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possible that g ( n ) will make the system unstable, in which case the DTFT of h(n) will not exist. Feedback systems are typically analyzed using z-transforms.

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The inverse DTFT may be viewed as adecomposition of x ( n ) into alinear combination of all complex exponentials that have frequencies in the range -17 i 5 IT. Table 2- 1 contains a list of some useful DTFT pairs. w

Sequence 6(n) S(n - no) 1

In order to maximize total pro ts, a rm should hire additional units of a resource as long as each adds more to the rm s total revenue than to its total costs. The increase in total revenue is the MRP. The increase in total cost gives the marginal resource cost (MRC). If the rm is a perfect competitor in the resource market, it can hire any quantity of the variable resource at the given resource price, so MRC equals the resource price. Thus to maximize total pro ts, the rm should hire the resource until MRP equals the resource price. The declining MRP schedule then represents the rm s demand schedule for the resource.

Suppose X(eJ")consists of an impulse at frequency w = wo: X(eJ")= 6(w - wO)

Note that although x(n) is not absolutely summable, by allowing the DTFT to contain impulses, we may consider the DTFT of sequences that contain complex exponentials. As another example, if X(eJ")= r 6 ( w - 9 ) r 8 ( w computing the inverse DTFT, we find x(n) = i

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+ i e - l " w o = cos(nwo)

There are a number of properties of the DTFT that may be used to simplify the evaluation of the DTFT and its inverse. Some of these properties are described below. A summary of the DTFT properties appears in Table 2-2.

The discrete-time Fourier transform is periodic in w with a period of 2 n : ~ ( ~ j = x (,jW+zx) w )

This property follows directly from the definition of the DTFT and the periodicity of the complex exponentials:

Property Linearity Shift Time-reversal Modulation Convolution Conjugation Derivative Multiplication

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