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  Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering
Number of Followers: 0  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1303-6009 - ISSN (Online) 2618-6462
Published by Ankara University Homepage  [5 journals]
  • 3D reconstruction of coronary arteries using deep networks from synthetic
           X-ray angiogram data

    • Authors: İbrahim ATLI; Osman Serdar GEDİK
      Abstract: 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.
      PubDate: Thu, 30 Jun 2022 00:00:00 +030
  • The effects of DC offset in direct-conversion receivers for WLAN systems

    • Authors: Yunus Emre ŞEN; Selçuk TAŞCIOĞLU
      Abstract: In this paper, the effects DC offset in direct-conversion receivers for WLAN systems are analyzed using both experimental and simulation data. DC offset estimation is performed by using data-aided methods which are based on the short training sequence of WLAN preamble. In the simulations, DC offset and frequency offset estimations are carried out on the signals affected by frequency selective Rayleigh fading channel and additive white Gaussian noise. Estimation performance of the methods is compared for different SNR levels and frequency offset values in terms of mean square error. Experimental data which is in the form of WLAN packets and transmitted through a wireless channel is captured by using a software defined radio. The experimental performance of the DC offset compensation methods is evaluated in terms of transmission success ratio.
      PubDate: Thu, 30 Jun 2022 00:00:00 +030
  • Averages of observables on Gamow states

    • Authors: Manuel GADELLA; Carlos SAN MILLAN
      Abstract: 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 thepossibility of correctly defining averages of observables on Gamow states.
      PubDate: Thu, 30 Jun 2022 00:00:00 +030
  • Classification of five different rice seeds grown in Turkey with deep
           learning methods

    • Authors: Bülent TUĞRUL
      Abstract: The increase in the world population and harmful environmental factors such as global warming necessitate a change in agricultural practices with the traditional method. Precision agriculture solutions offer many innovations to meet this increasing need. Using healthy, suitable and high-quality seeds is the first option that comes to mind in order to harvest more products from the fields. Seed classification is carried out in a labor-intensive manner. Due to the nature of this process, it is error-prone and also requires a high budget and time. The use of state-of-the-art methods such as Deep Learning in computer vision solutions enables the development of different applications in many areas. Rice is the most widely used grain worldwide after wheat and barley. This study aims to classify five different rice species grown in Turkey using four different Convolutional Neural Network (CNN) architectures. First, a new rice image dataset of five different species was created. Then, known and widely applied CNN architectures such as Visual Geometry Group (VGG), Residual Network (ResNet) and EfficientNets were trained and results were obtained. In addition, a new CNN architecture was designed and the results were compared with the other three architectures. The results showed that the VGG architecture generated the best accuracy value of 97%.
      PubDate: Thu, 30 Jun 2022 00:00:00 +030
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