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because one side of this inequality differs from the other in that the a s and b s in Eq. (6) would be interchanged, but the subscripts would not and none of the elements of Mc would remain the same under such a change. (That is, the symbols would be different. It is possible that the numerical values might turn out to be the same.)
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Planning and estimation is one of the most challenging tasks in software development. The Standish Group 2004 Report [1] validates this conjecture by stating that 18% of software projects failed completely (canceled prior to completion, or delivered and never used); another 53% were challenged (late, over budget, and/or with less than the required features and functions); and only 29% of the surveyed projects succeeded as planned. The dif culty in estimating the costs and schedules of software projects stems not only from the fact that each product is unique, but also from the fact that perpetual technological advances very quickly render estimation models outdated. Common planning problems fall into the following two categories: 1. Underestimation of efforts due to unforeseen project circumstances, such as an increase of project scope 2. Unplanned activities, such as the need for clari cation of requirements, redesign, or additional debugging Through the iterative approach to planning, some of the estimation problems can be alleviated. As the project progresses, a higher degree of approximation accuracy can be achieved. The software development plan described in this chapter is a living document and its rst version should be created after the core requirements have been speci ed and a high-level architecture has been outlined. Ideally, the plan should be reviewed after each iteration and updated as needed. As the project progresses, the technology infrastructure might require further adjustments, customization, and expansion. At the same time, the people infrastructure might need to be reassessed and augmented to match the needs of the project. While not limited to this phase, these activities should have high priority during extended planning. In this chapter, we explain how to create an initial software development plan and how this plan should be adjusted as the product iterations progress. We also show examples of the extended infrastructures necessary to meet the needs of applications such as embedded systems, web applications, web services, and databases. The expected work products for this phase are:
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11.2.1. Tensor products The tensor product allows us to obtain a tensor expression of the transition probabilities matrices of discrete time processes with K components. But it also translates at the generators level of the CTMC of continuous time processes, the impact of synchronization transitions which simultaneously produce state modi cations in several subsystems. It is mainly this property that we will use since we will study continuous time models. DEFINITION 11.2 (Tensor product). Let A Mn1 ,p1 and B Mn2 ,p2 . The tensor product ( ) of A and B is the matrix C Mn1 n2 ,p1 p2 : C=A B with cij = ai1 j1 bi2 j2
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FIGURE 9.5 Scan performance of the ring FSS considered in Figure 9.4 at transmit-band and reflect-band frequencies for 22 5 scan plane.
Information Collection (Performed by the Measurement Collection Function): Information must be collected about the network in order to feed the QoS assurance decision-making process. Information Processing and Use: Once information has been collected, it must be processed and algorithms must be used to support ACF and QAF decisions.
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