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It is identified by the first 2 bits of the first byte If the first bit is a 1, then the algorithm checks the second bit If the second bit is a 0, this will identify a Class B address This allows for 16,384 network numbers (1011111111111111hosthost or (2n14), with each network number capable of supporting 65,534 (2n16 2) hosts (netnet11111111111 11110) Wait, there are 16 bits in the first two fields, this should allow for 65,535 networks Since Class B reserves the first 2 bits to identify the class type (in binary, a 10xxxxxx in the first field), there are limited address numbers that may be used in the first field (valid range becomes 2n14) This translates to 128 191 (in decimal) as the allowable network numbers in the first field.

312 GNSS-Based Indoor Positioning and a Summary of Indoor Techniques TABLE 10.1 High sensitivity GNSS positioning.

Since the first field identifies the class, the second field is free to use all 8 bits, and can range from 0 to 255 The total range for network numbers for Class B addresses is 128 to 191 (in the first field), 0 to 255 (in the second field), and xxxxxx (x represents the host ID) in the third and fourth fields This is the most popular class of addresses It provides the largest range of addressing possibilities However, unless companies have handed in their Class B addresses, this class is exhausted and they are no longer given out..

At praystation.com (www.praystation.com), you can view the online Flash explorations of Joshua Davis. Click a link in the left window to experience some very compelling visual effects. The effects in this site feature some very sophisticated ActionScript. Take a look at the animations and see if you can figure out how they were created.

There is currently only one such commercially available system that includes a new method based on multiple measurements and re nement of.

Although a technical discussion of model identification is beyond the scope of this chapter (see Pearl, 2000), it is important that researchers have some understanding of this concept In nonmathematical terms, identification refers to whether one can obtain a unique solution estimate for the unknown parameters in the model If a model is identified, one and only one set of values will best reproduce the observed data, under the constraints imposed on the model by the researcher If more than one set of values can reproduce the data equally well, then the researcher will obtain only a set of arbitrary parameter estimates that have no substantive meaning regarding real-world effect sizes Such a model is referred to as underidentified If a unique set of parameters does not exist, then a model is not testable There are no fully satisfactory guidelines for identification.

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A necessary, but not sufficient, test of identification is that the number of data points is greater than the number of parameters so that df is positive (Bollen, 1989) An underidentified model with df < 0 is not identified If a model is identified, and has df = 0, it is said to be just identified Because just-identified models will always be able to perfectly fit the data, they do not provide a test of the overall model (ie, they are not falsifiable) However, the parameter estimates from a just-identified model are still useful in describing relations among the variables If a model is identified and has df > 0, the model is said to be overidentified Most CFA /SEM models are overidentified; they can be tested in relation to their ability to fit the data and are falsifiable Having more date points than estimated parameters (ie.

i (i+1)

deleting) these interfaces. We can also use inheritance to extend the interfaces in a more portable fashion. Ultimately, we can even choose to abandon the interfaces here completely. All of these choices are valid, but they have very different maintenance and reusability impacts.

, df > 0) is not sufficient to guarantee identification However, there are some fairly general conditions under.

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Falsetti & Resnick (2001)

mi is the upper bound of the uncertainty jmi k 1 j < mi. Proof: Let us de ne the following Lyapunov function:

Figure 9.15 A space diversity con guration. The vertical distance between the upper and lower antennas is of key importance.