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Elektronik ve Haberleşme Mühendisliği Bölümü Yayın Koleksiyonu

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Browsing Elektronik ve Haberleşme Mühendisliği Bölümü Yayın Koleksiyonu by Journal "2012 20th Signal Processing and Communications Applications Conference, SIU 2012, Proceedings"

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    Citation Count: Günay, Osman...et al. (2012). "Entropy functional based adaptive decision fusion framework", 2012 20th Signal Processing and Communications Applications Conference, SIU 2012, Proceedings, 2012 20th Signal Processing and Communications Applications Conference, SIU 201218 April 2012 through 20 April 2012.
    Entropy functional based adaptive decision fusion framework
    (2012) Günay, Osman; Töreyin, Behçet Uǧur; Köse, Kıvanç; Çetin, A. Enis; 1932
    In this paper, an entropy functional based online adaptive decision fusion framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several sub-algorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular sub-algorithm. Decision values are linearly combined with weights which are updated online according to an active fusion method based on performing entropic projections onto convex sets describing sub-algorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. © 2012 IEEE.
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    Citation Count: Yazar, Ahmet; Çetin, A. Enis; Töreyin, B. Uǧur (2012). "Human activity classification using vibration and PIR sensors", 2012 20th Signal Processing and Communications Applications Conference, SIU 2012.
    Human activity classification using vibration and PIR sensors
    (2012) Yazar, Ahmet; Çetin, A. Enis; Töreyin, B. Uǧur; 19325
    Fall detection is an important problem for elderly people living independently and people in need of care. In this paper, a fall detection method using seismic and passive infrared (PIR) sensors is proposed. Fast Fourier transform, mel-frequency cepstrum coefficients, and discrete wavelet transform based features are extracted for classification. Seismic signals are classified into "fall" and "not a fall" classes using support vector machines. Once a moving person is detected by the PIR sensor within a region of interest, fall is detected by fusing seismic and PIR sensor decisions. The proposed system is implemented on a standard personal computer and works in real-time. © 2012 IEEE.