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Within each ber or waveguide there is a long tapered section, then a uniform section of length Z where they are fused together (for the FBT) or lithographed to be narrower and closer together (for the PLC device), followed by a positive taper back to the original cross sections The tapers are very gradual (adiabatic), so that a negligible fraction of the energy incident from either input port is re ected back that is, the device has a very large return loss As we mentioned in Section 31, and as the diagram indicates, narrowing the core destroys the con nement of the radiation.

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Both employees and the self-employed need to carefully consider insurance needs. You need life, health, homeowner s or renters, disability, and possibly an umbrella liability policy. If you have your own business, explore whether you need business insurance such as errors and omissions coverage, which kicks in if a client charges you with inadequate attention to the professional

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Table 7.1 TFRC Example transmission format and resource combinations (TFRCs). Modulation E ective code rate (ECR) 0.14 0.27 0.39 0.52 0.64 0.77 0.32 0.38 0.45 0.54 0.65 0.77 Instantaneous data rate with 1 HS-PDSCH code [kbps] 68.5 128.5 188.5 248.5 308.0 368.5 302.5 362.0 433.0 518.0 619.5 741.5

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Genetic algorithms [53,54] are derivative-free stochastic optimization methods based loosely on the concepts of natural selection and evolutionary processes [55]. (See Appendix C for basic principles and operations of GAs.) Their popularity can be attributed to their freedom from dependence on functional derivatives and to their incorporation of the following characteristics: GAs are parallel-search procedures that can be implemented on parallel processing machines for a massive increase in their operational ef ciency. GAs are applicable to both continuous and discrete (combinatorial) optimization problems. GAs are stochastic and less likely to get trapped in local minima, which are inevitably present in any practical optimization application. GAs offer exibility that facilitates both structure and parameter identi cation in complex models such as neural networks and fuzzy inference systems. Dubitzky and Azuaje [50] developed an evolutionary computing approach for learning of global and local feature weights using the CBR system architecture depicted in Figure 3.3. The retrieval of cases is based on partitioning the cases into subsets. The part of the diagram above the dashed line in Figures 3.3a and b demonstrates how supervised and unsupervised learning interacts with the system components. Typical supervised learning methods include kd-trees, shared-feature and discrimination networks, decision trees, and arti cial neural networks, and unsupervised methods include techniques such as automatic cluster detection.

GSM 31/88 rev. 1 shows the result of the rst round of approvals in GSM#17 in Florence in February 1988

9 Other Electrochemical Techniques in Non-Aqueous Solutions Fig. 9.11 Idealized diagram of the trapping of molecule A that is oxidized at the tip and rereduced at the conductive substrate in the system for single molecule electrochemistry [31]. See the size of the tip electrode.

where Wc and Wf are respectively the bandwidths of the C=A-code (2.046 MHz) and the signal recovery lter. If Wf 2000 Hz, about 30 dB of suppression can be obtained for narrow-band interferors. When the signal recovery lter has a bandwidth smaller than 1000 Hz, the situation is more complicated, since the despread interfering sinusoid will have discrete spectral components with a 1000Hz spacing. As the bandwidth of the interfering signal increases, the C=A-code despreading process provides a decreasing amount of interference suppression. For interferors having a bandwidth greater than that of the signal recovery lter, the amount of suppression in decibels provided by the C=A-code is approximately WI Wc Z 10 log WI dB; 3:9

1 n 1 kn lnf xi xj 2 2 n 1 T 2 g 1 n 1 kn ln xi xj 2 2nT 2 1 n kn 1 ln xi xj 2 2nT 2 or G xi ; T; xj ; T

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