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top to bottom, the resolution passes from = 12 ms to = 6144 .This kind of scale invariance takes its pure form in the canonical process called fractional Gaussian noise (FGN) Z(t) This discrete time stationary process, which satis es E[Z(t)] = 0, has a parametric correlation function with a ingle parameter,. 1)2 2 1 (1 )(2 ) c 2. Make Barcode In .NET .Related: 

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A cubic function is a function whose dominant term is some constant times N3 For example, I ON3 + N2 + 40N + 80 is a cubic function Similarly, a quadratic function has a dominant term that is some constant times N2,and a linear function has a dominant term that is some constant times N The expression O ( N log N ) represents a function whose dominant term is N times the logarithm of N The logarithm is a slowly growing function; for instance, the logarithm of 1,000,000 (with the typical base 2) is only 20 The logarithm grows more slowly than a square or cube (or any) root We discuss the logarithm in more depth in Section 65 Either of two functions may be smaller than the other at any given point, so claiming, for instance, that F(N) < G(N)does not make sense Instead we measure the functions' rates of growth This approach is justified for three reasons First, for cubic functions such as the one shown in Figure 62, when N is 1000 the value of the cubic function is almost entirely determined by.

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the cubic term In the function I ON3 + N* + 40N + 80, for N = 1000, the value of the function is 10,001,040,080, of which 10,000,000,000 is due to the 1 ON3 term If we were to use only the cubic term to estimate the entire function, an error of about 001 percent would result For sufficiently large N, the value of a function is largely determined by its dominant term (the meaning of szlfJiciently large varies by function) The second reason for measuring the functions' growth rates is that the exact value of the leading constant of the dominant term is not meaningful for different machines (although the relative values of the leading constant for identically growing functions might be) For instance the quality of the compiler could have a large influence on the leading constant The third reason is that small values of N generally are not important For N = 20, Figure 61 shows that all algorithms terminate within 5 ms The difference etween the best and worst algorithm is less than a blink of the eye We use Big-Oh notation to capture the most dominant term in a function and to represent the growth rate For instance, the running time of a quadratic algorithm is specified as O(N2) (pronounced "order en-squared") Big-Oh notation also allows us to establish a relative order among functions by comparing dominant terms We discuss Big-Oh notation more formally in Section 64 For small values of N (eg, those less than 40), Figure 61 shows that one curve may be initially better than another, which doesn't hold for larger values of N For example, initially the quadratic curve is better than the O(N log N) curve, but as N gets sufficiently large, the quadratic algorithm loses its advantage For small amounts of input, making comparisons between functions is difficult because leading constants become very significant The function N + 2500 is larger than N 2 when N is less than 50 Eventually, the linear function is always less than the quadratic function Most important, for small input sizes the running times are generally inconsequential, so we need not worry about them For instance, Figure 6 I shows that when N is less than 25, all four algorithms run in less than 10 ms Consequently, when input sizes are very small, a good rule of thumb is to use the simplest algorithm Figure 62 clearly demonstrates the differences between the various curves for large input sizes A linear algorithm solves a problem of size 10,000 in a small fraction of a second The O(N log N) algorithm uses roughly 10 times as much time Note that the actual time differences depend on the constants involved and thus might be more or less Depending on.

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