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Priyank Trivedi, Tejaswi Agarwal, K. Muthunagai
RANSAC is a repeating hypothesize-and-verify procedure for parameter estimation and filtering of noise or outlier data. In the traditional approach, this algorithm is evaluated without any prior information on the set of data points which leads to an increase in the number of iterations and compute time. In this paper, we present a GPU based […]
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Sergio Bernabe, Antonio Plaza
In this work, we develop a new parallel implementation of the k-means unsupervised clustering algorithm for commodity graphic processing units (GPUs), and further evaluate the performance of this newly developed algorithm in the task of classifying (in unsupervised fashion) satellite imagery available from Google Maps engine. With the ultimate goal of evaluating the classification precision […]
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Roberto Roverso
In this thesis, we explore the use of a centrally-coordinated peer-to-peer overlay as a possible solution to the live streaming problem. Our contribution lies in showing that such approach is indeed feasible given that a number of key challenges are met. The motivation behind exploring an alternative design is that, although a number of approaches […]
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Phuong H. Ha, Philippas Tsigas, Otto J. Anshus
This paper aims at bridging the gap between the lack of synchronization mechanisms in recent graphics processor (GPU) architectures and the need of synchronization mechanisms in parallel applications. Based on the intrinsic features of recent GPU architectures, we construct strong synchronization objects like wait-free and t -resilient read-modify-write objects for a general model of recent […]
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