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projects
Whack-A-Mole-Game with VHDL on Basys3 Board
Date:
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.
Disease Detection from X-Ray Images with Classical Backbone Networks
Date:
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.
Flight Price Prediction using ML Techniques Written From Scratch
Date:
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.
Monkeypox Disease Detection using Classical Backbone Models
Date:
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.
IdentiFusion: A Multimodal Approach to Facial Attribute Recognition using FaceBERT and LightenedMOON
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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. 
Machine Learning-Enhanced Visible Light Positioning in IRS-Assisted Indoor Environments with a Single LED Transmitter
Date:
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.
Wireless Train Signalization
Date:
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.
From Pixels to Wireframes: 3D Reconstruction via CLIP-Based Sketch Abstraction
Date:
The project explores a novel approach to abstracting 3D surfaces into wireframe-like representations by leveraging CLIP-based semantic guidance and Bézier curve parameterizations. The project website can be found at this link.
DetoxText: Text Detoxification Using Finetuned Encoder-Decoder Models
Date:
The project focuses on fostering safer online spaces by developing deep learning models that detect hate speech in various formats, including text, images, memes, videos, and audio content. The project repository can be found in the given link.
Large-Scale 3D Scene Relighting using Pre‑Trained Diffusion Models
Date:
This project is a NeRF editing framework that enables localized relighting and texture edits using pretrained diffusion models. Built on the DDS pipeline, it integrates wavelet-based gradient filtering to preserve reflections and fine appearance details during editing. The framework keeps scene geometry fixed after NeRF training, ensuring structural consistency, and enhances edits with surface normal prediction for improved view-dependent rendering. The result is high-fidelity, semantically guided 3D scene edits with strong reflection and color consistency. The GitHub repository can be found in the following link.
publications
IRS Aided Visible Light Positioning with a Single LED Transmitter
Published in Elsevier Digital Signal Processing, 2025
We propose a visible light positioning (VLP) system with a single light emitting diode (LED) transmitter and an intelligent reflecting surface (IRS) for estimating the position of a receiver equipped with a single photo-detector. By performing a number of transmissions from the LED transmitter and optimizing the orientation vectors of the IRS elements for each transmission, position information is extracted by the receiver based on power measurements of the signals reflecting from the IRS. The theoretical limit and the maximum likelihood (ML) estimator are presented for the proposed setting. In addition, an algorithm, named IRS focusing, is proposed for determining the orientations of the IRS elements during the localization process. The effectiveness of the proposed localization approach is demonstrated through simulations. Furthermore, extensions are provided to apply the proposed approach in the presence of partial prior information about the receiver position and when the IRS is located at the LED transmitter. Keywords: Intelligent reflecting surface, Visible light positioning, Estimation, Cramér-Rao lower bound.
Recommended citation: E. Tarhan, F. Kokdogan, and S. Gezici, "IRS aided visible light positioning with a single LED transmitter," *Digital Signal Processing*, vol. 156, Part A, 104799, 2025. [Online]. Available: https://doi.org/10.1016/j.dsp.2024.104799
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talks
Talk 1 on Relevant Topic in Your Field
Date:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
Date:
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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