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Figure 14.11. A non linear optical material formed from nanopores in an InP membrane. The picture on the left is the top view, whereas that on the right shows the cross section. [Reproduced with permission from Ref. 60. Copyright 2003 Wiley-VCH.]
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It s true that the previous version of Event Viewer also had a filtering capability that managed to do a fairly reasonable job, but this new version is much simpler to use. Right-click on the Custom Views Node and select Create Custom View to open a properties sheet where you can configure (filter) your View (see Figure 13.13) based on a range of criteria including level, date and time, user, event IDs, or even keywords. Logs can sometimes be helpful in determining what failed and why. The System log, for instance, contains events that usually have an Event ID associated with them, and by searching in Help and Support, you can often find out more information about the problem and if any Windows Update has been issued for it.
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Figure 5.6.2 Schematic representation of the semi-in-lens type objective lens of the JSM-6340F, with accelerating and retarding electrodes and in-lens SED. Courtesy of Jeol Ltd. Reproduced by permission of Springer-Verlag Wien
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Pt Pd (80 20) by sputtering (Cressington 208HR). When applied in this thickness range the Pt Pd coating is invisible to the JSM-6340F and hence does not introduce any observable artefacts. This way of working is very often more ef cient than working under dynamic charge balance conditions, especially for heterogeneous surfaces where E2 changes with the position of the beam on the sample.
Figure 8.43 Structure for a silicon nitride nonvolatile MOSFET. 1(1) and 1(3) are silicon dioxide insulators, and 1(2) is composed of silicon nitride or silicon oxynitride.
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In addition, this method can also be applied to Al and Mg, although they are omitted here because they cannot be analysed with ordinary TXRF. It should be mentioned, however, that alkaline metals (Na and K) and some heavy metals (Cr, W,
Edgeworth, 49, 307 F-test for linear models, 298 F-test for variances, 297 Factor analysis, 199 Fan, R., 315, 316 Famsworth, D., 195 Feller, W., 52, 157, 307 Field, C.A., 308, 309, 311, 312, 315, 316 Filippova, A.A., 41 Finite sample minimax robustness, 259 Finite sample breakdown point, 279 Finite sample theory, 6, 249 Fisher consistency, 9, 145, 290, 300, 305 of scale estimate, 106 Fisher information, 67, 76 convexity, 78 distribution minimizing, 76, 207 equivalent expressions, 80 for multivariate location, 225 for scale, 114 minimization by variational methods, 81 minimized for &-contamination,83 Fisher information matrix, 132 Fisher, R.A., 2 Fitted value asymptotic normality, 157, 158 consistency, 155 Fourier inversion, 308 FrCchet derivative, 36-38, 40 FrCchet differentiability, 67, 300 Fraser, D.A.S., 315 Freedman, D.A., 328 Functional statistical, 9 weakly continuous, 42 Giteaux derivative, 36, 39, 113 Gale, D., 137 Generalized Linear Models, 304 Global fit minimax, 240 Gnanadesikan, R., 201, 203 Green, E., 89, 90 Gross error. 3 Gross error model, see also Contamination neighborhood Gross error model, 12 generalized, 258 Gross error sensitivity, 15, 17, 70, 72, 290
Part I
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