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As a result, we can construct Markov and LRD traf c models as we wish Then we de ne loss performance measures in the transient state In Section 133, we compare transient loss performance between the traditional Markov models and the LRD models To keep the comparison reasonable, for the Markov and LRD models, except for the distributions of the times spent by the traf c processes in their respective states, we let all other traf c parameters be the same By doing so, the difference in loss behavior between Markov and LRD traf c is only due to the modeling assumption on the underlying traf c process We then compare transient loss of Markov and LRD traf c for two cases.



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Feb 20, 2018 · Optical Character Recognition, or OCR is a technology that enables you to ... There are a couple of open source frameworks that can be used to build an OCR ... JMagick — JMagick is the java interface for ImageMagick C-API.

Proof Assume the optimal schedule Sopt (P) for the smaller system is given By adding one processor to the schedule without using it (ie, the processor is idle), we are not changing the original schedule Yet, this schedule is a feasible schedule S(P+1 ) for the larger system Hence, sl(S(P+1 )) = sl(Sopt (P)) and for the optimal schedule on P+1 it must hold that sl(Sopt (P+1 )) sl(Sopt (P)) In Section 432 it will be seen that the situation is less trivial, when the number of used processors (De nition 411), that is, nonidle, is considered





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A Java JNA wrapper for Tesseract OCR API. Tess4J is released and distributed under the Apache License, v2.0 and is also available from Maven Central ...

In the rst case, we assume that both traf c processes are in the same state with the same initial condition characterized by the amount of traf c left in the system when the processes enter the state In the second case, we consider two-state Markov and LRD uids To examine whether it is appropriate to predict loss performance computed according to Markov models in steady state for LRD traf c, in Section 134, we show how to compute steady-state limits of transient loss measures for general two-state uids, and compare transient loss against loss in steady state In Section 135, we discuss the impact of long-range dependence in network traf c, based on the analytical and numerical results obtained We conclude this chapter in Section 13.

6, with a summary of the ndings of our study, and a brief discussion on the challenge posed by transient performance guarantee in the presence of longrange dependence and some extension of this work Section 137 contains two appendixes 132 TRAFFIC MODELS AND TRANSIENT LOSS MEASURES.

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We adopt general uid-type stochastic processes with multiple states as a framework for traf c modeling. The state of such a process is associated with the bit rate of the underlying traf c source. When the process is in a given state, the source generates traf c at a constant bit rate. The bit rates are different for different states. Such uidtype traf c models have been used in many previous studies for traf c engineering. A well-known example is the Markov-modulated uid model [5]. However, the traf c model in our study is essentially different from traditional uid traf c models: our traf c model is not necessarily Markovian, which allows us to capture the

property of long-range dependence in traf c A special case of our traf c model is the general two-state uid, which can capture the most important traf c properties such as long-range dependence and burstiness An important part of this work is based on the two-state uid model Various on=off uid models are special cases of the general two-state uid and have widely been used for traf c modeling For example, on=off sources with heavy-tailed on=off periods are proposed to explain long-range dependence or self-similarity in traf c [18] For an on=off uid, no traf c is generated in the off state In this book, on=off traf c models are also considered in s 5, 7, 11, and 17 Let us denote a uid-type traf c process by R t The physical meaning of R t is the time-dependent bit rate of the underlying traf c source.

Denote R t by R n for t P tn ; tn 1 , where tn is the instant at which the nth transition of the state of R t occurs Accordingly, tn ; tn 1 is an interval during which R t remains unchanged Suppose that the bit rate of the traf c source is r during the interval, that is, R n r Denote the length of the interval tn ; tn 1 by Dtn Clearly Dtn is a random variable, representing the time spent by R t in the state in which the bit rate of the traf c source is r Suppose that Dtn obeys a distribution FDtn s PfDtn sg We assume that the distributions of Dtn are the same when the traf c process is in the same state but may differ for different states.

Then we need to implement equals():

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An option if you want to also download the Aspose.OCR for Java API - Example Source Codes (To copy/import later into the project with the help of Aspose.OCR Example wizard). Aspose.OCR Example wizard lets you create / copy or import the downloaded Aspose.OCR for Java API - Example Source Codes into the project.












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