Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/7

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  • Conference Object
    Reflection Coefficient Prediction in Triple-Layer Microwave Absorbers: A Machine Learning Perspective
    (Institute of Electrical and Electronics Engineers Inc., 2025) Nas, Abdurrahim; Kankilic, Sueda; Karpat, Esin
    Electromagnetic absorbers prevent the reflection and transmission of electromagnetic waves. Electromagnetic absorbers have a wide range of applications from military to medical applications. In these areas, absorber designs have different importance in terms of parameters such as reflection coefficient, selected material and thickness. Many difficulties are encountered to achieve the optimal design. In this paper, we propose a machine learning regression method for three-layer microwave absorber architecture to obtain the optimum parameters, overcome the difficulties and speed up the process. The material and thickness of each layer are used as parameters to feed the models and the reflection coefficient is estimated using these parameters. Predictions are made with various regression algorithms. These algorithms are KNeighbors Regression, Random Forest Regressor, XGBoost Regression, CatBoost Regressor, AdaBoost Regressor which uses similarities between observations, Gradient Boosting Regressor which is tree based or boosted tree based algorithms, Linear Regression which uses a linear model, Partial Least Squares Regression which uses cross decomposition, Gaussian Process Regressor which uses statistical distribution, Stochastic Gradient Descent Regressor which uses a linear model to reduce empirical loss to predict an output. Mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), R-squared (R2) are used with the predictions of each model to obtain the metrics for the analysis of the results. The predicted values and actual values of the metrics are used to compare the regression algorithms used in the research. After the comparison, our observations show that in most cases CatBoost Regressor is better than other models used in the research. In general, it is observed that most of the machine learning regression algorithms used in this paper can be used to predict the reflection coefficient of three-layer microwave absorbers as output and input parameters used in the research. © 2025 Elsevier B.V., All rights reserved.
  • Conference Object
    Transaction Types in Cryptocurrencies
    (Institute of Electrical and Electronics Engineers Inc., 2024) Uysal, T.; Unozkan, H.
    The cryptocurrency ecosystem has witnessed an explosive growth, and this study delves into the mechanisms that fuel this expansion. Focusing on airdrops, staking, farming, and coin burning, the research analyzes a vast dataset of transactions from the Ethereum and Binance Networks. This analysis sheds light on the strategic use of these tools by highlighting transactions that engage users and distribute rewards (e.g., airdrops, staking). Furthermore, the study investigates farming as a method to enhance market efficiency by providing liquidity, and coin burning as a strategy to manage token supply and potentially increase value through scarcity. While effective utilization of these mechanisms can bolster token value and project success, regulatory challenges remain. Ultimately, this study aims to raise public awareness of cryptocurrency transaction types and the associated risks. Although many researchers have studied illicit flows on cryptocurrency transactions, in literature we haven't confronted with any study regarding transaction types. By analyzing over 107 million transaction records, the research presents the distribution of these transaction types. With the analysis of the transaction types, the definitions of them, the statistical outputs from the more than 107 million transaction records from ERC20 and BEP20 blockchain systems and the analysis of some specific token types such as Pancake Swap and Shiba, this research is the first study to present an academic approach with statistical analysis. This study can provide investors with knowledge that safeguard them from the potential pitfalls of cryptocurrency transactions. Also, this study presents some basic statistics so as to understand main patterns of different types of transactions. © 2024 IEEE.
  • Conference Object
    Citation - Scopus: 2
    Unet3D Based Next Frame Prediction;
    (Institute of Electrical and Electronics Engineers Inc., 2024) Akbacak, E.
    The concept of next-frame prediction, which is predicting the subsequent frames using historical frames' spatial and temporal properties, is indispensable in computer vision. There are various application of frame prediction such as predicting a future event in autonomous vehicles, predicting patient falls in biomedical engineering, and reducing the amount of data transmitted in video transmission. Deep learning applications in this field are the focus of the most effective methods. Especially CNN-LSTM, Convolutional LSTMs, and GAN-supported deep learning methods are very common. This study proposes the inflated 3D Unet encoder-decoder model, which is not yet used for the next-frame prediction problem. The proposed model predicts both the next frame and the subsequent frames. Experimental results have shown that the proposed method gives better results than CNN-LSTM and Convolutional LSTMs. © 2024 IEEE.
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 4
    Fpga Based Dco-Ofdm Phy Transceiver for Vlc Systems
    (Institute of Electrical and Electronics Engineers Inc., 2019) Levent, V.E.; Uysal, M.; Saglam, G.; Ugurdag, H.F.; Fajar Rizqi Annafianto, N.; Aydin, F.; Kebapci, B.; Tesfay, Shewit Weldu; Annafianto, Nur Fajar Rizqi
    Satisfying the demand for high bandwidth and low latency in Visible Light Communication (VLC) systems is a difficult challenge. VLC channels exhibit frequency-selectiveness at peak-speeds. This phenomenon generates a serious inter-symbol interference effect. A physical layer (PHY) design that can carry out physical layer communication tasks to overcome all these strains is required. In this study, an FPGA based PHY design that implements DCO-OFDM algorithm is described for VLC systems. A demo was performed with the developed PHY design using an LED transmitter and a photodetector receiver. © 2019 Chamber of Turkish Electrical Engineers.