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could, depending on the system and the technology used, have no run-time representation at all . For example, it may contain only a set of software artifacts to be used by other business components at developntent-time . Also, Base does not correspond to a business c oncept . at least not in the usual sense of the term . In a large system, it may be useful for the whole system to have a separate base business component for each business sector (see sidebar) or even business domain within the system . For example, in an enterprise resource planning system, it may be necessary to have a base component for the financial aspects, one for the manufacturing aspects, and one for the supply chain management aspects . System business component . The business component System contains all the software artifacts of the system that must have a cross-system level of visibility in order to work properly but that do not belong to any of the other business components .

although Fireworks attempts to honor any changes you make to the hTML table in Dreamweaver, certain changes may cause Fireworks to modify your Dreamweaver table code. If, for example, you add or remove cells to or from the table in Dreamweaver and then go to edit the table in Fireworks, Fireworks displays an alert that it will replace the table in Dreamweaver. To avoid having your original table modified inappropriately by Fireworks, simply click Done right away when you get such an alert (before making any changes). Doing so keeps the original table as-is in Dreamweaver. at this point, make a backup of the entire current page; then go ahead and try out your Fireworks table edit. n

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Batch versus sequential processing. Assume the total number of data (i.e., samples) that is available for processing by a lattice filter is N 1, say { d ( j ) , u ( j ) ,0 5 j 5 N } . At each time instant i, the estimate d M ( i ) computed by the lattice implementation is an estimate of d ( i ) of order

latter produces a digital description of a physical object Performance metrics in algebraic coding theory are relatively straightforward The source data are a set of symbols The goals are for the receiver to replicate these symbols without error and without some unfriendly receiver intercepting them These goals are challenging because noise and distortions in the transmission channel corrupt the transmitted signal Algebraic coding structures are used to avoid error and interception even in the presence of noise and eavesdropping Performance metrics for algebraic coding include the rate of data transmission, the probability of error, the probability of interception, and the computational complexity of encoding and decoding These metrics may be balanced against each other, codes with high transmission rates tend to produce greater error than do codes with lower rates and more redundancy.

Performance metrics and the goals of code design are more dif cult to de ne for optical sensing The source data are a unique physical object The overall system goal is to transmit and estimate a set of symbols describing this object The sensor does not exactly replicate the object Common measures of system performance include the mean-square error between a set of object properties and the corresponding image properties Alternatively, a weighted sum of errors based on the information value of each image property or a utility measure based on the decision value of the image as a whole might be considered, or the information transfer capacity between the object state and the image might be evaluated As increasingly sophisticated computational sensor systems emerge, performance metrics have become more diverse and application-speci c.

Figure 10-4

Non e-mail folders those for tasks, calendar, journal, and contacts are handled a bit differently than mail folders. You can only create folders for some items for the default mail account, so if you are using multiple accounts, make sure that you have set the appropriate account as the default.

A chemometric imaging system might be evaluted on the basis of its chemical classi cation and concentration accuracy, a biometric system might be evaluated in terms of human identi cation metrics, or an imaging system might be evaluated according to the visual appearence of speci c types of objects For example, infrared imaging systems are evaluated according to the range at which a human observer can nd a human target in the image These complex performance-speci c measures are often dif cult to accurately model in hierachical sensor systems in which the channel coding and decoding (eg, the optoelectronic subsystem) is largely distinct from the object decoding (ie, digital signal processing) Sensor system coding generally predates algebraic coding; indeed, Golay s work on coded aperture spectroscopy [94] is a primary precursor to algebraic coding theory.

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