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This structure may be simplified by noting that the two sets of delays are delaying the same sequence. Therefore, they may be combined as illustrated in Fig. 8-8 for the case in which p = q. The computational requirements for a direct form I1 structure are as follows: Number of multiplications: p Number of additions: p

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P3 : F3 23 etc. These first few are true because of the following relationships: P1 : F1 = 1 2 F2 = 2 4 P2 : F3 8 P3 : For the inductive step, we assume that n 1 statements P1 , P2, P3 , . . . , Pn 1 are true and compare them with the nth statement Pn: P1 : P2 : P3 : : Pn 2: Pn 1: F1 F2 F3 : Fn 2 Fn 1 2n 2 2n 1 21 22 23

Number of delays: max(p. q ) The direct form I1 structure is said to be canonic because it uses the minimum number of delays for a given H(z).

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Theorem A.2 Laws of Logarithms 1. log b (b y) = y 2. b logb x = x 3. log b uv = log b u + log b v 4. log b u/v = log b u log b v 5. log b u v = v log b u 6. log b x = (log c x)/(log c b) = (log b c) (log c x) 7. For a positive integer n, lg n + 1 = lgn + 1. EXAMPLE A.1 Applying the Laws of Logarithms

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Pn : Fn 2n Comparing the nth statement with the two that precede it, we see that Fn = Fn 1 + Fn 2 2n = (2)(2n 1) = 2n 1 + 2n 1 > 2n 1 + 2n 2 So we can derive Pn from Pn 1 and Pn 2 like this: Fn = Fn 1 + Fn 2 2n 1 2n 2 < 2n 2n for all n). This proves that all the statements are true (i.e., Fn

Because there are six alternations of I H (eJ'")12in the passband, N filter,

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log 2 256 = log 2 (28) = 8 log 2 1000 = (log 10 1000)/(log 10 2) = 3/0.30103 = 9.966 log 2 1,000,000,000,000 = log 2 10004 = 4(log 2 1000) = 4(9.966) = 39.86 (ln n)/(lg n) = (log e n)/(log 2 n) = log e 2 = ln 2 = 0.693147, for any n > 1

where A is a constant. Applying the bilinear transformation to HJs) results in a discrete-time filter with a system function H(z) that has five poles inside the unit circle, and five zeros on the unit circle at := - I (as shown in hob. 9.29, the five zeros come from the five zeros in H,,(s) at s = m).

APPENDIX]

Use the bilinear transformation to design a discrete-time Chebyshev high-pass filter with an equiripple passband with

ASYMPTOTIC COMPLEXITY CLASSES In computer science, algorithms are classified by their complexity functions. These are functions that describe an algorithm s running time relative to the size of the problem. For example, the Bubble Sort belongs to the complexity class (n2). This means that if the Bubble Sort takes T milliseconds to sort an array of n elements, then it will take about 4T milliseconds to sort an array of 2n elements because (2n)2 = 4n2. The symbol () is one of five symbols used to describe complexity functions. They all can be defined in terms of the ratios of f(n) and g(n), where f(n) is the algorithm s timing function and g(n) is a characterizing function such as lg n or n2. For a given function g(n), the five asymptotic complexity classes are O(g(n)) = { f(n) S | f(n)/g(n) is bounded } (g(n)) = { f(n) S | g(n)/f(n) is bounded } (g(n)) = { f(n) S | f(n)/g(n) is bounded and g(n)/f(n) is bounded } o(g(n)) = { f(n) S | f(n)/g(n) 0 as n } (g(n)) = { f(n) S | g(n)/f(n) 0 as n } These definitions assume that f(n) and g(n) that are positive ascending functions. As sets of functions, o(g) O(g) (g) (g) (g) = O(g) (g) EXAMPLE A.2 Asymptotic Growth Classes

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