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Timing is everything. When you are planning a launch, you need to have a plan B up your sleeve in case some unforeseen event occurs such as the Princess of Wales untimely death. At the very least, you should establish a point of no return. If tragedy occurs up to point X, you abort. After point X, you carry on regardless. Some events such as peak summer vacation time and a presidential election can be forecast. You will be wise to avoid periods when your target s primary attention is likely to be diverted by Thanksgiving or Christmas unless there is some peculiar spin-off (you write games software). Even events such as the Olympics, the soccer World Cup, or the Super Bowl and peak holiday periods may be better avoided.

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In turn, the site hands you some random error message to explain the denial If this happens, check out another method in this chapter that actually works..

Infrared Data Association (IrDA)

This unit implements and supports the environmental variables of WAVE-WP, which may be related to individual multiple nodes and individual multiple waves kept in the current WI, and also to the interpreter itself and its underlying hardware, as well as to the external world Knowledge Network Keeps part of the distributed knowledge network that resides in the current interpreter, that is, its nodes and links between them, as well as.

Table 16-5

Figure 4.13 Schematic process for the production of copper foil used in printed circuit boards: (a)

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This chapter has presented auto-tuning of RRM parameters of UMTS network based on fuzzy logic controllers, with a special focus on design and optimisation The fuzzy logic controller (FLC) is shown to be a simple and effective framework for designing a controller that orchestrates the auto-tuning process In the design of an FLC, engineering rules are presented in terms of a linguistic form, and are directly translated into mathematical form The optimisation of the controllers is shown to be essential to guarantee a high quality auto-tuning process, and may be required when the conditions of utilisation of the FLC change, ie network environment or traffic composition Two optimisation approaches are presented: off-line approach, performed on a computer using the Particle Swarm optimisation; and on-line approach using the Reinforcement Learning, that can directly be implemented on the network The two optimisation approaches fully automate the FLC design process.

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A common mistake is to include in the status report everything about the project that anyone might want to know Instead of having the intended effect of informing everyone, these obese reports are overwhelming for a busy audience When developing the report content, keep it practical A department manager responsible for 250 projects made the point this way, Some of our project leaders have 10 projects If they have to spend two hours a week writing status updates, there won t be any time left for them to do work They report to supervisors overseeing up to 80 projects.

If all went well, the JSP can output some HTML text containing the new member s membership number:

The main task remaining for the user is to identify the best input indicators that are best suited to the parameter to be auto-tuned The design and optimisation of the macrodiversity FLC has been described in detail Two auto-tuning models have been presented and analysed: MD blocking based model and load based model The latter utilises downlink load of the BS and its neighbours as input indicators and has shown to be both simple and effective The two models bring about a capacity increase of around 30 % in a network in a dense urban environment This example illustrates the importance of autotuning in UMTS engineering Studies have shown that auto-tuning of macrodiversity and admission control can be carried out simultaneously using two controllers The extension to auto-tuning of several RRM functionalities needs further investigation.

to achieve a packet error rate less than 8% as required by the call for proposal (CFP) document. Figure 12.9 shows that the pulsed-OFDM system can operate at a range of 9.7 m on CM4 type channels while the range of normal OFDM is only 8.8 m. Figure 12.10 shows the results of the simulation of both the nonpulsed and

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