winforms data matrix

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winforms data matrix

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That is, di (t) is upper bounded by the maximum value of and is the minimum duration required after time t to send out all the traf c accumulated up to time t. Let the target delay upper bound of ow i, with Di de ned as the maximum time that elapses between traf c arrival at a ow and its successful receipt at its destination. Also, let Dir be the maximum reservation delay of ow i, which is de ned as the time that elapses between the arrival of a packet to a ow and its successful resource reservation. This resource reservation is necessary because we focus on uplink scheduling in a centralized network architecture where the base station is assumed to be the destination of all traf c. Based on these few de nitions, in order to meet the delay upper bound Di of a ow i, the following must be valid: di (t) Di Dir , t 0 (7.33)

Their different relative shares are due to the differently designed ow control and mobility mechanisms of GPRS and UMTS A detailed explanation of the differences in these mechanisms between GPRS and UMTS and their impact on the one-way packet delay can be found in Romirer-Maierhofer and Ricciato (2008) Having visually inspected the delay distribution at two different SGSNs, we have to seek summary indicators that can be used as input signals for the detection of abnormal delay deviations These indicators must be stable over time, that is, should not present wide variations due to small uctuations in the underlying delay distributions The simplest candidate indicators are percentiles and threshold-crossing frequencies Given a continuous random variable x, the generic pth percentile dx (p) denotes the value that is exceeded with probability p (p < 1), that is, dx (p) : P (x dx (p)) = p.

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Therefore, we x the value of p and obtain from the measurements an empirical estimation of the dx (p) Conversely, we can de ne a xed threshold h in the domain of the random variable x, and denote by qx (h) the relative fraction of samples that exceed this value, that is qx (h) : P (x h) = qx (h) Such a quantity will be denoted as threshold-crossing frequency By plotting the empirical ccdf of x, it is immediately clear that the value of dx (p) relates to the horizontal distance of the.

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and arise in Equations 1.21 and 1.22 by making the homogeneous, macroscopic approximation that B = H and D = E . Often, we will use these equations for a good, nonmagnetic conductor in which there is relatively little polarization due to the electric dipole character of the propagating medium (e.g., copper) so we may use 0 and 0 in that application. In that case, k2 2 0 0(1 j / 0), and we can use a mathematical convenience of de ning an effective r,eff = (1 j / 0), which takes into account the conductivity as if it were part of the permittivity constant. Two warnings for later analysis: (1) we multiplied and divided by in factoring out 2 so we cannot consider r,eff(0) without remembering that the correct term to consider is lim ( ), and (2) D = 0 r,eff( )E only insofar as the permittivity contains the conductivity (i.e., r,eff( ) is not expressing the alignment of polar molecules).

of t On one day we obtain a total of 8640 data points in a bi-dimensional space (recall that we are using 10 s time bin) In order to summarize this data, we t the 8640 data points representing a single-day trajectory with a quadratic curve, which is a degree-two polynomial In addition to the variance, the same procedure is applied also to the skewness, so as to observe the skewness-mean trajectory The resulting curves are given in the left-hand plot of Figure 103 for 22 days The dashed and continuous lines refer respectively to days 6 20 (with the bottleneck) and days 21 27 (after the bottleneck removal) The variance-mean trajectory is such that when the bottleneck is in place the variance reaches a maximum when the mean is approximately half of the bottleneck capacity, after which it decreases again.

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