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A wide class of discrete-time, second-order self-similar processes has been presented in this chapter. Theorem 8.3.1 gives all self-similar traf c models with independent sources and Poisson arrivals. This class of processes is simple for simulation, by nature close to real network traf c, and suitable for analytical study. In Section 8.4 lower and upper bounds for the probability distribution of queue length with selfsimilar input traf c were presented. In order to get these bounds, we split input traf c into two processes. The rst process represents typical behavior of self-similar traf c, while the second one represents large deviation cases. To analyze queue behavior under the rst process, we used the law of large numbers and results similar to the Chernov bound. The second process can be analyzed on the basis of probability computations for rare events. As we discovered, asymptotic behavior of queue length probability is strongly dependent on the ratio of server rate to the average rate of the source. Thus, to achieve good performance of a queueing system driven with self-similar traf c, the average rate of heavy-tail sources should be bounded by a suf ciently small value. When we get bounds for a probability distribution, we use one simple particular form of the self-similar process from our class. Nevertheless, methods developed in Section 8.4 can be applied to the more complicated processes presented in Section 8.3.



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Applications . OCR engines have been developed into many kinds of domain-specific OCR applications , such as receipt OCR , invoice OCR , check OCR , legal billing document OCR . They can be used for: Data entry for business documents, e.g. check, passport, invoice, bank statement and receipt.

I try to avoid getting deep into speci c tools in my books. They change so fast that enumerating speci c systems or services ends up more confusing than not over time. I ll leave that to those who do it for a living like Forrester Research and Gartner Research and those who do it for a passion like the thousands of bloggers who follow a wide variety of such things. From newcomers like Teragram Sentiment Analysis Manager (www.teragram.com/solutions/sentiment-analysis. html), which scrapes reviews from Amazon and Overstock along with blogs and tweets to monitor how well products are rated by the masses, to time-honored rms like Nielsen with their BuzzMetrics service (http://enus.nielsen.com/tab/product families/nielsen buzzmetrics), each has its own special something it brings to the party.





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The relevance of this idealization has already been illustrated by, for instance Abry et al [3], Abry and Veitch [5], and Flandrin [32, 33], and will play a key role in the next section 2236 Multiple Exponents, Multifractal Processes Property P1 (wavelet reproduction of the power law) extends further to classes of generalized scaling processes whose behavior cannot be described by a single scaling exponent, but which requires a collection, even an in nite collection, of exponents We brie y describe three classes of examples The rst example is in the spirit of the simple fractal processes described in Section 2234 Consider a generalization where the exponent h, which describes the statistics of local scaling properties, is no longer constant in time: E X t t X t 2 % jtj2h t , t 3 0.

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A backup window is the amount of time that systems can be affected by a backup. That used to mean the time that the systems were unavailable while backups were taken. Nowadays, systems can remain operational while backups go on, but they will likely suffer a performance impact unless special precautions are taken. The bottom line on backup windows is that they are getting smaller and smaller, approaching zero. In many shops, they already are zero; backups cannot cause any service interruption at all. Fortunately, even tiny backup windows are no excuse to give backups short shrift. In this section, we look at some of the techniques for shortening the duration of the interruptions caused by taking backups. Some of these techniques reduce the amount of data being backed up; others require additional specialized hardware or software to get their job done. Some techniques are specific to one vendor or another, and others work only on databases, or only on filesystems.

One consequence is that the local regularity of sample paths is no longer uniform but depends on t A class of processes called multifractional Brownian motion has been proposed [56], which satis es such a property, with h being a continuous function of t As detailed in Flandrin and Goncalves [35, 36] the time evolution of h can be traced through an analysis of the continuous wavelet transform coef cients at small scales: EjTX a; t j2 % a2h t 1 , a 3 0 This relation is to be understood as a time-dependent generalization of P1 The second class, multifractal processes, is one that allows an extremely rich scaling structure at small scales, far richer than simply fractal in general There is not the space here to give precise de nitions of such processes, nor of the related multifractal formalism.

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