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excellent for sequences C and D, good for sequence A, and adequate for sequence B. The negative-binomial distribution is the discrete analog of the gamma distribution, and a discretized version of the latter can be used when it is more convenient to do so. Figure 12.2 shows the autocorrelation functions. The ordinate has a log scale, so geometric functions will appear as straight lines. The geometric property holds for at least 100 lags (2.5 seconds) for sequences B, C, and D, and for 50 lags for sequence A. For lags larger than 250, the geometric function underestimates the autocorrelation function. We examined sequences A, B, and C and concluded they possess longrange dependence. Since the autocorrelation functions shown in Fig. 12.2 are so large for small lags, it seem intuitive (to us, at least) that the short-range correlations should be the important ones to capture in a source model. We propose using the geometric function rk for the autocorrelation function. Since the negative-binomial and gamma distributions are speci ed by two parameters, these parameters can easily be esimated from the mean and the variance of the number of cells per frame by the method of moments. Only those two moments and the correlation coef cient (r) are needed to specify the key properties of VBR teleconference traf c. The correlation coef cient can be estimated from the geometric portion of the autocorrelation function by taking logarithms and doing a linear regression. 12.2.2 The DAR Model



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tesseract ocr java pdf

Tesseract OCR with Java with Examples - GeeksforGeeks
In this article, we will learn how to work with Tesseract OCR in Java using the ... Pre-process image data, for example: convert to gray scale, smooth, de-skew, ...

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I tried with PDFBox and it produced satisfactory results. Here is the code to extract text from PDF using PDFBox: import java.io.*; import ...

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Our rst investigations of these sequences with Tabatabai [16] and Heeke [18] focused on multiplexing issues. First, we established that the time series were stationary. This was done by examining plots of smoothed versions of the time series and boxplots of many partitions of the time series. Next, we showed a Markov chain provided a good description of the time series. This was done via simulations as described in Section 12.2.2.1. This means that the marginal distributions of the time series can be viewed as the steady-state distributions of the Markov chain. A Markov chain that has a geometric autocorrelation function and whose steady-state distribution can be speci ed is the DAR(1) process introduced by Jacobs and Lewis [20]. The only member of the DAR k family that is used here is the DAR(1), so the (1) will be deleted. The transition matrix is given by P rI 1 r Q; 12:1

Limitations The task graph as a general model does not provide any mechanism to ef ciently represent an iterative computation

Evaluation of g*

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We work under the assumption that the scaling sequence fv*; t 1; 2; . . .g satis es t Condition (ii) of Proposition 9.6.2. Fix y > 0. Setting z 0 in Eq. (9.78) we obtain b y c rin K g y : Next, going back to the expression (9.49) we note that vt yx L y sup lim inf inf x>y h tx y>0 t3I vt sup lim inf inf x c rin y x>y h tx 0<y<1 t3I vt y lim inf inf x c rin t3I x>y h tx so that vt x c rin a y Kg y lim inf inf t3I x>y h tx 9:79

9:80

Iterative Computations For iterative computations, the size of the task graph depends on the number of iterations, which directly in uences the memory consumption and the processing time of task scheduling algorithms With the loss of regularity information in the task graph, scheduling algorithms also cannot bene t from the inherent regularity of cyclic computations Furthermore, if the number of iterations is only known at runtime, the task graph cannot be constructed for the general case Still, scheduling techniques for cyclic computations (Sandnes and Megson [164], Sandnes and Sinnen [166], Yang and Fu [208]) do use the task graph and associated techniques, for example, to represent the iterative kernel

9:81

with closed forms available for speci c choices of the pmf G (Section 9.10). 9.9.2 Evaluation of g*

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If we assume that ft 1 Stb ct ; t 1; 2; . . .g satis es a large deviations principle with good rate function L*, then blind substitution into Eq. (9.78) yields g inf g y y c rin :

Another limitation is not a particular limitation of the task graph, but of all models covered in this chapter In fact, it was already introduced during the de nition of the general graph model in Section 32

9:82

Unfortunately, the Gartner Ellis theorem [9, Theorem 2.3.6, p. 45] here gives a trivial lower bound (9.34) for ft 1 Stb ct ; t 1; 2; . . .g. This fact, pointed out by Duf eld [11], precludes the use of Proposition 9.6.1 to conclude the lower bound (9.4). Nevertheless, as discussed in Section 9.11, such a lower bound does hold in many cases of interest, with case-speci c arguments needed to establish it. 9.10 EXAMPLES

The examples considered here are constructed by taking the f1; 2; . . .g-valued rv s to be of the form s st X , where X is an integrable R -valued rv with P X 0 0. 9.10.1 Integrated Tails and Forward Recurrence Times

Static Model The graph models according to De nition 37, to which the task graph model belongs, do not exhibit conditional statements of the code; that is, there is no branching These control dependences are either transformed into data dependences or encapsulated within a node (see Section 32)

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Tesseract OCR with Java with Examples - GeeksforGeeks
In this article, we will learn how to work with Tesseract OCR in Java using the ... Pre-process image data, for example : convert to gray scale, smooth, de-skew, ...












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