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Prediction theory. There has been extensive work on lattice structures for least-mean-squares (as opposed to least-squares) estimation and prediction since the late 1940s. These earlier investigations have a rich history and they relate to contributionsby Szego (1939) on orthogonal polynomials and by Levinson (1947) and Durbin (1960) on the linear prediction of stationary time series. The reflection coefficients of the resulting lattice prediction filters are usually referred to as PARCOR (i.e., as partial correlation) coefficients, since they relate to the correlation between forward and backward prediction errors - recall Probs. II.31 and II.32. It is instructive to compare the recursions of hob. 11.31 for the least-mean-squares lattice filter with the recursions of the a posteriori lattice filter of Alg. 41.1; compare also with the normalized lattice filter of Alg. 42.3. One of the earliest applications of these lattice least-mean-squares (prediction) filters was in the context of speech analysis and synthesis by Itakura and Saito (1972). State-space derivation. In Sec. 42.1 we showed that each one of the reflection coefficients, { K M ( ~ ) &(i), , &(i)}, can be interpreted as performing the projection of a normalized error vector onto another normalized error vector. In other words, each one of these coefficients can be regarded as the solution to a first-order regularized least-squares problem. In this way, by invoking the equivalence that exists between RLS and Kalman filtering (cf. Sec. 31.2), we should be able to derive lattice filters by equivalently formulating state-space estimation problems of first-order for updating each of these reflection coefficients. This is indeed the case, as was shown by Sayed and Kailath (1994b). This point of view provides another convenient way of deriving lattice filters - see Robs. X.11-X.13.

9'-0"

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Recursion for the inverse Riccati variable:

v = -;;;

Control: The Control setting defines what input is used in determining the variance applied by the jitter settings. Setting the Control to Fade allows you to specify how many steps the jitter adjustment is applied over. The other settings allow you to use the pen pressure, tilt, and stylus wheel values to control the variance that s applied. The Size Jitter, Angle Jitter, and Roundness Jitter settings all have Control settings that allow you to define how the value of each is controlled. Size Jitter: The Size Jitter settings control the variation in the brush tip s size as the stroke is being created by dragging the cursor. A Size Jitter setting of 0 percent means that the brush tip size doesn t change at all during the stroke. The higher the percentage, the more the size dynamically changes as the stroke is being made. The Minimum Diameter setting specifies the minimum size that the jitter can reduce the brush as the stroke is being applied. Using the Pen Tilt setting to control the size jitter gives you the most control. If you are using the Pen Tilt option in the Control setting, the Tilt Scale option can be used to define the amount of variance in the Size Jitter. The larger the Tilt Scale setting, the more variance you see in the size of the brush.

You use the dbConnect Manager, shown in Figure 20.4, to perform all the connectivity functions. The dbConnect Manager has its own toolbar, which becomes active when you choose a connected data source. All configured data sources are listed. FIGURE 20.4

This is a gain close to 2, and therefore we will use resislOrs that produce a 2: 1 ratio, that is. very close to 1.96. At this point, one additional consideration must be addressed. Note that the resistors at the summer input are essentially connected in parallel with the sense resistors. To ensure that all the fan current flows in the sense resistors, we select very large values for the op-amp resistors. Let us choose R, = R, = RJ = 100 k!t and then R, = 200 kl1.