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lens with a maximum aperture of f/1.4. This is one of three lenses that are f/1.4, the fastest that Sony makes. The 35mm has the equivalent focal length of 52.5mm when the lens crop factor is taken into account. This makes it the standard in normal lenses. Combined with the Super SteadyShot technology, this lens can be used in very low light.

TABLE 18-2

This product is similar to that in the Ozanam problem. The sum of the three terms is fixed, at 2AQ. The product will be a maxImum when AP = 2AQ - 2AP, or AP = fAQ.

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The uplink s inner-loop power control adjusts the mobile s transmit powerin order to maintain the received uplink SIRat the given SIR target, namely at SIRtaTget. base stations The that are communicating with the mobile generate Transit Power Control (TPC) commands and transmit them, once per slot, to the mobile. The mobile then derives from the TPC commands of the various base stations, a single TPC command, TPC-cmd, for each slot, combining multiple received TPC commands if necessary. In [360] two algorithms were defined forthe processing of TPC commands and hence deriving TPC-cmd. for Algorithm I : [360] When not in soft-handover,i.e. when the mobile communicates with a single base station, only one TPC command will be received in each slot. Hence, for each slot, if the TPC command is equal to 0 ( S I R > SIRtaTget) TPC-cmd = -1, otherwise, if the TPC then command is 1 ( S I R < SIRtaTget) TPC-cmd = 1, which implies powering down or then up, respectively. When in soft handover, multiple TPC commandsare received in each slot from the different base stations in the active base station set. If all of the base station s TPC commands are identical, then they are combined to form a single TPC command, namely TPC-cmd. However, if the TPC commands of the different base stations differ, then a soft decision Wi is generated for each of the TPC commands, TPCi, where i = 1 , 2 , . . . ,N , and N is the number of TPC commands. These N soft decisions are then used to form a combined TPC command TPC-cmd according to:

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quite well understood and the physical constants governing the interaction and the extent of radiation absorption can be derived accurately from measurements obtained in well-de ned conditions It is thus, in principle, possible to correct for deviations of linearity between measured intensities and elemental concentrations, especially if the measurement conditions are simpli ed as much as possible by using a high intensity monochromatic primary excitation and employing an energy dispersive detector for the measurements Powerful methodologies are now available for modeling by ab initio Monte Carlo (MC) simulation both the beam optics and the beam sample interaction within the sample and the detector (Vincze et al, 1999a; Vincze et al, 1999b; Vincze et al, 1999c) (see also Subchapter 61).

n (primary electrons / X-ray photon)

The combined use of nonlinear least squares deconvolution of X-ray spectra and the methodology for modeling the beam optics and beam sample interaction allow the optimum use of all spectral information, including the use of Rayleigh and multiple Compton scattering A number of background effects that were never fully evaluated in XRF appear in the SR spectra, the most important being electron Bremsstrahlung in sample and detector Electron Bremsstrahlung is able to generate uorescence radiation from low Z elements and, hence, appears to be a quite important factor in quantitative analysis that was never systematically taken into account for quantitative analysis It appeared, for instance, in the simulation of experimental data that ca 50 % of the uorescence radiation of bulk Si in a 250 m thick Si wafer irradiated with 27 keV results indirectly from the 25.

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16 keV photoelectrons generated by the beam in the sample, rather than directly through impinging X-rays (Vincze et al, unpublished results) Also, speci c spectral artefacts complicate the spectra, eg resonant Raman scattering which creates peak-like structures in the spectra at excitation energies near the absorption edge, thus complicating spectral deconvolution (Gel mukhanov and Agren, 1999) A systematic comparison of experimentally obtained spectral data with modeling results based on MC simulations can pinpoint speci c artefacts.

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