Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Citation - Scopus: 5Performance Comparison of Combined Collaborative Filtering Algorithms for Recommender Systems(Institute of Electrical and Electronics Engineers Inc., 2012) Tapucu, Dilek; Kasap, Seda; Tekbacak, FatihRecommender systems have a goal to make personalized recommendations by using filtering algorithms. Collaborative filtering (CF) is one of the most popular techniques for recommender systems. As usual, huge number of the datasets on the Internet increase the amount of time to work on data. This challenge enforces people to improve better algorithms for processing data with user preferences and recommending the most appropriate item to the users. In this paper, we analyze CF algorithms and present results for combined user-based/item-based CF algorithms for different size of datasets. Our goal is to show combined solution results using Loglikelihood, Spearman, Tanimoto and Pearson algorithms. The contribution is to describe which user based CF algorithms and user/item based combined CF algorithms perform better according to dataset, sparsity, execution time and k-neighborhood values. © 2012 IEEE.Conference Object Approximate Best Linear Unbiased Channel Estimation for Frequency Selective Channels With Long Delay Spreads: Robustness To Timing and Carrier Offsets(Institute of Electrical and Electronics Engineers Inc., 2005) Özen, Serdar; Nerayanuru, Sreenivasa M.; Pladdy, Christopher; Fimoff, Mark J.We provide an iterative and a non-iterative channel impulse response (CIR) estimation algorithm for communication systems which utilize a periodically transmitted training sequence within a continuous stream of information symbols. The iterative procedure calculates the (semi-blind) Best Linear Unbiased Estimate (BLUE) of the CIR. The non-iterative version is an approximation to the BLUE CIR estimate, denoted by a-BLUE, achieving almost similar performance, with much lower complexity. Indeed we show that, with reasonable assumptions, a-BLUE channel estimate can be obtained by using a stored copy of a pre-computed matrix in the receiver which enables the use of the initial CIR estimate by the subsequent equalizer tap weight calculator. Simulation results are provided to demonstrate the performance of the novel algorithms for 8-VSB ATSC Digital TV system. We also provide a simulation study of the robustness of the a-BLUE algorithm to timing and carrier phase offsets.Conference Object Citation - WoS: 6Citation - Scopus: 11Dynamically Adaptive Partition-Based Data Distribution Management(Institute of Electrical and Electronics Engineers Inc., 2005) Kumova, Bora İsmailPerformance and scalability of distributed simulations depends primarily on the effectiveness of the employed data distribution management (DDM) algorithm, which aims at reducing the overall computational and messaging effort on the shared data to a necessary minimum. Existing DDM approaches, which are variations and combinations of two basic techniques, namely region-based and grid-based techniques, perform purely in the presence of load differences. We introduce the partition-based technique that allows for variable-size partitioning shared data. Based on this technique, a novel DDM algorithm is introduced that is dynamically adaptive to cluster formations in the shared data as well as in the physical location of the simulation objects. Since the re-distribution is sensitive to inter-relationships between shared data and simulation objects, a balanced constellation has the additional advantage to be of minimal messaging effort. Furthermore, dynamic system scalability is facilitated, as bottlenecks are avoided.Conference Object Rank Kestirim Yöntemi Kullanarak Svd Tabanlı Gürültü Filtreleme(Institute of Electrical and Electronics Engineers Inc., 2004) Çek, Mehmet Emre; Savacı, Ferit AcarIn this paper, an algorithm which performs the singular values decomposition of a noisy matrix has been presented in order to make noise reduction by rank estimation of the noise free data matrix. In this study the rank estimation methodis done by finding ratios of among all consecutive singular values and selecting the maximum of these ratios as the noise threshold.