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First, the relevance of the M =G=I input model to network traf c modeling is perhaps best explained through its connection to an attractive model for aggregate packet streams proposed by Likhanov et al [21] They show that the combined traf c generated by several independent, identically distributed (iid) on=off sources with Pareto distributed activity periods behaves in the limit, as the number of sources increases, like the M =G=I input stream with a Pareto distributed s This provides a rationale for the view that M =G=I input processes could provide a natural alternative to existing traf c models, at least for certain multiplexed applications Second, the class of M =G=I input processes is stable under multiplexing; that is, the superposition of several M =G=I processes can be represented by an M =G=I input process.



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exists is relevant For distinction in a pictorial representation, the edge of an antidependence is sometimes drawn as an arrow with a line crossing through and the edge of an output dependence as an arrow with a circle through it (Banerjee [15], Wolfe [204]) A ow dependence edge is denoted by a plain arrow Figure 38 shows a DG that uses this kind of notation for the program of Example 2: task 4 is antidependent on task 3 and also output dependent on task 2 All other dependence relations are ow, or real, dependences 331 Iteration Dependence Graph A typical utilization area for dependence graphs is in compilers The DG gives a compiler a way to capture the precedence constraints that prevent it from reordering operations in the program Parallelizing compilers usually focus on the parallelization of loops, as they commonly accommodate the largest share of computational load A logical specialization of the DG is therefore the iteration dependence graph, re ecting dependence relations in loops The theoretical background for the transition from a simple DG to an iteration dependence graph is given in Section 252, where dependence relations in loops were analyzed Reconsider the loop in Example 5, here shown in Figure 39(a) A graphical representation of the dependence relations the iteration dependence graph is given in Figure 39(b) Each instance of the statements S and T is modeled as a task and thus a node of the graph, while the edges represent the dependence relations between these instances In Figure 39(b), a node is drawn as a dot in the coordinate system spanned by the statements and the iterations of the loop The dependence distance vector corresponds to the spatial vector drawn in the graph illustration, whereby the vertical dimension is for the distinction between the tasks The short arrows going bottom up re ect the uniform dependence and the arrows going top down re ect the nonuniform dependence between instances of the tasks S and T (see also Section 252) Due to the nonuniform dependence between instances of tasks S and T, the graph is irregular.





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Mar 17, 2018 · Simple java program code to convert Image to Text ... to text using CMD Command Prompt ...Duration: 15:51 Posted: Mar 17, 2018

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How to use the Tesseract API (to perform OCR ) in your java code | T ...
18 Jan 2014 ... Hi there,. I have been working on a small app recently which reads an image and converts it into text using optical character recognition .

Third, the M=G=I model displays great exibility in capturing positive dependencies over a wide range of time scales; this is achieved very simply through the tail behavior of s (Proposition 941) The degree of positive correlation can further be characterized by the sum of the autocovariances, or index of dispersion of counts (IDCs), with the process being short-range dependent (SRD) (ie, IDC nite) if and only if E s2 is nite (Proposition 951) Insights into how temporal correlations of M=G=I input processes will affect queueing performance can be gained by analyzing the behavior of a multiplexer fed by an M =G=I input process For simplicity, we model the multiplexer as a discretetime single server system consisting of an in nite size buffer and a server with a constant release rate c (cells=slot).

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Java GUI and Tools for Tesseract OCR . Contribute to tesseract4java/ tesseract4java development by creating an account on GitHub .

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The number of customers in the input buffer at time t is denoted by qt Our performance index is the steady-state buffer tail probability P qI > b , as this quantity is indicative of the buffer over ow probability in a corresponding nite buffer system with b positions Computing these tail probabilities, either analytically or numerically, represents a challenging problem in the absence of any underlying Markov property for M =G=I inputs Instead, we focus on the simpler task of determining the asymptotic tail behavior of the queue-length distribution for large buffer size More precisely, we seek results of the form lim 1 ln P qI > b g h b 9:1.

for some positive constant g and mapping h : R 3 R ; these quantities are characterized by l, G, and c and should be easily computable. Limits such as Eq. (9.1) suggest approximations of the form P qI > b $ e h b g b 3 I : 9:2

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