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A new interface providing the PCRF with subscriber-related information is added in R7 to the architecture. This new interface is called Sp and is also shown in Figure 3.17. The subscriber pro le repository (SPR) contains subscriber information used for QoS and service authorisation, as well as charging information. Basically, the PCRF fetches the needed information when the subscriber establishes his rst bearer (e.g., a PDP context for GPRS access). The PCRF could cache the information for further use, but it could access the SPR later, if needed. In addition, the possibility to retrieve the SPR and PCRF information from the roaming subscriber (located in the visited PLMN) home network is being discussed. The needed information could be fairly generic for example, operator-speci c (the same for all the operator s subscribers) and precon gured in the visited PLMN gateway or PCRF. It could also be obtained from the home network either through the visited PCRF home PCRF interface or the visited PCRF home SPR interface. There are some di erences between R5 and R6 QoS and policy control and R6-based FBC solutions, which need to be addressed as these two are combined:

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Figure 6-35: The Distiller window shows the new Job Options setting used and a report of the distillation time.

You can supplement this information with a book devoted to the subject, like Wi-Fi Toys by Mike Outmesguine (Wiley, 2004)..

Now, the complex loads as de ned above are plotted on the Smith Chart (Figure 4.23). We start from the resistances of cases (a) and (b) and add a shunt susceptance and a

A use of Chebyshev ( ) polynomials is to economise expressions for the calculation of functions. The function is scaled to lie between 1 and the error has a constant ripple within this region. Chebyshev polynomials of the first kind, order n are given by [6, p.537] 1n cosh(n arccosh|x|) Tn(x) = cos(n arccos x) cosh(n arccosh x) x < 1 1 < x < 1 x>1 (17)

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The estimated process mean and standard deviation can be used to calculate the practical parameters for the I chart in Equation (13). The constant d2 value is determined by k and can be found by using k as n in Table 3. Common k values can range from 2 to 5. The MR chart is used to monitor process variability. Considering MRk as a random variable with a mean of MRk and a standard deviation of MRk , the theoretical parameters of the MR chart can then be stated as UCL = MRk + 3 MRk Centerline = MRk LCL = MRk 3 MRk Since MRk and MRk are not usually available, they can be estimated as MRk = MR k and MRk = d3 MR k d2 (18) (17)

where a is the number of failures that are not caused by network problems. For example, failures caused by incorrect settings on mobile devices include:

Prevalence of overweight (%) a 14 29 14 25 36 4 14 13 27 7 12 29 12 24 5 11 13 19 34 15 9 6 (urban 8) 8 (urban 12) 10 19 11 21

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Since the Zernike polynomials are not orthogonal over the data set, the aberration coef cients bl are not independent of each other, the mean value of a polynomial is ^ not zero, b1 is not equal to the mean value a1 of W xn ; yn , the variance of the aberration cannot be obtained by summing the squares of the aberration coef cients bl with l 2; 3; . . . ; L, and the value of a coef cient changes as the number L of the aberration terms changes. Hence, when one or more aberration terms are added or subtracted from the aberration function, the coef cients bl change. In practice, the F-polynomials are not known. What we know are the wavefront errors at an array of points within a pupil of arbitrary shape, either by ray tracing, as in optical design, or by measurement, as in optical testing, and we want to determine the Zernike coef cients that match these errors in a least squares sense. A summary of the procedure for determining these coef cients is as follows. 1. Using the Zernike circle or annular polynomials, depending on the whether the shape of the actual pupil is close to being circular or annular, determine the

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