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This project contains content from courses taught in the Electrical and Electronics Engineering curriculum at Bilkent University. To access the entire Google Drive containing all courses from Bilkent University, please introduce yourself and submit an access request to the Google Drive.
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This project is developed as part of the Bilkent University Electrical and Electronics Engineering Industrial Design Project. It aims to introduce a wireless railway signalization system utilizing RF-based wireless technologies and passive RFID antenna balises. This system is designed to optimize track capacity and safety by providing continuous communication and real-time management of train movements.
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This study explores visible light positioning (VLP) using a single LED transmitter and an intelligent mirror array in confined spaces with obstructed paths. Evaluated algorithms include Maximum Likelihood Estimation (MLE), K-Nearest Neighbors (KNN) Regression, and Neural Networks. MLE showed superior accuracy, while intelligent reflecting surface orientations enhanced receiver localization.
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IdentiFusion introduces a multimodal system combining NLP and Computer Vision to match textual face descriptions with facial images. It integrates FaceBERT for extracting facial features from text and LightenedMOON for recognizing facial attributes from images. This project showcases effective NLP and Computer Vision integration for image retrieval, matching, and recommendation systems, aiding law enforcement and forensic investigations. 
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This project, under ICONLAB, aims to detect and distinguish monkeypox from smallpox, chickenpox, and measles. It focuses on raising awareness in the global medical imaging community. For accurate classification, we utilized deep learning backbone models such as VGG16, Inception Net, and ResNet.
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This project explores the relationship between airline ticket prices and various factors such as airline, destination, flight times, booking period, and flight class. We developed custom-built machine learning models using fundamental Python libraries (numpy, pandas) to predict ticket prices based on these features.
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This TUBITAK project aims to develop a system for detecting diseases from X-ray images using neural networks. It involves preparing and labeling a dataset, preprocessing images to 224x224 pixels, and binarizing labels for classification. Data augmentation techniques like rotation, zoom, and shifts enhance the training dataset.
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The primary goal of this project is to develop a digital game, Whack-A-Mole, using a VGA connector and a BASYS3 FPGA. The project aims to enhance digital design abilities, focusing on debugging, glitch removal, and the implementation of sequential and combinatorial design elements to create a glitch-free game.