Browsing by Subject "Selection algorithm"

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    ItemOpen Access
    Efficient transport of time-varying IP traffic in flexi-grid optical networks
    (IEEE, 2014) Tunc, Çağlar; Akar, Nail
    Frequency slot width selection in flexi-grid optical networks refers to the process of online update of the slot width of the channel, according to time-varying traffic demands. Since slot width updates are associated with signaling costs, it is common to limit the rate of updates. In this article, we propose a model-free hysteresis-based slot width selection algorithm for flexi-grid optical networks. © 2014 IEEE.
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    Fundamental limits and improved algorithms for linear least-squares wireless position estimation
    (John Wiley & Sons, 2010-09-22) Guvenc, I.; Gezici, Sinan; Sahinoglu Z.
    In this paper, theoretical lower bounds on performance of linear least-squares (LLS) position estimators are obtained, and performance differences between LLS and nonlinear least-squares (NLS) position estimators are quantified. In addition, two techniques are proposed in order to improve the performance of the LLS approach. First, a reference selection algorithm is proposed to optimally select the measurement that is used for linearizing the other measurements in an LLS estimator. Then, a maximum likelihood approach is proposed, which takes correlations between different measurements into account in order to reduce average position estimation errors. Simulations are performed to evaluate the theoretical limits and to compare performance of various LLS estimators.
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    Mining of remote sensing image archives using spatial relationship histograms
    (IEEE, 2008-07) Kalaycılar, Fırat; Kale, Aslı; Zamalieva, Daniya; Aksoy, Selim
    We describe a new image representation using spatial relationship histograms that extend our earlier work on modeling image content using attributed relational graphs. These histograms are constructed by classifying the regions in an image, computing the topological and distance-based spatial relationships between these regions, and counting the number of times different groups of regions are observed in the image. We also describe a selection algorithm that produces very compact representations by identifying the distinguishing region groups that are frequently found in a particular class of scenes but rarely exist in others. Experiments using Ikonos scenes illustrate the effectiveness of the proposed representation in retrieval of images containing complex types of scenes such as dense and sparse urban areas. © 2008 IEEE.