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A natural generalization consists of considering wavelets with more than one vanishing moment, in the sense of Eq (25), since assuming that the number of vanishing moments is N allows the analysis to be blind to polynomials of order up to N 1 In other words, the removal of a polynomial trend of order p is guaranteed by a wavelet with N ! p 1 From a practical point of view, when p is the unknown order of a polynomial approximation of a trend, trend removal amounts to analyzing the data with different wavelets such that N 1; 2; Until the effective value N p 1 is reached, the analysis is governed by the trend and gives N-dependent results, whereas, as soon as N ! p 1, stabilized results are obtained and reveal relevant features of the detrended data.

Notes: *Numerical results are measured as the average number of computations required to admit a client at a given average network utilization ( ), averaged over requests for all 274 videos. # Numbers in parentheses are the average number of unsuccessful admission tests performed for each request.

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Exact detrending is expected to occur in the case of polynomial trends, but it is worthwhile to remark that the procedure still remains effective in the case of nonpolynomial trends, including many power-law trends, and oscillatory functions In Fig 26 an example is given of an FGN series contaminated with linear and sinusoidal trends Although the logscale diagram with N 2 is free from the effects of the linear trend (top right), higher values of N are required to effectively remove the sinusoidal trend (bottom right) Note that increasing N unfortunately also decreases the number of scales available for the analysis The usefulness of an ability to effectively remove smooth nonpolynomial trends is again illustrated in Fig 2.

To admit a new stream, network resource reservations are performed according to the generated transmission schedules on a per-stream basis. Admission test for schedules generated by optimal smoothing is performed with a round length of one second (cf. Section 7.3.2). Each simulation run simulates a duration of 3,000 days. We summarize the results in the following sections.

7, where, using a value of N as low as 2, a change in mean level in Ethernet byte data is shown to not affect the estimation of the exponent a of long-range dependence See Veitch and Abry [74], Fig 9, for further discussion The data set is derived from the ``pOct'' Bellcore trace [43] The versatility of wavelets with respect to the freedom of choice of N makes them an easy and ef cient tool for trend removal This contrasts with other methods, such as the Whittle estimator and parametric estimators in general, whose performance is heavily affected by trends (see Abry and Veitch [5, Fig 2 and Table 1]) An important advantage is hat one can choose N to eliminate preselected trends, without having to know if they are actually present a priori nor to jointly estimate their characteristics 234.

The starch polymer possesses negatively charged side chains (carboxylates) that can interact with proteins and hinder migration by an ion-exchange retention mechanism, so that starch gels, like cellulose, may possess both electrophoretic and chromatographic properties The negative charge on the gel also leads to a phenomenon called electroosmosis, wherein positively charged counterions (H3O ) in the running buffer move toward the cathode In effect, the buffer ows toward the cathode, and the overall transport of analyte species in the gel is the sum of transport due to electrophoretic migration and transport due to electroosmosis The electroosmotic effect can be quantitated using blue dextran, an uncharged oligosaccharide that possesses zero electrophoretic mobility Blue dextran may be added to a sample, and the migration of the blue dextran from the sample origin is measured after electrophoresis and subtracted from other measured distances to provide corrected migration distances.

2 Time Varying Scaling Exponents Because of the high variability inherent in scaling processes, instances of scaling behavior can incorrectly be judged as ``nonstationarity,'' in the broad sense of unstructured time variation, or conversely, variability due to nonstationarity may be erroneously taken to be scaling in nature..

Table 7.1 compares the admission complexity of Optimal Smoothing and the MDR scheduler. The simulation results are obtained from counting the average number of computations required to admit a new client. We separate the computations incurred in unsuccessful and successful admissions. For unsuccessful admissions, the computation complexity is comparable for the MDR scheduler and the Optimal Smoothing scheduler. By contrast, the MDR scheduler requires signi cantly fewer computations than the Optimal Smoothing scheduler for successful admissions, which dominates the total admission complexity.

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