Elektronik ve Haberleşme Mühendisliği Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/260

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Author: search.filters.author.Cetin, A. EnisPublisher: search.filters.publisher.Ieee-inst Electrical Electronics Engineers inc

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  • Article
    Citation - WoS: 72
    Citation - Scopus: 85
    Entropy-Functional Online Adaptive Decision Fusion Framework With Application To Wildfire Detection in Video
    (Ieee-inst Electrical Electronics Engineers inc, 2012) Toreyin, Behcet Ugur; Kose, Kivanc; Cetin, A. Enis; Gunay, Osman
    In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. 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. In this case, image data arrive sequentially, and the oracle is the security guard of the forest lookout tower, verifying the decision of the combined algorithm. The simulation results are presented.
  • Article
    Citation - WoS: 12
    Citation - Scopus: 13
    An Online Adaptive Cooperation Scheme for Spectrum Sensing Based on a Second-Order Statistical Method
    (Ieee-inst Electrical Electronics Engineers inc, 2012) Toreyin, Behcet Ugur; Qaraqe, Khalid A.; Cetin, A. Enis; Yarkan, Serhan
    Spectrum sensing is one of the most important features of cognitive radio (CR) systems. Although spectrum sensing can be performed by a single CR, it is shown in the literature that cooperative techniques, including multiple CRs/sensors, improve the performance and reliability of spectrum sensing. Existing cooperation techniques usually assume a static communication scenario between the unknown source and sensors along with a fixed propagation environment class. In this paper, an online adaptive cooperation scheme is proposed for spectrum sensing to maintain the level of sensing reliability and performance under changing channel and environmental conditions. Each cooperating sensor analyzes second-order statistics of the received signal, which undergoes both correlated fast and slow fading. Autocorrelation estimation data from sensors are fused together by an adaptive weighted linear combination at the fusion center. Weight update operation is performed online through the use of orthogonal projection onto convex sets. Numerical results show that the performance of the proposed scheme is maintained for dynamically changing characteristics of the channel between an unknown source and sensors, even under different physical propagation environments. In addition, it is shown that the proposed cooperative scheme, which is based on second-order detectors, yields better results compared with the same fusion mechanism that is based on conventional energy detectors.