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9.4.1.2 Exact Analysis of Average Bit Error Probability Many problems dealing with the BER performance of multichannel reception of differentially coherent and noncoherent detection of PSK and FSK signals in AWGN channels have a decision variable that is a quadratic form in complex-valued Gaussian random variables. In 1968 Proakis [58] developed a general expression for evaluating the BER when the decision variable is in that particular form. Indeed, the development and results originally obtained in [58] later appeared as App. B of Ref. [7] and have become classics in the annals of communication system performance literature. The most general form of the BER expression [7, Eq. (B-21)] obtained by Proakis was given in terms of the rst-order Marcum Q-function and modi ed Bessel functions of the rst kind. Although implied but not explicitly given in Refs. [58] and [7], this general form can be rewritten in terms of the generalized Marcum Q-function, Ql (., .) as (see Appendix 9A) Pb (L, t ; a, b, ) = Q1 a t , b t 1 + 1 (1 + )2L 1 1 (1 + )2L 1



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These are the reasons not to deploy disparate voice systems (such as a PBX) and separate IM systems.

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In 1975, UNIX V6 became the first version of UNIX available for widespread use outside of Bell Laboratories. From this early UNIX source code, the first major variant of UNIX was created at University of California at Berkeley. It was named the Berkeley Software Distribution (BSD). For most of the next decade, the BSD and Bell Labs versions of UNIX headed off in separate directions. BSD continued forward in the free-flowing, share-the-code manner that was the hallmark of the early Bell Labs UNIX, while AT&T started steering UNIX toward commercialization. With the formation of a separate UNIX Laboratory, which moved out of Murray Hill and down the road to Summit, New Jersey, AT&T began its attempts to commercialize UNIX. By 1984, divestiture was behind AT&T and it was ready to really start selling UNIX.

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more prominent in IT projects than they are in most other project industries This puts much greater emphasis on the tasks of project de nition and user involvement in IT than in other project situations (Morris, 1996, p 323) It is certainly the case that taking account of users and some other stakeholders needs is particularly problematical where IS design is concerned As Flowers (1996, p 87) says: The history of IS usage in organizations is littered with examples of systems that were either never used or were under-used because of poor consultation This danger is recognized within the Inquiry Report [into the failure of the London Ambulance Service computerized despatch system] which states that any future system must have total ownership by management and staff It was also seen in some of the accounts used in the analysis reported in 9 of this book.

The following section discusses methods used to assess the accuracy of a regression model, that is, a model built to predict a continuous variable. One effective way to visualize the accuracy of the model is to draw a scatterplot of the actual response values against the predicted response values. In Fig. 4.4 a model was built with the actual response values ( y) plotted against the predicted values (^). The reference y line indicates where values would lie if the model made a perfect prediction, that is, when the predicted values are equal to the actual values. A good model has points close to the line, like the model displayed in Fig. 4.4. A number of methods can be used to assess the model. The error or residual refers to the difference between the actual response value ( yi) and the predicted response value (^i ). To quantify the error over the entire test set, the squared or y absolute error is used, thus avoiding using a negative number which would bias the overall error evaluation. The mean square error and the mean absolute error sum these errors and divide the sum by the number of observations (n). Both values provide a good indication of the overall error level of the model.

In order to provide system developers with tools that will enable them to cope with the problems alluded to above by Morris (1996), White (2003) has developed a projectspeci c form of the Formal System Model and devised a schema to assist in ensuring that all project stakeholders and environmental risks are successfully identi ed at the start of a project The project-speci c form of the Formal System Model is shown in Figure 105 It incorporates all the systems concepts and other features, as discussed in 7, but aims speci cally to assist project managers to predict and manage project risk.

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