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averaging shows that all the coherent oscillations have been smoothed out in the average. However, in order to produce a significant amount of decrease in brightness temperature due to reflection, a small correlation length of 2 mm is required as shown in Fig. 1.1.2. The reasons are that the permittivity in a Gaussian random process is a continuous function of z. As we see in some of the examples in 5 of Volume I, a continuous profile usually does not produce much reflection. In order to produce reflection, the continuous profile must have a substantial change of permittivity over a short distance, say 2 mm. Over a long distance of 2.5 m, this means more than 1200 reflections. Thus to match brightness temperature experimental data, a small standard deviation of 0.0156 g/cm3 is used for the density fluctuations. This will reduce the amount of power reflections for each reflection. Hence, the 3 choice of parameters of lz = 2 mm and pf 2) = 0.0156 g/cm is dictated by the underlying assumption of Gaussian random process. By choosing the parameters of the Gaussian random process, we can get reasonable brightness temperatures. However, the question is whether these physical parameters are reasonable. In the next section we discuss the discrete layering model.

If the event handler needs to perform differently based on the control that generated the event, the event handler can reference the control s Name property For example:

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The ground truth measurements of snow density profile have been taken by Rott et al. [1993]. Figure 1.2.1 illustrates the results for the station at Vestestraumen. The profiles indicate discrete layering with layer thickness of the order of 5 cm. Abrupt changes of densities are associated with discrete layering. The ground truth layering geometry does not correspond to the continuous Gaussian random process that is discussed in Section 1. Thus a satisfactory theory needs to (1) give an accurate and correct solution of Maxwell's equations; (2) match experimental measurements, and (3) use a correct medium characterization with parameters that are verified by ground truth measurements. To incorporate ground truth data on the density profile, we apply a centimeter discrete layering model instead of a millimeter correlation length continuous random medium model. The measured profile in Fig. 1.2.1 shows a background trend of the form 1 - exp(oz) (z < 0) which saturates below about 1 m of depth. A theoretical profile similar to the measured profile can be generated using two random variables and a background exponential trend. The two random variables are the layer thickness and the density. The layer thickness is assumed to be exponentially distributed. The layer-to-

The fast multipole method (FMM) was invented by Rohklin [Rohklin, 1990; Coifman et al. 1993]. Michielssen and Chew [1996] used the steepest descent method to express the product of impedance matrix and column vector. Jandhyala et al. [1998a,b] applied the steepest decent fast multipole method (SDFMM) to 3-D rough surface scattering problem. Recently, FMM has been applied extensively to large scale electromagnetic boundary value problems. SDFMM has also been combined with the PBTG method [Li, 2000]. In the following, we apply the method for one-dimensional rough surface for the Dirichlet problem. In Sections 3.1-3.4, the theoretical analysis is presented. In Section 3.5, the computational algorithm and the computational complexity of the approach are given.

(r, r')

~H61) (k [r - r'[)

(5.3.1)

Private Sub ProcessEnter(ByVal sender As Object, _ ByVal e As SystemEventArgs) _ Handles NameTextBoxEnter, DescriptionTextBoxEnter If DirectCast(sender, Control)Name = "NameTextBox" Then ' Use one color for name DirectCast(sender, Control)BackColor = ColorLightYellow Else ' Use a different color for other fields

9 (x, z) =

Like much of Asia, Australia is playing catch-up with the United States, Europe, and Japan in becoming a serious competitor in the biotech sector The major advantage of Australia, which is host to about 200 small and medium-sized biotech companies as of 2003, is that it has a tradition of academic excellence in the life sciences When it entered the commercial biotech sector in the late 1990s, it did so with several decades of worldclass academic research in the life science behind it As described here, the Australian biotech industry can become a signi cant player in the interna tional market if it can successfully leverage the country s academic research expertise in creating viable commercial enterprises.

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(5.3.2)

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