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49. T. Rappaport. Wireless Communications: Principles and Practice. Prentice Hall, 1996. 50. R. Screiber. Implementation of adaptive array algorithms. IEEE Trans. Acoust., Speech, Signal Processing, pages 1038-1045, 1986. 51. B. D. VanVeen. Adaptive convergence of linearly constrained beamformers based on the sample covariance matrix. IEEE Trans. Signal Processing, 39:1470-1473,1991. 52. J. H. Winters, J. Salz, and R. D. Gitlin. The impact of antenna diversity on the capacity of wireless communication systems. IEEE Trans. Comm., 42:1740-1751,1994. 53. T. Gebauer and H. G. Gockler. Channel-individual adaptive beamforming for mobile satellite communications. IEEE Trans. Comm., 13:439-448, 1995. 54. R. G. Vaughan. On optimum combining at the mobile. IEEE Truns. Veh. Tech.: 37:181-188, 1988. 55. C. Passerini, M. Missiroli, G. Riva, and M. Frullone. Adaptive antenna arrays for reducing the delay spread in indoor radio channels. IEE Electronics Letters, 32:280-281, 1996. 56. J.Capon. High resolution frequency-wavenumber spectral analysis. Proc. Of the IEEE, 57(8):1408-1418, August 1969. 57. R.O. Schmidt. Multiple emitter location and signal parameter estimation. IEEE Trans. On Antennas and Propagation, 34(3), March 1986. 58. A. J . Barabell. Improving the resolution performance of eigenstructure-based direction finding algorithms. Proc. of the IEEE Int l. Conf. on Acoustics, Speech, and Signal Processing, pages 336-339, 1983. 59. S. V. Schell, Calabretta, W. A. Gardner, and B. G. Agee. Cyclic music algorithms for signal selective doa estimation. Proc. of the IEEE Int l. Conf. on Acoustics, Speech, and Signal Processing, pages 2278-2281, 1989. 60. G. Xu and T. Kaiath. Fast subspace decomposition. IEEE Trans. on Signal Processing, 42(3):539-550, March 1994. 61. R. S. Thoma, D. Hampicke, A. Richter, G. Sommerkorn, A. Schneider, U. Trautwein, and W. Wirnitzer. Identification of time-variant directional mobile radio channels. IEEE Transactions on Instrumentation and Measurement, 49(2):357-364, April 2000.

The electronic patient record (ePR) system is an emerging concept that offers a way to supplement or extract relevant data of the patient from small-, medium-, or largescale clinical-based hospital information system (HIS) or clinic management system (CMS). The basic concept of ePR is described in s 7 and 13. The major functions of ePR are as follows:

assess the economics of the procurement and the comparative value-for-money of specific PF tender options.

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cheek but exhorted to compete and win as well. The strain of making choices among conflicting values and loyalties besets us at every turn. Competing claims on our time and divergent demands to behave one way here and another there keep us in constant turmoil and prevent us from finding a stable anchor or from settling on a fixed course. For example, an anxious and dejected 36-year-old mother of three could not resolve the problem of whether to follow her former career as a lawyer, which she had interrupted at the time of her first child s birth, or whether to remain a housewife; when first seen, she was torn between the desire to accept a position as legal counsel for a public agency engaged in humanitarian social programs and feelings of guilt that, by so doing, she would fail to fulfill her responsibilities to her husband and children. With no system of consistent values, we drift erratically from one action to another; countervailing pressures only lead us into uncertainty, confusion, conflict, and hypocrisy.

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The results of Example 4.15 are characteristic of the behavior of the Cram&-Rao lower bound on the variance of biased estimators. First, the example shows that the Cram&-Rao lower bound matrix computed is specific of a restricted class of biased estimators of the parameters p and 6'. It only applies to estimators that have a Jacobian matrix described by (4.178). Furthermore, the example shows that the Cram&-Rao variance for such a biased estimator of the parameter a2 is (4.179) whereas that for an unbiased estimator is

5.3.2 FINE /Turbo results The FINE /Turbo CFD solver was also used to perform the calculation of the PR18 compressor. Table 5.7 shows the overall results of the different computations. The mesh size for one of the computations was comparable to the default mesh size in the pbCFD study and an additional finer mesh was also computed by NUMECA. As shown in Tables 5.7 and 5.8, the calculated efficiency varies from one to two points below the measured value. These NUMECA cases were run with only the 90 degree bend at the inlet. Table 5.7 FINE /Turbo prediction results for PR18 compressor

The precise de nition of the metabolic syndrome remains undecided. Insulin resistance, initially suggested as the central defect, is hard to measure in clinical practice and does not relate consistently to hypertension or other features of the syndrome (Saad et al., 1991; Cheal et al., 2004). Various bodies have proposed de nitions of the syndrome, including the WHO (World Health Organization, 1999), the European Group for the Study of Insulin Resistance (EGIR) (Balkau and Charles, 1999), the National Cholesterol Education Program (NCEP) (NCEP, 2001) and the International Diabetes Federation (IDF) (Alberti, Zimmet and Shaw, 2005). These de nitions are shown in Table 10.2.

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Two hierarchical re nement strategies, illustrated in Figure 11.16, are supported in the Sierra framework: (1) the classical hanging node re nement illustrated by the upper sequence of quadrilateral meshes and (2) longest edge re nement [48] illustrated by the lower sequence of triangle meshes. In both strategies, an application selects and marks elements for re nement or unre nement.

BRAND VALUES (Values communicated to target audience)

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