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which enable the system developer to take a precondition and postcondition specification, and from it systematically derive an implementation through the use of proof rules (Morgan, 1994). Part of the reason for t h s comparative simplicity is that there is often an easily understandable relationship between the preconditions and postconditions that characterize an operation and the program structures required to implement it. For agent systems, which fall into the category of Pnuelian reactive systems (see the discussion in 2 ) , refinement is not so straightforward. This is because such systems must be specified in terms of their ongoing behaviour - they cannot be specified simply in terms of preconditions and postconditions. In contrast to precondition and postcondition formalisms, it is not so easy to determine what program structures are required to realize such specifications. As a consequence, researchers have only just begun to investigate refinement and design technique for agent-based systems.

Making Over Backlit Images (continued)

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It is instructive to examine the use of Thevenin 's and Norton's theorems in the solution of the network in Fig. 5.4a, which is redrawn in Fig. 5.IOa.

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replaces them with a different solid color for each <div> tag. The resulting patchwork shows at a glance how the page is structured (see Figure 10-6). This option is also useful when debugging layouts because it clearly shows which <div> tags if any overlap.

With this definition, the partially-averaged system (24.30) becomes (24.31) We are now in a position to study the steady-state and transient behavior of such systems. Steady-state analysis. Assume the data { d ( i ) , satisfy model (22.1) and let e ( i ) = d ( i ) ui} uizo,--l denote the output error at time i. In steady-state analysis, we are interested in evaluating 'For a discussion on a larger class of adaptive filters,including LMF, LMMN, and sign-error LMS, see Shin, Sayed, and Song (2004b).

where O is any (I @ S)-unitary matrix that produces the block zero entry in the post-array. In other i words, this array form propagates the low-rank factor {Lili-l} as well as the gain matrix { K p , i } . The algorithm does not involve computation or propagation of the Riccati variable Pili-1, which therefore results in an order of magnitude improvement in complexity. While the covariance form of the Kalman filter requires O(n3) operations per iteration, the above array form requires O(cyn2) operations per iteration and cy << n usually. Theinitialconditionsare R,,o = R+Hl-IoH* and K,,oR:/: = F~-IoH*R,:'~with obtained from the factorization

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To open the Layer Properties dialog box, select a layer in the Layers palette, open a context menu, and select Properties from the menu options.

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