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versus RF pulse width is shown for the Ti/Pt/Au and Al gate GaAs MESFET structures. As the applied pulse width increases, the incident power-to-failure decreases. From the experimental results, Whalen [10] assumed a model of incident power-to-failure versus pulse width, PI KI tn

IETF. The IETF combined Working Groups into Areas, and designated Area Directors. An Internet Engineering Steering Group (IESG) was formed of the Area Directors. The IAB recognized the increasing importance of the IETF, and restructured the standards process to explicitly recognize the IESG as the major review body for standards. The IAB also restructured so that the rest of the Task Forces (other than the IETF) were combined into an Internet Research Task Force (IRTF), with the old task forces renamed as research groups. The growth in the commercial sector brought with it increased concern regarding the standards process itself. Starting in the early 1980s (and continuing to this day), the Internet grew beyond its primarily research roots to include both a broad user community and increased commercial activity. Increased attention was paid to making the process open and fair. This coupled with a recognized need for community support of the Internet eventually led to the formation of the Internet Society in 1991, under the auspices of the Corporation for National Research Initiatives (CNRI). In 1992, the Internet Activities Board was reorganized and renamed the Internet Architecture Board, operating under the auspices of the Internet Society. A more peer relationship was defined between the new IAB and IESG, with the IETF and IESG taking a larger responsibility for the approval of standards. Ultimately, a cooperative and mutually supportive relationship was formed among the IAB, IETF, and Internet Society, with the Internet Society taking on as a goal the provision of service and other measures that would facilitate the work of the IETF.

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Because workers and businesses can find out about macroeconomics just as well as the politicians running the government, they realise that the only long-run effect of the upcoming stimulus will be for prices to rise from Po to P1 In addition, workers understand that real wages will remain unchanged in the long run, because both their nominal wages and their cost of living (given by the price level) will increase by equal amounts As a result, they know that in the long run, the stimulus isn t going to help them at all Indeed, their only hope for gains is based entirely upon the short run, when nominal wages should go up and the price level should stay the same In other words, they hope to benefit from the movement from A to B in Figure 7-2 But firms aren t stupid.

The description of an arbitrary real number requires an in nite number of bits, so a nite representation of a continuous random variable can never be perfect. How well can we do To frame the question appropriately, it is necessary to de ne the goodness of a representation of a source. This is accomplished by de ning a distortion measure which is a measure of distance between the random variable and its representation. The basic problem in rate distortion theory can then be stated as follows: Given a source distribution and a distortion measure, what is the minimum expected distortion achievable at a particular rate Or, equivalently, what is the minimum rate description required to achieve a particular distortion One of the most intriguing aspects of this theory is that joint descriptions are more ef cient than individual descriptions. It is simpler to describe an elephant and a chicken with one description than to describe each alone. This is true even for independent random variables. It is simpler to describe X1 and X2 together (at a given distortion for each) than to describe each by itself. Why don t independent problems have independent solutions The answer is found in the geometry. Apparently, rectangular grid points (arising from independent descriptions) do not ll up the space ef ciently. Rate distortion theory can be applied to both discrete and continuous random variables. The zero-error data compression theory of 5 is an important special case of rate distortion theory applied to a discrete source with zero distortion. We begin by considering the simple problem of representing a single continuous random variable by a nite number of bits. 10.1 QUANTIZATION

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