Yazar "Canbilen, Ayse Elif" seçeneğine göre listele

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    BER Analysis of SM-MIMO Systems with MRC Detectors over Weibull Fading Channels
    (IEEE, 2017) Canbilen, Ayse Elif; Gultekin, Seyfettin Sinan; Develi, Ibrahim
    In this study, hit error rate (BER) performances of 2x4 and 2x8 spatial modulation multiple input multiple output (SM-MIMO) systems over Weibull fading channels are investigated. Transmitted data and the transmitter antenna which conveys the data are estimated by using iterative maximum ratio combining algorithm at the receiver side. BER performance is obtained by changing the value of fading parameter and the level of modulation for several scenarios with computer simulations. While reducing the computational complexity of similar SM-MIMO system which uses maximum likelihood for the estimation procedure, it is obtained up to 13 dB better performance.
  • Küçük Resim Yok
    Öğe
    A Novel Approach for the Classification of Liver MR Images Using Complex Orthogonal Ripplet-II and Wavelet-Based Transforms
    (SPRINGER INTERNATIONAL PUBLISHING AG, 2018) Canbilen, Ayse Elif; Ceylan, Murat
    This study presents a decision support system aid to radiologists for defining focal lesions and making diagnosis more accurate by using liver magnetic resonance images. A new method called the complex orthogonal Ripplet-II transform is proposed as a feature extraction procedure. Artificial neural network is utilized to classify the obtained features as a hemangioma or cyst. The results are evaluated with the results of the systems using Ridgelet, Ripplet type-II and orthogonal Ripplet type-II transforms. The highest accuracy ratio (85.3%) and area under curve value (0.92) are achived by the complex orthogonal Ripplet-II transform. The accuracy of the classification procedure is increased up to 95.6% by a combined system that collectively analyzes the results obtained from the artificial neural network outputs of the two methods (Ridgelet and complex orthogonal Ripplet-II transforms). While this combined system is built up of three methods (adding Ripplet type-II), the accuracy rate reaches 97.06% and the area under curve value to 0.99.