WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Conference Object Outage and Intercept Performance in THz LEO-Ground Communication With Satellite Selection(IEEE, 2025) Bakirci, Emre Berker; Ahrazoglu, Evla Safahan; Altunbas, Ibrahim; Erdogan, EylemSatellite communication and THz communication systems are some of the methods that aim to meet the demand of increasing data rates. With an importance growing alongside increasing data amounts, data security is on its way to a position that cannot be neglected when building systems. In this study, it has been shown that secure data transmission can be made possible through the use of THz frequencies in a link between LEO satellites and a ground station. Proposed scenarios data transmission performance have been analyzed. It has been shown that selection transmission have improved both data transmission and security performances.Conference Object Performance Evaluation of Filter-Based Gene Selection Methods in Cancer Classification(IEEE, 2025) Gokalp, OsmanWith the advances in microarray technology, gene expression levels can be measured efficiently, and this data can be used to solve important problems such as cancer classification. However, microarray data suffers from the high-dimensionality problem and requires dimensionality reduction techniques such as feature selection. This study addresses the cancer classification problem using microarray datasets and comparatively evaluates the performance of different filter-based gene (feature) selection methods. To this end, 11 microarray datasets have been evaluated using 6 different filter methods, and experimental results are presented. According to the findings, the gene selection methods used can improve classification performance by 5% to 30%. Using 5-fold cross-validation, the highest accuracy rates were achieved with 32 genes selected by the gain ratio filter for the Breast and Colon datasets, and with 8 genes selected by the information gain filter for the CNS dataset.Conference Object Df-Segdiff: Adiffusion Segmentation Model Using a New Distributed Parallel Computing Algorithm(IEEE, 2024) Mi, Hancang; Gan, Hong-Seng; Wang, Xiaoyi; Shimizu, Akinobu; Ramlee, Muhammad Hanif; Unlu, Mehmet ZubeyirBrain tumours are among the most life-threatening diseases, and automatic segmentation of brain tumours from medical images is crucial for clinicians to identify and quantify tumour regions with high precision. While traditional segmentation models have laid the groundwork, diffusion models have since been developed to better manage complex medical data. However, diffusion models often face challenges related to insufficient parallel computing power and inefficient GPU utilization. To address these issues, we propose the DF-SegDiff model, which includes diffusion segmentation, parallel data processing, a distributed training model, a dynamic balancing parameter and model fusion. This approach significantly reduces training time while achieving an average Dice score of 0.87, with several samples reaching Dice values close to 0.94. By combining BRATS2020 with the Medical Segmentation Decathlon dataset, we also integrated a comprehensive dataset containing 800 training samples and 53 test samples. Evaluation of the model using Dice, IoU, and other relevant metrics demonstrates that our method outperforms current state-of-the-art techniques.Conference Object Citation - WoS: 3Citation - Scopus: 5Predicting Software Functional Size Using Natural Language Processing: an Exploratory Case Study(IEEE, 2024) Unlu, Huseyin; Tenekeci, Samet; Ciftci, Can; Oral, Ibrahim Baran; Atalay, Tunahan; Hacaloglu, Tuna; Demirors, OnurSoftware Size Measurement (SSM) plays an essential role in software project management as it enables the acquisition of software size, which is the primary input for development effort and schedule estimation. However, many small and medium-sized companies cannot perform objective SSM and Software Effort Estimation (SEE) due to the lack of resources and an expert workforce. This results in inadequate estimates and projects exceeding the planned time and budget. Therefore, organizations need to perform objective SSM and SEE using minimal resources without an expert workforce. In this research, we conducted an exploratory case study to predict the functional size of software project requirements using state-of-the-art large language models (LLMs). For this aim, we fine-tuned BERT and BERT_SE with a set of user stories and their respective functional size in COSMIC Function Points (CFP). We gathered the user stories included in different project requirement documents. In total size prediction, we achieved 72.8% accuracy with BERT and 74.4% accuracy with BERT_SE. In data movement-based size prediction, we achieved 87.5% average accuracy with BERT and 88.1% average accuracy with BERT_SE. Although we use relatively small datasets in model training, these results are promising and hold significant value as they demonstrate the practical utility of language models in SSM.Conference Object Citation - WoS: 1Citation - Scopus: 1Robust and Energy-Efficient Hardware Architectures for Dizy Stream Cipher(IEEE, 2024) Schmid, Martin; Arul, Tolga; Kavun, Elif Bilge; Regazzoni, Francesco; Kara, OrhunIn the era of ubiquitous computing, efficient and secure implementations of cryptographic hardware are crucial. This paper extends the hardware implementations of a Small Internal State Stream (SISS) cipher, namely DIZY. Previous work shows that DIZY's hardware performance, in terms of area cost and power consumption, is among the best when compared to notable stream ciphers, especially for frame-based encryptions requiring frequent initialization. In this study, we initially optimize the existing hardware implementation and then evaluate the energy efficiency of DIZY. We implement different unrolled versions of DIZY and analyze their energy consumption. Furthermore, we address physical security by integrating masking techniques into the DIZY S-box to protect the implementation against side-channel attacks. We thoroughly investigate the associated overhead and apply optimizations to reduce it, ensuring robust security without compromising efficiency. Our results present a secure, energy-efficient, and lightweight cryptographic hardware design for the stream cipher DIZY, making it suitable for various applications, including Internet of Things (IoT) and embedded systems.Conference Object Development of Low-Cost Portable Blood Vessel Imaging System(IEEE, 2021) Altay, Ayse; Gumus, AbdurrahmanAs an alternative to high-cost near-infrared (NIR) vascular imaging devices in the market [1], a microcomputerbased, real-time, low-cost, non-contact and safe vascular imaging system has been developed. The higher absorption coefficient of blood from skin and fat, as well as the differences in oxy and deoxyhemoglobin spectra in blood, were helpful factors in the use of the NIR region during the acquisition of vessel images. A device, which uses NIR LED light operated at 850 nm, was designed using optical and electronic components. Image analysis were performed using OpenCV, which is an open-source software library, and data visualization libraries. Tests were carried out to optimize the best imaging conditions for the device. In this study, a portable device design with improved vessel image quality is presented which could potentially be used to assist the health professionals to investigate the abnormalities in the superficial vascular structures at different times during patients' treatments.Conference Object Citation - WoS: 1Citation - Scopus: 1A Metric for Measuring Test Input Generation Effectiveness of Test Generation Methods for Boolean Expressions(IEEE, 2021) Ufuktepe, Deniz Kavzak; Ufuktepe, Ekincan; Ayav, TolgaThe literature includes several methods to generate test inputs for Boolean expressions. The effectiveness of those methods needs to be analyzed by extensive comparisons. To this end, mutation analysis is often benefited by applying a distinctively selected set of mutants on each test generation method. Mutation analysis provides substantive information about the effectiveness of a test suite by indicating the percentage of killed mutants, which is a common metric. However, as we claim and show in this paper, this metric alone is not sufficient to demonstrate the effectiveness of the methods. For a test generation method, the amount of generated test inputs is also an important attribute to evaluate effectiveness. To the best of our knowledge, there is no metric that measures the effectiveness within a scale taking into account several attributes. In this study, we propose a new metric to measure the effectiveness of test input generation methods, which takes into account both the number of killed mutants and the number of test inputs. We demonstrate our new metric on three well-known test input generation methods for Boolean expressions.Conference Object Citation - WoS: 1Konteyner Görüntülerini Kullanarak Hasar Tespiti ve Sınıflandırması(IEEE, 2020) Imamoglu, Zeynep Ekici; Tuglular, Tugkan; Bastanlar, YalinIn the logistics sector, digital transformation is of great importance in terms of competition. In the present case, container warehouse entry / exit operations are carried out manually by the logistics personnel including container damage detection. During container warehouse entry / exit process, the process of detecting damaged containers is carried out by the personnel and several minutes are required to upload to the IT system. The aim of our work is to automate the detection of damaged containers. This way, the mistakes made by the personnel will be eliminated and the process will be accelerated. In this work, we propose to use a convolutional neural network (CNN) that takes the container images and classify them as damaged or undamaged. We modeled the problem as a binary classification and employed different CNN models. The result we obtained shows that there is no single best method for the classification. It is shown how the dataset was created and how the parameters used in the layered structures affect the models employed in this study.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.Conference Object Citation - WoS: 1Citation - Scopus: 2Integrated Space Domain Awareness and Communication System(IEEE, 2023) Geçgel Çetin, Selen; Özbek, Berna; Karabulut Kurt, GüneşSpace has been reforming and this evolution brings new threats that, together with technological developments and malicious intent, can pose a major challenge. Space domain awareness (SDA), a new conceptual idea, has come to the forefront. It aims sensing, detection, identification and countermeasures by providing autonomy, intelligence and flexibility against potential threats in space. In this study, we first present an insightful and clear view of the new space. Secondly, we propose an integrated SDA and communication (ISDAC) system for attacker detection. We assume that the attacker has advanced communication capabilities to vary attack scenarios, such as random attacks on some receiver antennas. To track random patterns and meet SDA requirements, a lightweight convolutional neural network architecture is developed. The proposed ISDAC system shows superior and robust performance under 12 different super-attacker configurations with a detection accuracy of over 97.8%. © 2023 IEEE.