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y their very nature, HTML images are rectangular. Although you can make portions of a rectangular graphic transparent, giving the impression of an irregularly shaped picture, the image itself and thus its clickable region is still a rectangle. For more complex images, in which shapes overlap and you want several separate areas of a picture to be hyperlinked, not just the overall graphic, you need an image map. Dreamweaver puts its image map tools front and center so you can draw and manage your hotspots right on your graphics in the Document window. The onscreen image map tools make it much easier to manipulate your hotspots, but there s another major advantage: behaviors can easily be attached to hotspots. This chapter introduces you to Dreamweaver s hotspot tools and also covers more advanced techniques for creating server-side and rollover image maps.



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When this program is compiled, linked, and run, the output is a single number, 2,445, confirming our calculations In this example, it is clear that the selected strategy is the best, provided only one unit is held for any position However, some profit is lost due to commissions, which is inevitable It is easy to see that, if commissions are zero, then the value is Pardo s potential profit, equal to (|4273 4235| + |4235 4391| + |4391 4333|) 100 = $2,520 It is $75 greater than our real profit, the result of paying commissions of $25 for each (a total of three) round-turn trade At first glance, it looks as though commissions do not add much complication in getting the potential profit value.





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Just separate each deal in a true reversal system into independent trades, sum the price differences as absolute values, and subtract commissions from each trade However, this simplicity is an illusion The main problem is to build the potential profit strategy selecting only price differences that offset transaction costs In our example, I intentionally selected large price fluctuations while using small commissions Instead, let s say that the cost is $600 (too big for gold commissions but possible if we add slippage when gold is trading at $700/oz) and/or price fluctuations are smaller due to low volatility Then the first and the last trades must not be taken since they both would generate a loss An algorithm must filter out those transactions A maximum profit strategy for a $600 per trade cost is U(0, 1, 1, 0) The profit is equal to (4391 423.

5) 100 600 = 1,560 600 = $960 However, if the costs are equal to $400, then only the first trade must be skipped and the best strategy would be U(0, 1, 2, 0) or the same U(0, 1, 2, 1) The profit is equal to 1,560 400 + 580 400 = $1,340..

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Figure 1 1.10 Homepage of mySimon.com (Reproduced with permission of mySimon)

Thus, the j t h component of the GFFT of v is obtained by evaluating v ( x ) at x = a'. Let us also define a polynomial based on V = (Vo, V1, . . . , Vn-1) by

that are higher by 550 K between 30 and 50 km depth (Henstock & Levander, 2000). Beneath the Transverse Ranges, a velocity model (Fig. 8.13), constructed using active-source seismic data, reveals the presence of an 8-km-thick crustal root centered beneath the surface trace of the San Andreas Fault (Godfrey et al., 2002). The presence of this crustal root indicates that the transpression associated with the Big Bend in the San Andreas Fault (Section 8.2) affects the entire crust. The data also show an offset Moho. Estimates of the magnitude of the offset are variable, mostly because they depend on the speci c velocities used. Published estimates show the Moho to be at least one and possibly several kilometers deeper on the northern side of the fault. A similar, small Moho disruption also occurs beneath the Eastern California Shear Zone (Zhu, 2000) (Fig. 8.1). These offsets suggest that a narrow zone of brittle and ductile deformation surrounding the southern segment of the San Andreas Fault also extends vertically through the entire crust. In addition to an offset Moho, the velocity model shown in Fig. 8.13 indicates that relatively slow seismic velocities (6.3 km s 1), which are consistent with weak, quartz-rich lithologies, characterize the middle and

1.57 _ a) X =14.7075-1.2042Z1-0.46288Z2;

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