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+ 12(2)"]u(n).

+ ~ ] ( i ) ~ u ( (nb)). y(n) = (n + I ) ( f ) n u ( n ) .

Solved Problem 15.1 a. Why does a prospective monopolistic competitor nd it relatively easy to start production in the long run b. Why does the demand curve of a monopolistic competitor shift down when more rms start production c. Why is it dif cult or impossible to de ne the industry under monopolistic competition d. Why is there a cluster of prices rather than a single equilibrium price in this kind of industry Solution: a. A prospective monopolistic competitor usually nds it relatively easy to start production because very little capital and no great technical know-how are required to open a small gasoline station, grocery store, barber shop, etc. b. When more rms start producing a differentiated product, the demand curve of previously existing monopolistic competitors shifts down because each rm now has a smaller share of the market. c. Technically speaking, we cannot de ne the monopolistically competitive industry because each rm produces a somewhat different product. We simply cannot add together aspirins, Bufferins, Excedrins, etc. to get the market demand and supply curve because they are similar, but not identical, products. Thus, our graphical analysis must be con ned to the typical or representative rm. d. Slightly differentiated products also permit and cause slightly different prices. That is, even in long-run equilibrium, there will be a cluster of equilibrium prices, one for each differentiated product, rather than a single, industry-wide equilibrium price.

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2

The Fourier representation of signals plays an extremely important role in both continuous-time and discrete-time signal processing. It provides a method for mapping signals into another "domain" in which to manipulate them. What makes the Fourier representation particularly useful is the property that the convolution operation is mapped to multiplication. In addition, the Fourier transform provides a different way to interpret signals and systems. In this chapter we will develop the discrete-time Fourier transform (i.e., a Fourier transform for discrete-time signals). We will show how complex exponentials are eigenfunctions of linear shift-invariant (LSI) systems and how this property leads to the notion of a frequency response representation of LSI systems. Finally, we will explore how the discrete-time Fourier transform may be used to solve linear constant-coefficient difference equations and perform convolutions.

Eigenfunctions of linear shift-invariant systems are sequences that, when input to the system, pass through with only a change in (complex) amplitude. That is to say, if the input is x(n), the output is y(n) = kx(n), where A, the eigenvalue, generally depends on the input x(n).

Printcap name = <printer configuration file> Load printers = <yes, no> Print shares can be defined either by a share name enclosed in square brackets or by using the special [printers] share section. [print share name] comment = <text description> printer = <printcap printer name> path = <spooling directory> writeable = no guest ok = yes printable = yes The special [printers] share section represents the set of printer names listed in the printer configuration database identified by the printcap name parameter. The list is searched for a match using the client supplied printer name. If a match is found, a temporary share is set up using the matched name by cloning the parameters defined in the [printers] share. Share-level parameters include the definition of command scripts to be executed when printing or interacting with a print queue from a remote client. Command scripts are strings that represent any valid UNIX command or script: print command = lpr r P%p %s lpq command = lpq %p lprm command = lprm -P%p %j A set of substitution variables is provided to identify the printer name, spool file name, and job number: %p %j %s %f printer name integer job number spool file name spool file name without full path prefix

Signals of the form ~ ( n = ejnw )

where w is a constant, are eigenfunctions of LSI systems. This may be shown from the convolution sum:

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