Browsing by Author "Bilgiç, Burcu"

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900 MHz'de yere yakın antenlerle deneysel radyo yayılımıçalışması
(2020) Bilgiç, Burcu; Kara, Ali; Aydın, Elif; Department of Electrical & Electronics Engineering
Bu çalışmanın amacı, dış ortamdaki radyo sinyal yayılımını deneysel sonuçlara göre analiz etmektir. Bu tez, Ankara'nın çevresindeki kırsal ve banliyö ortamlardan alınan deneysel verileri içermektedir. Beş farklı sahadaki GHz altı yol kaybı deneyleri gösterilmiştir. Yol kaybı ölçümleri farklı alıcı ve verici anten yüksekliklerinde yapılmıştır. Buna ek olarak, açık ve kapalı görüş sahalarındaki deneysel çalışmalardan elde edilen eğri uydurma yöntemleri sunulmuştur. Bu araştırma, farklı dış ortamlardaki linklerde kablosuz cihazların etkin kullanımını açıklamıştır. Bu tez, farklı anten yükseklikleri ve farklı arazi çeşitlerinde, yol kayıplarını modellemek ve yeni formüller oluşturmak için eğri uydurma yöntemiyle kataloglama sistemi oluşturmayı sağlamıştır. Hem anten yüksekliği etkisi hem arazi yapısının yol kaybına etkisi yeni modellerle sunulmuştur. Ayrıca, literatürde yer alan modellerle oluşturulan yeni model karşılaştırılmıştır.
Citation Count: 5
Experimental Investigation of Communication Performance of Drones Used for Autonomous Car Track Tests
(Mdpi, 2021) Yildiz, Melih; Bilgic, Burcu; Kale, Utku; Rohacs, Daniel; Department of Electrical & Electronics Engineering; Airframe and Powerplant Maintenance
Autonomous Vehicles (AVs) represent an emerging and disruptive technology that provides a great opportunity for future transport not only to have a positive social and environmental impact but also traffic safety. AV use in daily life has been extensively studied in the literature in various dimensions, however; it is time for AVs to go further which is another technological aspect of communication. Vehicle-to-Vehicle (V2V) technology is an emerging issue that is expected to be a mutual part of AVs and transportation safety in the near future. V2V is widely discussed by its deployment possibilities not only by means of communication, even to be used as an energy transfer medium. ZalaZONE Proving Ground is a 265-hectare high-tech test track for conventional, electric as well as connected, assisted, and automated vehicles. This paper investigates the use of drones for tracking the cars on the test track. The drones are planned to work as an uplink for the data collected by the onboard sensors of the car. The car is expected to communicate with the drone which is flying in coordination. For the communication 868 MHz is selected to be used between the car and the drone. The test is performed to simulate different flight altitudes of drones. The signal strength of the communication is analyzed, and a model is developed which can be used for the future planning of the test track applications.
Citation Count: 14
A new outlier detection method based on convex optimization: application to diagnosis of Parkinson's disease
(Taylor & Francis Ltd, 2021) Taylan, Pakize; Yerlikaya-Ozkurt, Fatma; Bilgic Ucak, Burcu; Weber, Gerhard-Wilhelm; Department of Electrical & Electronics Engineering; Industrial Engineering
Neuroscience is a combination of different scientific disciplines which investigate the nervous system for understanding of the biological basis. Recently, applications to the diagnosis of neurodegenerative diseases like Parkinson's disease have become very promising by considering different statistical regression models. However, well-known statistical regression models may give misleading results for the diagnosis of the neurodegenerative diseases when experimental data contain outlier observations that lie an abnormal distance from the other observation. The main achievements of this study consist of a novel mathematics-supported approach beside statistical regression models to identify and treat the outlier observations without direct elimination for a great and emerging challenge in humankind, such as neurodegenerative diseases. By this approach, a new method named as CMTMSOM is proposed with the contributions of the powerful convex and continuous optimization techniques referred to as conic quadratic programing. This method, based on the mean-shift outlier regression model, is developed by combining robustness of M-estimation and stability of Tikhonov regularization. We apply our method and other parametric models on Parkinson telemonitoring dataset which is a real-world dataset in Neuroscience. Then, we compare these methods by using well-known method-free performance measures. The results indicate that the CMTMSOM method performs better than current parametric models.