Cilt:64 Sayı:01 (2022)

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https://dspace.ankara.edu.tr/handle/20.500.12575/86465

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Browsing Cilt:64 Sayı:01 (2022) by Author "Other"

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    3D reconstruction of coronary arteries using deep networks from synthetic X-ray angiogram data
    (Ankara Üniversitesi Mühendislik Fakültesi, 2022) Atlı, İbrahim; Other; Other
    Cardiovascular disease (CVD) is one of the most common health problems that are responsible for one-third of all deaths around the globe. Although X-Ray angiography has deficiencies such as two-dimensional (2D) representation of three dimensional (3D) structures, vessel overlapping, noisy background, the existence of other tissues/organs in images, etc., it is used as the gold standard technique for the diagnosis and in some cases treatment of CVDs. To overcome the deficiencies, great efforts have been drawn on retrieval of actual 3D representation of coronary arterial tree from 2D X-ray angiograms. However, the proposed algorithms are based on analytical methods and enforce some constraints. With the evolution of deep neural networks, 3D reconstruction from images can be achieved effectively. In this study, we propose a new data structure for the representation of objects in a tubular shape for 3D reconstruction of arteries using deep learning. Moreover, we propose a method to generate synthetic coronaries from data of real subjects. Then, we validate tubular shape representation using 3 typical deep learning architectures with synthetic X-ray data we produced. The input to deep learning architectures is multi-view segmented X-Ray images and the output is the structured tubular representation. We compare results qualitatively in terms of visual appearance and quantitatively in terms of Chamfer Distance and Mean Squared Error. The results demonstrate that tubular representation has promising performance in 3D reconstruction of coronaries. We observe that convolutional neural network (CNN) based architectures yield better 3D reconstruction performance with 9.9e-3 on Chamfer Distance. On the other hand, LSTM-based network fails to learn the coronary tree structure and we conclude that LSTMs are not appropriate for auto-regression problems as depicted in this study.
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    Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering
    (Ankara Üniversitesi Mühendislik Fakültesi, 2022) Gadella, Manuel; Other; Other
    We propose a formulation of Gamow states, which is the part of unstable quantum states that decays exponentially, with two advantages in relation with the usual formulation of the same concept using Gamow vectors. The first advantage is that this formulation shows that Gamow states cannot be pure states, so that they may have a non-zero entropy. The second is the possibility of correctly defining averages of observables on Gamow states.
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    Design and implementation of a microcontroller based split air conditioner control system
    (Annkara Üniversitesi Mühendislik Fakültesi, 2022) Timur, Oğuzhan; Other; Other
    Different methods should be developed to work on energy efficiency in the electrical systems that do not allow outside intervention in the control part. In this study, the command and control of split air conditioners is carried out through hardware and software designed using the embedded system board. Infrared signals in the remote control device of the air conditioner were read with the developed circuit and recorded in the internal memory of the card, and these codes were used for energy efficiency studies. The obtained codes were used in 2 different applications. Thermal camera technology has been used instead of the traditional presence and motion sensors, which cannot achieve the desired success in asset detection in the absence of motion in the implemented applications. In this way, the presence of living things in the areas where the application is made has been detected with a much higher sensitivity regardless of the movement. As a result of the realized studies on the existing systems, 30% energy saving potential is determined approximately.