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money, goods, services, or anything of value which within any one-year period has a value aggregating $5,000 or more. 14 In 1974, the amount was lowered to $1,000 as many of the credit cards frauds were less than $5,000. By 1974, credit card fraud losses began to climb significantly; losses were estimated to be at $460 million for the year.15 The criminals who had been so active stealing credit cards from the mail back in 1966 and 1967 when millions of unsolicited cards were sent out were again active. Plus, there were new mail thieves added to the mix. Mail theft of credit cards was becoming more and more of a problem with the growing consumer base of credit card users. People now had multiple credit cards, and new customers were being signed up at a record rate. Credit card applications were readily available, and everyone wanted credit cards. The commentary from 1974 that follows is illustrative of how the credit card fraud application process by fraudsters has changed over the years but also remained the same:

To select objects in an irregularly shaped area, use Window Polygon or Crossing Polygon. The Wpolygon (Figure 3.3a) and Cpolygon (Figure 3.3b), work like Window and Crossing windows. Type in WP or CP after the Select objects: prompt.

A positive Amount value in the Pinch dialog box produces an effect similar to a negative value in the Spherize dialog box. There is a slight difference between the spatial curvature of the 3-D calculations: Pinch pokes the image inward or outward using a rounded cone we re talking bell-shaped, much like a Gaussian model. Spherize wraps the image on the outside or inside of a true sphere. As a result, the two filters yield subtly different results. Pinch produces a soft transition around the perimeter of a selection; Spherize produces an abrupt transition. If this doesn t quite make sense to you, just play with one, try out the same effect with the other, and see which you like better.

It should be noted that the algorithm looks similar, but in fact it is not identical to, algorithms presented in the previous sections. The essential di erence is that the update rule for Wp in this section depends only on Wq , in the range q = 0, 1, . . . , p, while in other algorithms it depends on all parameters Wq , in the range q = 0, 1, . . . , L. The algorithm (9.126) has two important properties, the uniform performance (the equivariant property [155]) and the nonsingularity of W0 . In the multichannel deconvolution problem, an algorithm is equivariant if its dynamical behavior depends on the global transfer function G(z) but not on the speci c mixing lter H(z). In fact, the learning algorithm (9.126) satis es the equivariant property in the Lie group sense (see Appendix A). It should be noted that the learning algorithm (9.132) keeps the lter W(z) on the manifold M(L) if the initial lter is on the manifold. The equilibrium points of the learning algorithm satisfy the following equations E{f (y(k)) yT (k p)} = 0, E{f (y(k)) yT (k)} = , (p = 1, 2, . . . , L), (9.135) (9.136)

leading to :

q Select the Eraser tool and, using the tool s Options bar, set the Mode to a soft-edged brush and the Opacity to 50%. w Using your newly configured Eraser tool, fine-tune the edges of your image vignettes.

In order to simplify this equation, we resort to the same separation principle (16.7) that we used in the LMS case, namely, that at steady-state, ~ ~is independent of iea(i)i2.Under u ~ ~ ~ 2 this condition, equality (17.4) becomes

(II).

1. Create the spot channel as described earlier in this chapter. 2. Select a spot channel in the Channels panel. 3. Click the Load Channel as Selection button to get the selection of the channel. 4. Click the composite channel for the image in the Channels panel.

In order to relate P, to P we invoke the easily verifiable matrix identity:

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