On Robust Traffic Engineering in Transport Networks

Tizghadam, Ali; Leon-Garcia, Alberto

doi:10.1109/glocom.2008.ecp.456

Cited by 23 publications

(19 citation statements)

References 6 publications

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“…It is possible to replace these numerical methods by deriving an analytic model of the active contour and obtain analytic expressions for their gradients using symbolic computation. Specifically, we use Gaussian process regression (GPR) ( [10], [11]) to fit analytic functions oversubsets of original image and ExprLib ( [12], [13]) to do the update step of "Level Set Function" LSF by symbolic calculation of gradients. Furthermore, ExprLib has the capability to generate source code that represents these calculations.…”

Section: The Symbolic Active Contour Methodsmentioning

confidence: 99%

Mathematical Analysis and Computational Integration of Massive Heterogeneous Data from the Human Retina

Sangari¹,

Ardalan²,

Lambe³

et al. 2012

Technological Innovation for Value Creation

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Abstract. Modern epidemiology integrates knowledge from heterogeneouscollections of data consisting of numerical, descriptive and imaging. Largescale epidemiological studies use sophisticated statistical analysis, mathematical models using differential equations and versatile analytic tools that handle numerical data. In contrast, knowledge extraction from images and descriptive information in the form of text and diagrams remain a challenge for most fields, in particular, for diseases of the eye. In this article we provide a roadmap towards extraction of knowledge from text and images with focus on forthcoming applications to epidemiological investigation of retinal diseases, especially from existing massive heterogeneous collections of data distributed around the globe.

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Section: The Symbolic Active Contour Methodsmentioning

confidence: 99%

Mathematical Analysis and Computational Integration of Massive Heterogeneous Data from the Human Retina

Sangari¹,

Ardalan²,

Lambe³

et al. 2012

Technological Innovation for Value Creation

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“…We use P (i|j) to show the truncated (n − 1) × (n − 1) matrix that results from removing i th row and j th column of matrix P . It is shown in [10] that:…”

Section: Network Modelmentioning

confidence: 99%

“…We are interested in quantifying the betweenness of a node in the random-walk corresponding to a Markov chain. The original definition of random-walk betweenness is given in [7], but here we use a modified version defined in [10]. Consider the set of trajectories that begin at node s and terminate when the walk first arrives at node d, that is, destination node d is an absorbing node.…”

Section: Network Modelmentioning

confidence: 99%

“…Further development of the idea of criticality is studied in [10], where a mathematical framework for the definition of network criticality is given within the context of Markov chain theory.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper a network is said to be robust if its performance is not sensitive to the changes in topology, traffic or community of interest (the set of active sources/sinks for traffic). Based on this definition of robustness, we have proposed a metric, network criticality [10] to quantify the robustness of a network. * Email: alireza.bigdeli@utoronto.ca † Email: ali.tizghadam@utoronto.ca ‡ Email: alberto.leongarcia@utoronto.ca Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of network criticality, algebraic connectivity, and other graph metrics

Bigdeli

Tizghadam

Leon-Garcia

2009

Proceedings of the 1st Annual Workshop on Simplifying Complex Network for Practitioners

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The study of robustness and connectivity properties are important in the analysis of complex networks. This paper reports on an effort to compare different network topologies according to their algebraic connectivity, network criticality, average node degree, and average node betweenness. We consider different network types and study the behavior of these various metrics as scale is increased. Based on extensive simulations, we suggest some guidelines for the design and simplification of networks. The main finding is that, algebraic connectivity, network criticality, average degree, and average node betweenness capture different properties of a graph. Depending on the nature of the problem at hand, one needs to select which one is appropriate to use as the main metric for network analysis.

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CCRA: channel criticality based resource allocation in cognitive radio networks

Kumar

Minz

2015

Int J Communication

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In cognitive radio networks, signal to interference plus noise ratio (SINR) is a quantity that is used to analyze the bounds of the capacity of a channel. This is the reason for SINR being one of the important parameters toward evaluating the performance of spectrum sharing in every network. To maximize the channel utilization in any network, the SINR of a channel should be considered to be within a threshold. This also leads to lesser power consumption, and the quality of service for the licensed users is maintained. Further, reduced SINR leads to an improvement in the quality of communication in the network. In this paper, a graph theoretic measure for the efficient utilization of channels in cognitive radio networks has been proposed and is named as channel criticality based resource allocation (CCRA). Using the SINR as the weight of the graph, a novel concept of channel criticality has also been introduced in this work. The proposed CCRA technique has also been compared with the existing interference aware channel assignment (IACA) technique in terms of the channel utilization. Through simulations, the CCRA has been observed to outperform the IACA scheme. The average channel utilization of the proposed CCRA was observed to have increased by 8%, when the secondary users were introduced in the network as compared with the IACA technique. Copyright Int. J. Commun. Syst. 2017; 30: e3012 V. KUMAR AND S. MINZThe betweenness of the link k for the source and destination pair .a; b/ is given by of 12 6

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On Robust Traffic Engineering in Transport Networks

Cited by 23 publications

References 6 publications

Mathematical Analysis and Computational Integration of Massive Heterogeneous Data from the Human Retina

Mathematical Analysis and Computational Integration of Massive Heterogeneous Data from the Human Retina

Comparison of network criticality, algebraic connectivity, and other graph metrics

CCRA: channel criticality based resource allocation in cognitive radio networks

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