Browsing by Author "Sevimli, R. A."

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    RadGT: graph and transformer-based automotive radar point cloud segmentation
    (Institute of Electrical and Electronics Engineers, 2023-10-25) Sevimli, R. A.; Ucuncu, M.; Koç, Aykut
    The need for visual perception systems providing situational awareness to autonomous vehicles has grown significantly. While traditional deep neural networks are effective for solving 2-D Euclidean problems, point cloud analysis, particularly for radar data, contains unique challenges because of the irregular geometry of point clouds. This letter proposes a novel transformer-based architecture for radar point clouds adapted to the graph signal processing (GSP) framework, designed to handle non-Euclidean and irregular signal structures. We provide experimental results by using well-established benchmarks on the nuScenes and RadarScenes datasets to validate our proposed method.
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    System for removing shell pieces hazelnut kernels using impact vibration analysis
    (Elsevier BV, 2014-02) Çetin, A. Enis; Pearson, T. C.; Sevimli, R. A.
    A system for removing shell pieces from hazelnut kernels using impact vibration analysis was developed in which nuts are dropped onto a steel plate and the vibration signals are captured and analyzed. The mel-cepstral feature parameters, line spectral frequency values, and Fourier-domain Lebesgue features were extracted from the vibration signals. The best experimental results were obtained using the melcepstral feature parameters. The feature parameters were classified using a support vector machine (SVM), which was trained a priori using a manually classified dataset. An average recognition rate of 98.2% was achieved. An important feature of the method is that it is easily trainable, enabling it to be applicable to other nuts, including walnuts and pistachio nuts. In addition, the system can be implemented in real time. 2013 Elsevier B.V. All rights reserved