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If you notice that any of the yellow panels on the balloon are being unintentionally affected, use the eyedropper with the minus sign ( ) beside it, the Subtract From Sample Eyedropper tool, to click on the colors you don t want to affect and remove them from the selected colors. Keep working with the Add To and Subtract From tools until only the two rows of green panels are being affected by the Hue adjustment. Drag the Lightness slider all the way to the left to 100. This adds black to the selected color range, changing the panels to gray and black. Figure 7.19 shows a detail of the before and after colors on the balloon. Positive values with the Lightness slider add white to the color, which lightens it.

Defining the stretch frame for a stretch action is similar to specifying a crossing selection window in the STRETCH command, except that you can create the stretch frame from left to right. For example, objects within the frame are moved, and objects that cross the frame are stretched. However, you also select objects for a stretch action. If objects are in the stretch frame but not in the selection set, they are not stretched or moved. If objects are outside the frame, but are in the selection set, they are moved. After you select objects, you can select or deselect individual objects to add them to, or subtract them from, the selection set.

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The Hammerstein and Wiener systems consist of linear dynamical systems in cascade (series) with static (memoryless) nonlinearities (see Fig. 12.1). In the Wiener system, linear lters precede memoryless nonlinearities, while in the Hammerstein system a nonlinear memoryless system precedes linear (dynamical) lters. Both systems are special cases of an important class of block oriented sandwich systems with static nonlinearities sandwiched between two linear dynamical subsystems [1128]. Sandwich systems constitute a relatively simple but important class of nonlinear systems since linear combination of such systems can approximate a wide class of nonlinear dynamical systems [1125, 1127, 1128]. Such models arise in practice whenever measurement (sensor) devices have nonlinear characteristics (see Fig. 12.2). It should be noted that, in the special case when the nonlinear function is known or can be estimated and the inverse function exists, the Wiener model can be simpli ed to linear problems described in last three chapters, as illustrated on Fig. 12.2. However in such models, usually some typical nonlinearities such as hard limiters, dead-zone limiters, quantizers and hysteresis are excluded and for such nonlinearities it is impossible to estimate exact inverse systems. The problems illustrated in Fig. 12.1 (a) and (c) are di cult, because of the permutation and scaling ambiguities even if the nonlinearities are known and invertible. However, in the special case when the set of all nonlinear functions { } are identical and are known, we can easily convert the problem to linear multichannel blind deconvolution which was discussed in the previous chapters. It should be noted, that all these speci c nonlinear dynamical models can be described by a general and exible nonlinear state-space model or NARMA model discussed below.

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One (1) quart is .25, 25 percent, or 1/4 of a gallon. Anytime you are converting from a gallon to a quart, the multiplier is .25, 25 percent, or 1/4. If a recipe yields a gallon of stock and you need one quart, you can multiply all of the ingredients by .25, 25 percent, or 1/4.

n Second, you can press Shift during a drag to constrain a move to a 45-degree direction that is, horizontally, vertically, or diagonally. n And third, you can use the Info palette to track your movements and to help locate a precise position in the image. To display the Info palette, shown in Figure 8.17, choose Window Info or press F8. The first section of the Info palette displays the color values of the image area beneath your cursor. When you move a selection, the other eight items in the palette monitor movement as follows: n X, Y: These values show the coordinate position of your cursor. The distance is measured from the upper-left corner of the image in the current unit of measure. The unit of measure in Figure 8.16 is pixels. n X, Y: These values indicate the distance of your move as measured horizontally and vertically. n A, D: The A and D values reflect the angle and direct distance of your drag. n W, H: These values reflect the width and height of your selection.

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