Cloud computing provides reliable, affordable, and flexible resources for many applications and users with constrained computing resources and capabilities. The cloud computing concept is becoming an appealing paradig...
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Cloud computing provides reliable, affordable, and flexible resources for many applications and users with constrained computing resources and capabilities. The cloud computing concept is becoming an appealing paradigm for many industries including the gaming industry, leading to the introduction of cloud gaming architectures. Despite its advantages, cloud gaming suffers from unguaranteed end-to-end delay as well as server side's computational complexity. In this paper, a novel algorithm for reducing the computational complexity and hence speeding up the video encoding speed is proposed. Specifically, by performing minimum modifications in the game engine and the video codec, some information from the game engine is fed into the video encoder to bypass the motion estimation (ME) process. Our results show that the proposed method achieves up to 39% speedup in the ME process, leading to a 24% acceleration in the total encoding process.
Inspired by scaffold filling, a recent approach for genome reconstruction from incomplete data, we consider a variant of the well-known longest common subsequence problem for the comparison of two sequences. The new p...
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Inspired by scaffold filling, a recent approach for genome reconstruction from incomplete data, we consider a variant of the well-known longest common subsequence problem for the comparison of two sequences. The new problem, called Longest Filled Common Subsequence, aims to compare a complete sequence with an incomplete one, i.e. with some missing elements. Longest Filled Common Subsequence (LFCS), given a complete sequence A, an incomplete sequence B, and a multiset M of symbols missing in B, asks for a sequence B* obtained by inserting the symbols of M into B so that B* induces a common subsequence with A of maximum length. We investigate the computational and approximation complexity of the problem and we show that it is NP-hard and APX-hard when A contains at most two occurrences of each symbol, and we give a polynomial time algorithm when the input sequences are over a constant-size alphabet. We give a 3/5-approximation algorithm for the Longest Filled Common Subsequence problem. Finally, we present a fixed-parameter algorithm for the problem, when it is parameterized by the number of symbols inserted in B that "match" symbols of A. (C) 2019 Elsevier B.V. All rights reserved.
The two- dimensional ( 2D) cubic phase function ( CPF) is known as a highly accurate 2D polynomial phase signal estimator, but it has limited applicability due to the requirement for the 3D search for second- order pa...
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The two- dimensional ( 2D) cubic phase function ( CPF) is known as a highly accurate 2D polynomial phase signal estimator, but it has limited applicability due to the requirement for the 3D search for second- order partial phase derivatives. The authors propose an interpolation- based approach simulating non- uniform ( NU) signal sampling in order to reduce the 2D CPF calculation complexity. The NU resampling enables the 2D CPF evaluation using the 2D fast Fourier transform and searches over mixed- phase parameter. The computational complexity is reduced from O( N5) to O( N3 log2 N). The additional stage with dechirping, filtering and phase unwrapping is introduced to refine parameter estimates.
Simultaneous localization and mapping (SLAM) refers to a process that permits a mobile robot to build up a map of the environment and, at the same time, to use it to compute its location. One of its most important com...
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Simultaneous localization and mapping (SLAM) refers to a process that permits a mobile robot to build up a map of the environment and, at the same time, to use it to compute its location. One of its most important components is its ability to associate the most recently perceived visual measurement to the one derived from previsited locations, a technique widely known as loop closure detection. In this article, we evolve our previous approach, dubbed as `DOSeqSLAM' by presenting a low complexity loop closure detection pipeline wherein the traversed trajectory (map) is represented by sequence-based locations (submaps). Each of these groups of images, referred to as place, is generated online through a point tracking repeatability check employed on the perceived visual sensory information. When querying the database, the proper candidate place is selected and, through an image-to-image search, the appropriate location is chosen. The method is subjected to an extensive evaluation on seven publicly available datasets, revealing a substantial improvement in computational complexity and performance over its predecessors, while performing favourably against other state-of-the art solutions. The system's effectiveness is owed to the reduced number of places, which, compared to the original approach, is at least one order of magnitude less.
The first step in the diagnosis of failure occurrences in discrete event systems is the verification of the system diagnosability. Several works have addressed this problem using either diagnosers or verifiers for bot...
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The first step in the diagnosis of failure occurrences in discrete event systems is the verification of the system diagnosability. Several works have addressed this problem using either diagnosers or verifiers for both centralized and decentralized architectures. In this technical note, we propose a new algorithm to verify decentralized diagnosability of discrete event systems. The proposed algorithm requires polynomial time in the number of states and events of the system and has lower computational complexity than all other methods found in the literature. In addition, it can also be applied to the centralized case.
Classical step-by-step algorithms, such as forward selection (FS) and stepwise (SW) methods, are computationally suitable, but yield poor results when the data contain outliers and other contaminations. Robust model s...
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Classical step-by-step algorithms, such as forward selection (FS) and stepwise (SW) methods, are computationally suitable, but yield poor results when the data contain outliers and other contaminations. Robust model selection procedures, on the other hand, are not computationally efficient or scalable to large dimensions, because they require the fitting of a large number of submodels. Robust and computationally efficient versions of FS and SW are proposed. Since FS and SW can be expressed in terms of sample correlations, simple robustifications are obtained by replacing these correlations by their robust counterparts. A pairwise approach is used to construct the robust correlation matrix-not only because of its computational advantages over the d-dimensional approach, but also because the pairwise approach is more consistent with the idea of step-by-step algorithms. The proposed robust methods have much better performance compared to standard FS and SW. Also, they are computationally very suitable and scalable to large high-dimensional data sets. (c) 2007 Elsevier B.V. All rights reserved.
We present algorithms to calculate the stability radius of optimal or approximate solutions of binary programming problems with a min-sum or min-max objective function. Our algorithms run in polynomial time if the opt...
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We present algorithms to calculate the stability radius of optimal or approximate solutions of binary programming problems with a min-sum or min-max objective function. Our algorithms run in polynomial time if the optimization problem itself is polynomially solvable. We also extend our results to the tolerance approach to sensitivity analysis. (C) 1998 Elsevier Science B.V. All rights reserved.
Haplotyping, also known as haplotype phase prediction, is the problem of predicting likely haplotypes based on genotype data. One fast haplotyping method is based on an evolutionary model in which a perfect phylogenet...
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Haplotyping, also known as haplotype phase prediction, is the problem of predicting likely haplotypes based on genotype data. One fast haplotyping method is based on an evolutionary model in which a perfect phylogenetic tree is sought that explains the observed data. Unfortunately, when data entries are missing, which is often the case in laboratory data, the resulting formal problem IPPH, which stands for incomplete perfect phylogeny haplotyping, is NP-complete. Even radically simplified versions, such as the restriction to phylogenetic trees consisting of just two directed paths from a given root, are still NP-complete;but here, at least, a fixed-parameter algorithm is known. Such drastic and ad hoc simplifications turn out to be unnecessary to make IPPH tractable: we present the first theoretical analysis of a parameterized algorithm, which we develop in the course of the paper, that works for arbitrary instances of IPPH. This tractability result is optimal insofar as we prove IPPH to be NP-complete whenever any of the parameters we consider is not fixed, but part of the input. (C) 2012 Elsevier B.V. All rights reserved.
This study considers the identification problems of multiple-output non-linear equation-error moving average systems. There exist the product items of the parameters between the non-linear and linear parts. To solve t...
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This study considers the identification problems of multiple-output non-linear equation-error moving average systems. There exist the product items of the parameters between the non-linear and linear parts. To solve this difficulty, the key term separation technique is adopted. By using the model decomposition technique and the hierarchical identification principle, a maximum likelihood-based recursive extended least-squares estimation algorithm with reduced computational complexity is presented to estimate the parameters of the non-linear part and the linear part interactively. The simulation results demonstrate the effectiveness of the proposed method.
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