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Tesseract is an OCR engine with support for unicode and the ability to recognize more than 100 languages out of the box. It can be trained to recognize other ...

Consequently, for a link carrying LRD traf c, the discrepany between steady-state performance and transient performance can be more signi cant, compared with the situations in which traf c can be modeled by Markov processes Second, to guarantee QoS for traf c with the high variability caused by long-range dependence, dynamic and adaptive resource allocation may be necessary to account for the effect of the current system state Performance analysis based on the steady state may not be appropriate for this purpose, since in steady-state, any initial effect will disappear eventually Because steady-state performance may differ from transient performance signi cantly for LRD traf c, the image regarding loss behavior of LRD traf c in the transient state still largely remains vague.



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more common, probably because it s a little harder to see which power outlets are on the same circuit. The extreme at the other end is to obtain power from two different power grids or power companies. While this will provide system protection against some major outages, it can be prohibitively expensive and may introduce problems with grounding or other electrical issues.





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In this chapter, an approach different from conventional transient analysis is used, which allows us to investigate the transient performance impact of long-range dependence in traf c without rst seeking a closed-form transient solution That is, we limit our analysis to some short period of time, and even to a single state of a traf c process The reasons for us to adopt this approach are as follows First, it is relatively easy, and may also be suf cient, to consider transient solutions de ned only for a relatively short time period, since a short time period may actually cover the time span in which we are interested for transient performance analysis A large time span may be less interesting from a point of view of transient analysis, since the difference between the steady state and the transient state may diminish signi cantly after a long time has elapsed.

Second, if the arrival process is renewal type, then the behavior of the system is probabilistically periodic So it may be suf cient to focus only on a ``typical'' probabilistic period to study the transient performance of the.

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One customer I had was very concerned about power outages. He went to the extreme measure of running power to the building from two separate power companies. The problem was that they chose to run both lines into the building through the same power pole in the parking lot in front of the building. When a car knocked down that pole, power from both companies was interrupted until the pole could be re-erected and the lines rerun. Evan

One of the major challenges in designing modern communication networks is providing quality of service to the individual users. An important part of this design process is understanding statistical characteristics of network traf c streams and their impact on network performance. Unlike the conventional voice traf c, modern data traf c exhibits an increased level of ``burstiness'' that spans over multiple time scales. It was observed that sample paths of these data sequences show evidence of self-similarity. Their autocorrelation structure is characterized by long-range dependency and the empirical distributions are easily matched with subexponential and long-tailed distributions. Early discovery of the self-similar nature of Ethernet traf c was reported in Leland et al. [42] (see also Leland et al. [43]). More recently, Crovella [22] attributed the long-range dependency of Ethernet traf c to the longtailed le sizes that are transferred over the network. Long-range dependency of the variable bit rate video traf c was demonstrated by Beran et al. [9]. Long-tailed characteristics of the scene length distribution of MPEG video streams were explored in Heyman and Lakshman [30] and Jelenkovic et al. [37]. Practical importance, novelty, and the intriguing nature of these phenomena have attracted a great number of scientists to develop new traf c models and to understand the impact of these models on network performance. In this development there

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