Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object User Selection for Secure Massive Mimo Based Mobile Edge Computing With Delay-Sensitive Applications(IEEE, 2025) Yilmaz, Saadet Simay; Ozbek, BernaMobile edge computing (MEC) has been a promising technology that leverages cloud computing capabilities at the network edge to address compute-intensive and delay-sensitive applications of mobile users with limited resources. Employing massive multiple-input multiple-output (mMIMO) and nonorthogonal multiple access (NOMA) in the MEC system facilitates simultaneous task offloading for multiple users, resulting in increased spectral efficiency and decreased offloading delay. Despite the great potential of the mMIMO-NOMA-based MEC system, offloading computation tasks to MEC servers can introduce inherent security concerns and vulnerabilities. We address a notable gap in the existing literature by investigating the effect of user selection to minimize the delay in MEC while enhancing the security of this framework. Specifically, this paper presents a user selection strategy for an uplink mMIMO-NOMA-based secure MEC system in the presence of a malicious eavesdropper (Eve) to minimize offloading and computing delays, subject to the transmit power, computing resource, and secrecy rate constraints with remote computing. We propose a two-step secure user selection algorithm and solve the optimization problem with the active-set algorithm. The simulation results demonstrate the effectiveness of the proposed user selection strategy on secure MEC with a malicious Eve by minimizing the task execution delay compared to the benchmark schemes.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: 1Towards the Construction of a Software Benchmarking Dataset Via Systematic Literature Review(IEEE, 2024) Yurum, Ozan Rasit; Unlu, Huseyin; Demirors, OnurEffort estimation is a fundamental task during the planning of software projects. Prediction models usually rely on two essential factors: software size and effort data. Measuring the size of the software can be done at various stages of the project with desired accuracy. Nevertheless, the industry faces challenges when it comes to collecting reliable actual effort data. Consequently, organizations encounter difficulties in establishing effort prediction models. Benchmarking datasets are available, but, in most cases, they have huge variances that make them less useful for effort prediction. In this study, we aimed to answer whether creating a software benchmarking dataset is possible by gathering the data from the literature. To the best of our knowledge, a comprehensive dataset that gathers the functional size and effort data of the studies from the literature is unavailable. For this purpose, we performed a systematic literature review to find studies that include projects measured with the COSMIC Functional Size Measurement (FSM) method and the related effort. As a result, we formed a dataset including 337 records from 18 studies that shared the corresponding size and effort data. Although we performed a limited search, we created a larger dataset than many datasets in the literature. In light of our review, we obtained that most studies did not share their dataset, and many lacked case details such as implementation environment and the scope of software development life cycle activities included in the effort data. We also compared the dataset with the ISBSG repository and found that our dataset has less variation in productivity. Our review showed the applicability of creating a software benchmarking dataset is possible by gathering the data from the literature. In conclusion, this study addresses gaps in the literature through a cost-free and easily extendable dataset.Conference Object Outage and Ser Analyses for Dual-Hop Inter-Satellite Thz Communication(IEEE, 2024) Ahrazoglu, Evla Safahan; Erdogan, Eylem; Altunbas, IbrahimInter-satellite links have crucial significance in offering global connectivity and low latency in satellite mega-constellations. In such architectures, system capacity and data-rate can be enhanced by utilizing terahertz (THz) frequencies. Considering the importance of inter-satellite links in mega-constellations and the mounting interest in THz communications, in this study, an inter-satellite THz communication system is examined. In this setup, a low earth orbit (LEO) satellite is deployed to assist transmission between two LEO satellites by using variable-gain amplify-and-forward relaying protocol. The system's performance is analyzed in terms of both outage probability and symbol error rate, and asymptotic outage characteristic is explored. All theoretical findings are verified by Monte-Carlo simulations.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 - Scopus: 1Colorimetric Detection of Creatinine on an Electromechanical Mixing Platform(IEEE, 2021) Tarim, E. Alperay; Oksuz, Cemre; Karakuzu, Betul; Tekin, H. CumhurWe present an electromechanical mixing platform integrated with a smartphone for colorimetric detection of creatinine using an enzymatic reaction between horseradish peroxidase (HRP) and 3,3',5,5'-Tetramethylbenzidine (TMB). On the developed platform, the color resulting from the reaction between HRP and TMB in a reservoir of a poly(methyl methacrylate) (PMMA) chip is measured using a smartphone camera. With mixing, diluted creatine solutions can be detected in 1 min that can reduce long waiting times of creatinine detection due to enzymatic reactions. Furthermore, smartphone-based colorimetric detection reduces the cost of analysis by eliminating costly equipment for spectrometric measurements without affecting the sensitivity of analysis. We, therefore, think that the presented platform integrated with a smartphone could be used for automatic measurement of creatinine level in the sample by allowing low-cost and rapid analysis, and this would be particularly beneficial for monitoring of chronic kidney disease (CKD) at the point-of-care setting.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 Adaptive Limited Feedback Scheme for Stream Selection Based Interference Alignment in Heterogeneous Networks(IEEE, 2016) Beyazıt, Esra Aycan; Özbek, Berna; Le Ruyet,D.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.