Browsing by Subject "Least mean squares"

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    Efficient estimation of graph signals with adaptive sampling
    (IEEE, 2020) Ahmadi, Mohammad Javad; Arablouei, R.; Abdolee, R.
    We propose two new least mean squares (LMS)-based algorithms for adaptive estimation of graph signals that improve the convergence speed of the LMS algorithm while preserving its low computational complexity. The first algorithm, named extended least mean squares (ELMS), extends the LMS algorithm by virtue of reusing the signal vectors of previous iterations alongside the signal available at the current iteration. Utilizing the previous signal vectors accelerates the convergence of the ELMS algorithm at the expense of higher steady-state error compared to the LMS algorithm. To further improve the performance, we propose the fast ELMS (FELMS) algorithm in which the influence of the signal vectors of previous iterations is controlled by optimizing the gradient of the mean-square deviation (GMSD). The FELMS algorithm converges faster than the ELMS algorithm and has steady-state errors comparable to that of the LMS algorithm. We analyze the mean-square performance of ELMS and FELMS algorithms theoretically and derive the respective convergence conditions as well as the predicted MSD values. In addition, we present an adaptive sampling strategy in which the sampling probability of each node is changed according to the GMSD of the node. Computer simulations using both synthetic and real data validate the theoretical results and demonstrate the merits of the proposed algorithms.
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    Logarithmic regret bound over diffusion based distributed estimation
    (IEEE, 2014) Sayın, Muhammed O.; Vanlı, Nuri Denizcan; Kozat, Süleyman Serdar
    We provide a logarithmic upper-bound on the regret function of the diffusion implementation for the distributed estimation. For certain learning rates, the bound shows guaranteed performance convergence of the distributed least mean square (DLMS) algorithms to the performance of the best estimation generated with hindsight of spatial and temporal data. We use a new cost definition for distributed estimation based on the widely-used statistical performance measures and the corresponding global regret function. Then, for certain learning rates, we provide an upper-bound on the global regret function without any statistical assumptions.
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    Video based fire detection at night
    (IEEE, 2009) Taşdemir, Kasım; Günay, Osman; Töreyin, Behçet Uğur; Çetin, A. Enis
    There has been increasing interest in the study of video based fire detection as video based surveillance systems become widely available for indoor and outdoor monitoring applications. Video based fire detection methods in computer vision literature do not take into account whether the fire takes place in the day time or at night. A novel method explicitly developed for video based detection of fire at night (in the dark) is presented in this paper. The method comprises three sub-algorithms each of which characterizes certain part of fire at night. Individual decisions of the sub-algorithms are combined together using a least-mean-square based decision fusion approach.