a large amount of training data, our model, which was created using a small amount of training data and a real-life biometric verification scenario, achieved comparable results to studies using a large amount of data. The model was achieved 0.16% to 30.48% better results when classified according to their physical activities.
Advancing mmWave Altimetry for Unmanned Aerial Systems: A Signal Processing Framework for Optimized Waveform Design
(MDPI, 2024) Awan, Maaz Ali; Dalveren, Yaser; Kara, Ali; Derawi, Mohammad
This research advances millimeter-wave (mmWave) altimetry for unmanned aerial systems (UASs) by optimizing performance metrics within the constraints of inexpensive automotive radars. Leveraging the software-defined architecture, this study encompasses the intricacies of frequency modulated continuous waveform (FMCW) design for three distinct stages of UAS flight: cruise, landing approach, and touchdown within a signal processing framework. Angle of arrival (AoA) estimation, traditionally employed in terrain mapping applications, is largely unexplored for UAS radar altimeters (RAs). Time-division multiplexing multiple input-multiple output (TDM-MIMO) is an efficient method for enhancing angular resolution without compromising the size, weight, and power (SWaP) characteristics. Accordingly, this work argues the potential of AoA estimation using TDM-MIMO to augment situational awareness in challenging landing scenarios. To this end, two corner cases comprising landing a small-sized drone on a platform in the middle of a water body are included. Likewise, for the touchdown stage, an improvised rendition of zoom fast Fourier transform (ZFFT) is investigated to achieve millimeter (mm)-level range accuracy. Aptly, it is proposed that a mm-level accurate RA may be exploited as a software redundancy for the critical weight-on-wheels (WoW) system in fixed-wing commercial UASs. Each stage is simulated as a radar scenario using the specifications of automotive radar operating in the 77-81 GHz band to optimize waveform design, setting the stage for field verification. This article addresses challenges arising from radial velocity due to UAS descent rates and terrain variation through theoretical and mathematical approaches for characterization and mandatory compensation. While constant false alarm rate (CFAR) algorithms have been reported for ground detection, a comparison of their variants within the scope UAS altimetry is limited. This study appraises popular CFAR variants to achieve optimized ground detection performance. The authors advocate for dedicated minimum operational performance standards (MOPS) for UAS RAs. Lastly, this body of work identifies potential challenges, proposes solutions, and outlines future research directions.
Real-time radar classification based on software-defined radio platforms: enhancing processing speed and accuracy with graphics processing unit acceleration
(MDPI, 2024) Oncu, Seckin; Karakaya, Mehmet; Dalveren, Yaser; Kara, Ali; Derawi, Mohammad
This paper presents a comprehensive evaluation of real-time radar classification using software-defined radio (SDR) platforms. The transition from analog to digital technologies, facilitated by SDR, has revolutionized radio systems, offering unprecedented flexibility and reconfigurability through software-based operations. This advancement complements the role of radar signal parameters, encapsulated in the pulse description words (PDWs), which play a pivotal role in electronic support measure (ESM) systems, enabling the detection and classification of threat radars. This study proposes an SDR-based radar classification system that achieves real-time operation with enhanced processing speed. Employing the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm as a robust classifier, the system harnesses Graphical Processing Unit (GPU) parallelization for efficient radio frequency (RF) parameter extraction. The experimental results highlight the efficiency of this approach, demonstrating a notable improvement in processing speed while operating at a sampling rate of up to 200 MSps and achieving an accuracy of 89.7% for real-time radar classification.
The fast and reliable detection of multiple narrowband FH signals: a practical framework
(MDPI, 2024) Aydin, Mutlu; Dalveren, Yaser; Kara, Ali; Derawi, Mohammad
Frequency hopping (FH) is a well-known technique that is commonly used in communication systems owing to its many advantages, including its strong anti-jamming capability. In this technique, basically, radio signals are transmitted by switching the carrier between different frequency channels. As a result, the FH signal is not stationary; hence, its spectrum is expected to change over time. Therefore, the task of detection and parameter estimation of FH signals is very challenging in practice. To address this challenge, the study presented in this article proposes a method that detects and estimates the parameters of multiple narrowband FH signals. In the proposed method, first, short-time Fourier transform (STFT) is utilized to analyze FH signals, and a practical binarization process based on thresholding is used to detect FH signals. Then, a new algorithm is proposed to ensure that the center frequencies of the detected signals are successfully separated. Next, another algorithm is proposed to estimate the parameters of the detected signals. After estimating the parameters for the entire spectrum, an approach is used to detect FH signals. Lastly, the hop-clustering process is applied to separate the hops into groups without time overlap. The simulation results show that the proposed method can be an efficient way for the fast and accurate parameter estimation and detection of multiple narrowband FH signals.
Estimation of weibull probability distribution parameters with optimization algorithms and Foca wind data application
(Gazi Univ, 2024) Köse, Bayram; Işıklı, İbrahim; Sağbaş, Mehmet
In this study, the scale and shape parameters of the Weibull probability distribution function (W.pdf) used in determining the profitability of wind energy projects are estimated using optimization algorithms and the moment method. These parameters are then used to estimate the wind energy potential (WEP) in Foca region of Izmir in Turkey. The values of Weibull parameters obtained using Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Social Group Optimization (SGO), and Bat Algorithm (BA) were compared with the estimation results of the Moment Method (MM) as reference. Root mean square error (RMSE) and chi-square (chi<^>2) tests were used to compare the parameter estimation methods. The wind speed measurement values of the observation station in Foca were used. As a result of Foca speed data analysis, the annual average wind speed was determined as 6.15 m/s, and the dominant wind direction was found as northeast. Wind speed frequency distributions were compared with the measurement results and calculated with the estimated parameters. When RMSE and chi<^>2 criteria are evaluated together; it can be concluded that each used method behaves similarly for the given parameter estimation problem, with minor variations. As a result, it has been found that the optimization parameters produce very good results in wind speed distribution and potential calculations.
Estimation of weibull probability distribution parameters with optimization algorithms and Foça Wind Data application
(Gazi Univ, 2024) Köse, Bayram; Işıklı, İbrahim; Sağbaş, Mehmet
In this study, the scale and shape parameters of the Weibull probability distribution function (W.pdf) used in determining the profitability of wind energy projects are estimated using optimization algorithms and the moment method. These parameters are then used to estimate the wind energy potential (WEP) in Fo & ccedil;a region of & Idot;zmir in Turkey. The values of Weibull parameters obtained using Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Social Group Optimization (SGO), and Bat Algorithm (BA) were compared with the estimation results of the Moment Method (MM) as reference. Root mean square error (RMSE) and chi- square (chi<^>2) tests were used to compare the parameter estimation methods. The wind speed measurement values of the observation station in Fo & ccedil;a were used. As a result of Fo & ccedil;a speed data ana lysis, the annual average wind speed was determined as 6.15 m/s, and the dominant wind direction was found as northeast. Wind speed frequency distributions were compared with the measurement results and calculated with the estimated parameters. When RMSE a nd chi<^>2 criteria are evaluated together; it can be concluded that each used method behaves similarly for the given parameter estimation problem, with minor variations. As a result, it has been found that the optimization parameters produce very good results in wind speed distribution and potential calculations.
Comparative analysis of mathematics, statistics and physics based algorithms for obtaining optimum weibull probability distribution parameters for power density estimation in wind energy : Loras and Foca examples
(Turkish Soc Thermal Sciences Technology, 2024) Köse, Bayram; Telkenaroğlu, Bekir Can; Demirtürk, Bahar
In this work, the k and c parameters of the Weibull probability distribution function, which is generally used in the feasibility and efficiency studies of wind energy and preferred in electrical energy production, were estimated by Simulated Annealing Algorithm (SA) and Generalized Reduced Gradient Algorithm (GRG). AFunction parameters were also estimated by classical numerical methods, Least Squares Method(LMS), AJustus Empirical Moment Method(EMJ) and Lysen Empirical Moment Method(EML). When comparing the results, the coefficient of determination, the root mean square error (RMSE) and the chi-square distribution criteria(x(2)) Awere used. Wind speed frequency distributions were calculated with the estimated shape and scale parameter and compared with the measurement results. Consequently, better results can be seen from GRG algorithm than the classical numerical methods with coefficient of value of 0.0182 RMSE, determination of 0.8473, and the value x(2) of 0.0079 for Loras and with coefficient of value of 0.0066 RMSE, determination of 0.9793, and the value of 0.0011 for
Green Ccmpus certification and sustainability relations: Case of İzmir Bakircay University
(Tuba-Turkish Acad Sciences, 2024) Köse, Bayram; Ansay, Serkan; Akderya, Tarkan; Tabakoğlu, Gülbahar; Hızıroğlu, Abdülkadir; Berktaş, Mustafa
Technological advances, population growth and diversifying consumption habits are putting increasing pressure on natural resources, while preserving the ecological balance and protecting the rights of all living beings is of critical importance. The fair and sustainable use of resources is indispensable to ensure environmental sustainability. In this context, the United Nations' initiatives to combat climate change and the European Green Deal guide sustainability efforts based on the harmony of human life and ecosystems. In this study, green campus certification processes carried out by universities with a free and scientific approach and their connection with sustainable development goals are discussed. In addition, the green certification efforts of universities in Turkey have been examined in detail and the reflections of these efforts across the country have been evaluated. The findings reveal that universities in Turkey have increasingly prioritized sustainability efforts in recent years and have expanded their position among green-certified institutions by increasing their level of awareness.
Single VDCC based memcapacitor emulator circuit without using passive elements and analog multiplier
(Natl Inst Science Communication-Niscair, 2024) Korkmaz, Muhammet Oguz; Sağbaş, Mehmet; Babacan, Yunus; Yesil, Abdullah
In this work, a grounded charge-controlled memcapacitor emulator circuit based on a Voltage Difference Current Conveyor (VDCC) is presented. The proposed circuit is constructed with only one VDCC element and four MOSFETs. It has a transistor-based structure without the use of any passive components. The absence of any passive components makes the circuit eliminating the need for complicated analog components. The suggested circuit does away with the necessity for a mutator, which eliminates the need for an additional separate memristor emulator. The analog multiplier circuit is also not used. Additionally, the designed memcapacitor circuit allows for independent electronic control of both the fixed and variable components, adding to its flexibility and adaptability. To demonstrate the accuracy of the suggested circuit, a SPICE simulation was run using a VDCC constructed with 0.18 mu m TSMC CMOS transistors.
Smart health and artificial intelligence applications in mobile technologies
(CRC Press, 2024) Aksu, Mustafa Furkan; Sagbaş, Mehmet
Smart healthcare is described as bringing patients and physicians together on a single platform for intelligent health monitoring by analyzing daily human activities. Smart health services collect information about the patient or the environment in which the patient is located. Smart watches, heart belts, and activity bracelets are some examples of wearable devices that collect data. Numerous sensors are also used to collect information about the environment in which the patient is located. Through the patient’s mobile devices, the data collected by all these sensors can be sent to a cloud, where it can be analyzed using artificial intelligence algorithms. The analyses performed enable early intervention with the patient. The applications of smart health and AI in mobile technologies are the main topic of this chapter. The chapter starts with the Introduction Section. The overview of the sensors used in smart health applications and an explanation of how they work. The next section focuses on the communication technologies used by mobile smart health applications. At the end of the chapter, some artificial intelligence and smart health applications are described. © 2025 Mustafa Berktas, Abdulkadir Hiziroglu, Ahmet Emin Erbaycu, Orhan Er and Sezer Bozkus Kahyaoglu.
Intelligent diagnosis and treatment systems
(CRC Press, 2024) Öksüz, Cosku; Yürdem, Betül; Güllü, Mehmet Kemal
After the first unknown case of pneumonia emerged in China in December 2019, cases reported worldwide soon increased. The new type of coronavirus called SARS-CoV-2, which was determined to be the source of unknown pneumonia, caused the situation to be declared a pandemic within four months. After the past two years, the pandemic continued with the new mutations of the virus. The protracted pandemic has drastically impacted the whole world in many ways. The RT-PCR, which is accepted as the standard testing, has been used for detecting and isolating patients. Especially the high rates of false negatives for the RT-PCR test caused the need to develop alternative tools that are extremely sensitive. Therefore, many methods have been developed adopting machine- and deep-learning-based methods for recognizing COVID-19 disease over medical images. Many of these proposed intelligent systems are based on image-processing methods. More specifically, the researchers are rivaling in a manner to design deep learning based image-processing architectures for capturing the disease patterns effectively. In the scope of this study, the intelligent diagnosis methods proposed in the literature specifically for COVID-19 detection are overviewed by giving the logic behind and conceptualizing them. © 2025 Mustafa Berktas, Abdulkadir Hiziroglu, Ahmet Emin Erbaycu, Orhan Er and Sezer Bozkus Kahyaoglu.
CC-CBTA-Based floating inductance simulator with CM/VM PID controllers∗
(World Scientific, 2024) Sağbaş, Mehmet; Cam Taskiran, Z.G.; Ayten, U.E.
This work describes voltage-mode (VM), current-mode (CM) PID controllers and an electrically controllable floating inductance (FI) simulator circuit. Only a single current-controlled current backward transconductance amplifier (CC-CBTA) and a grounded capacitor are used in the proposed FI simulator circuit. One CBTA, one CC-CBTA, two capacitors and one resistor are used in the proposed PID controllers. All of the proposed circuits have appropriate input and output impedance values, allowing them to be utilized in cascade connections without requiring a buffer circuit. There is also no matching constraint between the active and passive values of the proposed circuit. The SPICE software environment and experimental results were used to validate theoretical derivations and associated outcomes. The layout of the CC-CBTA has been built, and the post-layout simulations are carried out using the netlist extracted from the layout. Comparisons were made with similar circuits found in the professional literature. © World Scientific Publishing Company.
Integrated mm-wave MIMO antenna for 5G IoT
(Institute of Electrical and Electronics Engineers Inc., 2024) Alkhmaisi, K.S.K.; Jusoh, M.; Adam, I.; Sabapathy, T.; Majid, H.A.; Al-Bawri, S.S.; Şeker, Cihat
This paper presents the design and optimization of a dual-band microstrip patch antenna for sub-mid band 5G IoT applications, focusing on frequencies 2.4 and 26 GHz. Utilizing a Taconic TLY-3 substrate with a dielectric constant of ϵr = 2.3, the antenna achieves a compact size. Simulation results using CST software show an S11 less than -20 dB for 2.4 GHz and -43 dB at 26 GHz, indicating efficient impedance matching. The antenna exhibits a gain of 1.120 dBi at 2.4 GHz and 7.1 dBi at 26 GHz. Mutual coupling reduction techniques are employed, achieving less than -15 dB isolation between elements. The radiation pattern is stable across both bands, demonstrating the antenna's efficacy for MIMO systems in 5G IoT networks. © 2024 IEEE.
Securing the internet of things: Challenges and complementary overview of machine learning-based intrusion detection
(Institute of Electrical and Electronics Engineers Inc., 2024) Isin, Latife Ilayda; Dalveren, Yaser; Leka, Elva; Kara, Ali
The significant increase in the number of IoT devices has also brought with it various security concerns. The ability of these devices to collect a lot of data, including personal information, is one of the important reasons for these concerns. The integration of machine learning into systems that can detect security vulnerabilities has been presented as an effective solution in the face of these concerns. In this review, it is aimed to examine the machine learning algorithms used in the current studies in the literature for IoT network security. Based on the authors' previous research in physical layer security, this research also aims to investigate the intersecting lines between upper layers of security and physical layer security. To achieve this, the current state of the area is presented. Then, relevant studies are examined to identify the key challenges and research directions as an initial overview within the authors' ongoing project. © 2024 IEEE.
Federated learning: Overview, strategies, applications, tools and future directions
(Elsevier Ltd, 2024) Yürdem, Betül; Kuzlu, Murat; Gullu, Mehmet Kemal; Catak, Ferhat Ozgur; Tabassum, Maliha
Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, by keeping data local and only exchanging model updates through the communication network. This review paper provides a comprehensive overview of federated learning, including its principles, strategies, applications, and tools along with opportunities, challenges, and future research directions. The findings of this paper emphasize that federated learning strategies can significantly help overcome privacy and confidentiality concerns, particularly for high-risk applications. © 2024 The Author(s)

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