Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
Browse
349 results
Search Results
Now showing 1 - 10 of 349
Article On Group Connected Transmissive Beyond Diagonal RIS for MIMO Systems(Institute of Electrical and Electronics Engineers Inc., 2026) Ilguy, M.; Özbek, B.; Le Ruyet, D.Reconfigurable intelligent surfaces (RIS) have emerged as an important technology for next-generation wireless networks by intelligently manipulating the wireless propagation environment. Beyond Diagonal RIS (BD-RIS) extends the traditional RIS architecture by allowing non-diagonal reflection matrices, enabling more flexible signal manipulation. Transmissive RIS (T-RIS), on the other hand, facilitates the transmission of signals through the metasurfaces. In this paper, we propose a novel design called transmissive BD-RIS (T-BD-RIS), which integrates the functionalities of BD-RIS and T-RIS to enhance the user data rate. We design an algorithm for the group connected (GC) configuration, which jointly optimizes the beamforming at the base station, the T-BD-RIS transmission matrix, and the receive combiner at the user side. The fully connected (FC) and single connected (SC) cases are special instances of the proposed generic GC design, obtained by an appropriate choice of the number of groups. We evaluate the performance of various schemes, demonstrating the potential of the proposed approach. © 1997-2012 IEEE.Article On the Electromagnetic Scattering from Deformed Spherical Dielectric Objects(Institute of Electrical and Electronics Engineers Inc., 2026) Ates, B.; Kuştepeli, A.; Çetin, Z.In this article, an analytical investigation of electromagnetic scattering from deformed dielectric spheres using Debye potentials and the perturbation method is presented. To address a broad spectrum of scattering problems, azimuthal variation is included, which leads to scatterers with non-rotationally symmetric shapes depending on the θ and φ angles in spherical coordinates. The analysis of the scattered and transmitted fields is carried out explicitly up to the second order in the perturbation parameter, thereby achieving higher accuracy. The coefficients of the scattered and transmitted fields are expressed in terms of Clebsch-Gordan coefficients, facilitating the computation of results for new geometries through basic algebraic manipulations. Numerical results and comparisons are provided for several obstacle geometries, including irregular shapes and bodies of revolution, with different relative permittivities and permeabilities, in terms of backward, forward, and bi-static radar cross sections. © 1963-2012 IEEE.Article Energy Harvesting in High Altitude Platform Station Enabled Sensor Networks(Institute of Electrical and Electronics Engineers Inc., 2026) Tuylu, M.; Erdoǧan, E.High altitude platform station (HAPS) systems are becoming crucial facilitators for future wireless communication networks, enhancing connectivity across all vertical communication layers, including small Internet of Things (IoT) sensors and devices, terrestrial users, and aerial devices. In the context of the widely recognized vertical heterogeneous network (VHetNet) architecture, HAPS systems can provide service to both aerial and ground users. However, integrating HAPS systems as a core element in the VHetNet architecture presents a considerable energy challenge, marking a prominent constraint for their operation. Driven by this challenge, we introduce an energy harvesting (EH) strategy tailored for HAPS systems, enabling a HAPS system to gather energy from another HAPS system, which is not constrained by energy limitations. To assess the performance capabilities of the proposed model, we derive outage probability (OP), ergodic capacity (EC) and verify them by using Monte Carlo (MC) simulations. Moreover, we explore the system in terms of throughput. The findings reveal that harnessing full potential of EH stands as a viable approach to meet the energy demands of HAPS systems. © 2001-2012 IEEE.Conference Object A 10-Round Attack on Midori-64(Institute of Electrical and Electronics Engineers Inc., 2025) Balikci, C.; Kara, O.Midori is a lightweight block cipher introduced by Banik et al. at ASIACRYPT 2015. It comes in two variants: Midori-64, which has a 64-bit state with 16 rounds, and Midori128, which has a 128 -bit state with 20 rounds. Both use a 128 -bit key. In this work, we present the first truncated differential attack on Midori-64. We construct a 6-round truncated differential by precisely computing the propagation probabilities of specific truncated differences through the cipher's matrix multiplication layer, as well as identifying the positions they may occupy. We also determine its exact probability, with a ratio of approximately 1.85 compared to a random permutation. Using this distinguisher, we mount the first known truncated differential attack on 10 -round Midori-64. Although our attack does not compromise the fullround cipher, it ranks among the most effective known attacks on reduced-round Midori. © 2025 IEEE.Conference Object Aspect-Based Medical Record Classification Using Large Language Model Guided Knowledge Graph(Institute of Electrical and Electronics Engineers Inc., 2025) Işik, E.; Inan, E.Traditional sentiment analysis approaches typically evaluate a text as a whole and assign it a single sentiment label, such as positive or negative. Although this method works well for many tasks, there are cases where it is more beneficial to understand sentiment related to specific aspects. To address this issue, Aspect-Based Sentiment Analysis (ABSA) focuses on analysing sentiment at the aspect level, treating it as a more detailed form of opinion mining. In this study, we proposed a method that initially identifies aspect terms as an extraction sub-task of anatomy terms by leveraging biomedical knowledge graphs. In the second subtask, we leverage well-known large language models to predict the sentiment polarities of these extracted aspect terms. The experimental results for each subtask demonstrate that the RaTE-NER-Deberta model yields the best performance in the anatomy aspect identification subtask, achieving precision, recall, and F1 scores of 65.385, 64.151, and 64.762, respectively. After identifying anatomical entities in the input texts using this model, we proceed with the classification task. The deberta-v3-base-absa-v1.1 model, a specialized version for aspect-based sentiment analysis, delivers the highest results, with a precision of 91.38, recall of 80.30, and an F1 score of 85.48. © 2025 IEEE.Conference Object Stability of Low-Cost High-Utility Patterns Under Uncertain Cost Assignments(Institute of Electrical and Electronics Engineers Inc., 2025) Oguz, D.Low-Cost High-Utility Itemset Mining (LCHUIM) is a recent extension of utility-based pattern mining that aims to identify itemsets with high utility and low associated cost. In many real-world applications, especially in domains like education or healthcare, explicit cost information may be unavailable or difficult to measure. This study investigates the stability of LCHUIM under uncertain cost settings by applying it to a real-world educational dataset where cost values are not explicitly provided. We generate three different cost assignment strategies using interpretable mappings and evaluate the impact of cost differences under various utility and support thresholds. Experimental results show that under stricter thresholds, LCHUIM yields highly stable results. As thresholds are relaxed, more patterns emerge, and the sensitivity to cost differences increases. Nevertheless, a considerable number of patterns remain consistent, indicating that LCHUIM is capable of producing reliable insights even when cost values are estimated. This work contributes to understanding the robustness of utility-based pattern mining in behavior-driven domains with incomplete or estimated cost values. © 2025 IEEE.Conference Object Reframing Software Log Summarisation as Multi-Label Classification With Encoder-Decoder Transformer Model(Institute of Electrical and Electronics Engineers Inc., 2025) Türkzeybek, F.Z.; Inan, E.As software systems become more advanced and capable of meeting sophisticated demands, they also become more complex. Consequently, software system logs, which are the most effective tool programmers have for understanding system diagnostics and taking appropriate action, become as complicated as the systems that generate them. To address this issue, software system log summarisation processes the logs generated by complex systems and extracts or summarizes their meaning in a more readable, less complex format. Recent improvements in natural language processing, brought about by transformers that evolved into large language models, offer substantial capabilities that can be implemented for log summarisation tasks. In this study, we explore this capability using a transformer-based model to summarize complex software system logs. The experimental results demonstrate that the fine-tuned T5-Small model improves the average ROUGE-1 and ROUGE-L scores of the BART-Large and Pegasus-Large models by approximately 8.46% and 15.37%, respectively. Thus, the average improvement of the fine-tuned T5-Small over the fine-tuned BART-Large and Pegasus-Large models is approximately 11.92% by means of R1 and RL scores with lesser computational cost. © 2025 IEEE.Conference Object A Study on the Influence of Magnetic Nanoparticle Concentration on Heating Efficiency in Magnetic Hyperthermia(Institute of Electrical and Electronics Engineers Inc., 2025) Savranguler, E.N.; Gümüş, S.; Harmansah, C.; Öztürk, Y.; Magat, H.Despite significant advancements in diagnostic and therapeutic technologies, cancer remains one of the most significant global health problems and continues to be among the leading causes of death. In addition to conventional cancer treatments, alternative and innovative methods are being developed for cancer therapies. One of the promising cancer therapies is magnetic hyperthermia, which is based on the principle of heat generation through the Brownian and Néel relaxation mechanisms of magnetic nanoparticles (MNPs) exposure to an alternating magnetic field. In this method, heat is generated by MNPs that are selectively targeted to tumor tissues, resulting in localized cell death. The heating efficiency of MNPs is directly influenced by their physical and chemical properties, such as particle size, magnetic anisotropy, chemical composition, and colloidal stability. Recent studies have shown that magnetic hyperthermia can be effective in tumor reduction when applied alone or in combination with other conventional treatment modalities. In this study, a low-cost and easily assembled magnetic hyperthermia measurement system was employed to investigate the effect of varying nanoparticle concentrations on heating efficiency. The experimental setup consisted of a thermally insulated sample holder, an 88 kHz magnetic induction heater, and a thermometer. EFH-1 magnetic fluid was diluted with hydrocarbon oil at particle volume concentrations ranging from 7.1% (100% EFH-1) to 0.14% (2% EFH1+%98 hydrocarbon oil), and measurements were taken under an applied field of 6.03 kA/m. Based on the experimental data, the rate of temperature change over time was calculated to be in the range of 0.16 K/s to 0.005 K/s. The resulting heating efficiencies, as a function of nanoparticle concentration, were analyzed and discussed by considering previous experimental and theoretical studies. © 2025 IEEE.Conference Object A Comparative Study of Attention-Augmented YOLO Architectures for Defect Detection in Fused Deposition Modelling(Institute of Electrical and Electronics Engineers Inc., 2025) Cezayirli, H.; Tetik, H.; Dede, M.I.C.; Phone, W.L.; Alkan, B.Additive manufacturing (AM), particularly fused deposition modelling (FDM), facilitates the fabrication of complex geometries with increasing flexibility and efficiency. Ensuring consistent print quality in FDM processes necessitates the development of accurate defect detection mechanisms. Attention-augmented YOLO (You Only Look Once) models have emerged as a promising solution for addressing this challenge. In this study, we systematically benchmark and evaluate the performance of YOLO architectures enhanced with attention mechanisms within the context of FDM 3D printing applications. The models were trained and evaluated using representative defect datasets. The attention-augmented models demonstrate improved detection performance. © 2025 IEEE.Conference Object Adapting Language Models to Sentiment Analysis for Automatically Translated and Labelled Turkish News Texts(Institute of Electrical and Electronics Engineers Inc., 2025) Serficeli, S.C.; Udunman, B.; Inan, E.The proliferation of news sources makes it difficult to track current events and social events in real time. In order to interpret social events in this context quickly and effectively, it is important to translate news texts provided in different natural languages into Turkish and to perform sentiment analysis on them. The aim of this study is to translate multilingual news texts into Turkish and perform sentiment analysis on these texts. The generated labels were compared and the data that were given the same label by all models were separated as automatically labelled data. This automatic labelling process ensured that the data for which different models produced consistent results were reliably labelled. When the results were evaluated, F1 score of 0.946 was achieved for sentiment analysis using the automatic labelling mechanism for texts translated into Turkish. © 2025 IEEE.