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The first step toward developing efficient block adaptive filters is to explain how a long FIR filter, such as the one shown in Fig. 27.1 and independent of the adaptive context, can be implemented in an equivalent form that operates on blocks of data rather than on one sample at a time. We find it convenient to motivate the block implementations by working with the a-transform notation, while traditional derivations of block adaptive filters tend to be carried out in the time-domain. The z-domain arguments allows one to exploit the block structure to great extent and to motivate families of block adaptive filters that use other orthogonal transformations - see, e.g., Apps. 1O.D and 10.E of Sayed (2003). The z-domain arguments also bring forth connections between block adaptive filters and another class of filters known as subband adaptive filters (see Sec. 28.2). Consider a long impulse response sequence g and its transfer function G(z), as in (27.2). We refer to G ( z )as thefullband filter. Let also { Y ( z ) U ( z ) }denote the z-transforms of , its causal input and output sequences { y ( i ) ,u(i)},

In a landmark paper, Willard [22] presented a uniform leader election protocol for the conditions of Scenario 2 terminating in log log u + O(1) expected time slots Willard s protocol involves two stages: the first stage, using binary search, guesses in log log u time slots a number i (0 i log u), satisfying 2i n < 2i+1 Once this approximation for n is available, the second stage elects a leader in O(1) expected time slots using the protocol of [12] Thus, the protocol elects a leader in log log u + O(1) expected time slots Willard \citeWIL86 went on to improve this protocol to run under the conditions of Scenario 3 in log log n + o(log log n) expected time slots.

The first stage of the improved protocol uses the technique presented in Bentley and Yao [4], which finds an integer i satisfying 2i n < 2i+1, bypassing the need for a known upper bound u on n More recently, Nakano and Olariu with probability exceeding 1 1/f, in log log n + o(log log n) + O(log f) time slots Our uniform leader election features the same performance as the nonuniform leader election protocol of [15] even though all the stations transmit with the same probability in each time slot In this chapter, we survey known leader election protocols See Table 101 for the characteristics of these protocols..

Working with symbols Creating loops Generating random numbers Creating functions Creating modular ActionScript project: Navigating to a random frame

At the domain layer, numerous patterns apply, depending on the context and problem being solved. In this section, we will evaluate the requirements of TheServerSide and choose implementations depending on factors affecting concurrency, portability, maintainability, the use of tools, and the need to generate primary keys. Concurrency. Among the use cases outlined in Figure 6.1, the EditMessage use case opens the potential for corruption of the underlying database unless special precautions are taken. Figure 6.1 shows that only site administrators can edit a message. But what happens when two different administrators attempt to edit the same message As explained in the Version Number pattern ( 3), there is a potential for the two administrators to overwrite each other s changes. The solution is to use the Version Number pattern, which would cause us to add optimistic concurrency checks to the domain objects that have use cases that could potentially result in database corruption.

Each transaction runs at the site where it has entered the system, moves to the other site, and then runs there. Therefore, T1 runs in NY rst and then in LA, while T2 runs in LA rst and then in NY. If these two transactions start at almost the same time, we would have the following two local schedules:

Based on the interview questions above or on the dot voting techniques described later in this chapter in the section Selecting a Metric, you should be able to identify the areas on your map most critically in need of measurement. A step-by-step method useful for identifying measures and metrics involves 1. Identifying the critical areas on the map. ( 10 describes dot voting as one method of identifying critical areas.) 2. Brainstorm the measures you need in order to control each critical area. Measures can be lead (drivers of the process) or lag (success measures). 3. Research metrics for each measure. If the process you are measuring is common in your industry, first try using an industry-standard measure. 4. Reduce the possible metrics to a short list by using either a dot voting technique or a weighted table. The following sections of this chapter describe these steps in more detail.

To scale an object with the Transform panel: