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Table 8.3 Average errors for all trips Trip no 3.4 4 6 9.2 GPS only 27.9 m 53.7 m >100 m 40.7 m OMMGPS 11.3 m 8.8 m 28.3 m 22.8 m 95% cut off OMMGPS 6.6 m 5.2 m 14.1 m 14.4 m

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Table 4.4 Characteristic infrared bands of amines Wavenumber (cm 1 ) 3335 2780 1615 1360 1250 1220 1020 850 750 715 Assignment N H stretching (doublet for primary amines; singlet for secondary amines) N CH2 stretching NH2 scissoring, N H bending Aromatic C N stretching Aliphatic C N stretching NH2 wagging and twisting N H wagging

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5:2-13

Wavenumber (cm 1)

13

11

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results are for binary amplitude holograms. Values of c 1=2 and M 1 in the Lohmann method were used throughout. For comparison purposes, the MSE occurring in the Lohmann method was normalized to one, so all other MSE values are relative to the Lohmann method. Table 16.3 shows the results of the computer experiments in which the amplitude and phase of each cell took on quantized values, and the ODIFIIT was applied. Each Lohmann cell is divided into N N smaller squares in N-level quantization. Quantization of amplitude and phase is done as discussed in Section 16.6. MSE is still relative to the MSE from the Lohmann method alone. The experimental results indicate that the ODIFIIT is better than the Lohmann method in terms of both MSE and ef ciency. In the constant amplitue Lohmann method, the error in the image region increased, but ef ciency was signi cantly improved. Basically, this means reconstruction of a brighter image with less de nition. This result seems reasonable for the following reasons: less information is used in LMCA, which increases the error, but the amplitude values throughout the hologram are greater due to aperture size always being as large as possible, which increases the brightness. The output displays a reconstruction of the desired image which is brighter, but has fuzzy edges. Less reconstruction occurs outside the desired image region further con rming the ef ciency increase. In fact, the ef ciency of LMCA surpassed the ef ciency of ODIFIIT, but the signi cant increase in MSE is a disadvantage. The results indicate that LM-ODIFIIT produces an output most resembling the desired image (lowest MSE), while its ef ciency is the same as the original Lohmann s method (LM). Simulations show qualitatively, that LM-ODIFIIT

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investors join the bandwagon, the discarding of a crash as a viable event becomes more widespread and, in effect, conventional wisdom. The more investors join a booming market, the more normal the investment looks to others. The Normality assumption becomes not just the rationale for entering into the market, but an end in itself. The market reaches a point where people are not buying any speci c asset. They are buying Normality (i.e., the complete absence of nasty surprises). If they expected something else, they wouldn t have joined the party. But with every new in ow of cash into the market, the chances of a rare event go up. The more participants in the market, the more chances that someone, somewhere would react negatively to a new development (such as corporate losses, an accounting scandal, or disappointing economic gures), would panic, and would liquidate as a result, prompting other investors to panic, liquidate, and so on all the way to a crash. In essence, when faced with the unexpected presence of the unexpected, Normality-believers will tremble and exacerbate the downfall. They never believed in outliers until they experienced one, and their reaction gives strength to the outlier, making it stronger, fattening the tails. The non-Normal distribution is thus unavoidably born, a testament to people s wildly changing trading habits. As long as people continue to not expect rare events to occur, rare events will inevitably take place.

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