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Redundancy used to carry much more of a stigma than it does in the modern workplace. In an age of cost-cutting and downsizing programmes, redundancy is now merely a reality of working life. If you can, explain that your redundancy was a cost-saving decision taken by the management rather than a personal decision because they didn t like you! Actually, about 30 of us were chosen for redundancy purely because we were the most junior staff. When the business started to do badly, they made a decision to reduce the size of our department by 15 per cent. Unfortunately, I was one of that percentage. Talking about how your role was made redundant by a restructuring in the team is an alternative response to this question. You may naturally feel hurt to have been made redundant. But avoid taking your feelings out on your former managers by talking disparagingly of them, as doing so may backfire and reflect badly on you.

The IM3s that are close to the desired signals tend to receive the same phase shift as the fundamental because their frequencies are close. Consider this scenario, which is tabulated in Table 4.1. Suppose that, at some point, two fundamental signals have phases 1 and 2 and a third-order product is created with timevarying phase of 2 1 (t) 2 (t), implying a frequency of 2 1 2 (close to the signal at 1 ) plus a phase 2 1 2 . In traveling to another module, these signals

It is simpler to measure the S parameters and obtain the T parameters from them. For example, T22 for module 1 is T22 = vi1 vi2 .

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Resource ownership is thread-relative by default on the Symbian platform. If a le is opened by the main thread it is not possible for a different thread in the process to use it without the handle being explicitly shared through a call to RSessionBase::Share() (and some Symbian platform servers do not support session sharing at all). Because of this restriction, it may be very dif cult to use multiple threads as a viable method for event-driven multitasking. Active objects run in the same thread, which means that memory and resources may be shared more readily.

All of the requirements and their priorities Use cases related to all requirements All features related to the requirements Test cases for each requirement Results of the test cases run

Now if n is large, it is reasonable to suppose that n 1 X @u .x ; y/ 1 @u i .xi ; y/ j j k k jD1 @y @y

Gain (dB)

11.3 GENERAL RECOMMENDATIONS FOR IMPROVING THE NUMERIC ACCURACY OF ANALYSIS The recommendations above are a guide to reproducing research, but what can researchers do to ensure that their results are accurate before publication For commonly used statistical analysis, choosing a statistically accurate software package and applying common sense in interpreting the results and standard diagnostics is probably enough. In other research, especially that involving complex maximum likelihood estimation or simulation, it may be necessary to repeat certain portions with different techniques and to track down all the implications of particular methods. Here we provide recommendations to improve numerical accuracy. We start with the steps that we believe to be most generally applicable, and nish with steps geared toward numerically intensive models: 1. Trust, but verify. Most packages correctly solve routine statistical analyses, such as univariate statistics, linear regression, and many simple nonlinear regression models. Choose software that is regularly updated, uses well-documented algorithms, and buttresses itself with standard tests of accuracy and reliability. A number of packages now publish the results of numerical benchmarks in their documentation, and benchmarks of accuracy are becoming a more regular part of statistical software reviews. Be wary of software packages that are not tested regularly for accuracy or that do not perform well. 2. Check for substantive and statistical plausibility. Examine results to ensure that they make sense and t these data. Check for evidence of misspeci cation, outliers, or other signs of measurement or coding errors. These precautions have

7.2.4 Applying Test-Driven Development for Algorithmically Complex and Critical Code Units In addition to the test- rst approach at the service and module levels, algorithmically complex code and critical code unitsh should be implemented using TDD. Algorithmically complex code may involve a unique computationally intensive solution or a large data processing problem. A critical code unit is referred to as the unit responsible for an application s critical state, for which failure may have disastrous consequences, such as a malfunction of a medical device or an intrusive access to high-security data. Both algorithmically complex code and critical code units require special attention in development.

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