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Progri IF, Ortiz W, Michalson WR, Wang J. The performance and simulation of an OFDMA pseudolite indoor geolocation system. In: ION GNSS 2006: Proceedings; September 2006. Forth Worth, (TX). Rizos C, Barnes J, Wang J, Small D, Voigt G, Gambale N. LocataNet: intelligent timesynchronised pseudolite transceivers for cm-level stand-alone positioning. In: 11th IAIN World Congress: Proceedings; October 2003. Berlin, Germany. Samama N, Vervisch-Picois A. Current status of GNSS indoor positioning using GNSS repeaters. In: ENC GNSS 2005: Proceedings; July 2005. Munich, Germany. Suh Y-C, Konish Y, Shibasaki R. Assessing the improvement of positioning accuracy using a GPS and pseudolites signal in urban area. Available at: www.chikatsu-lab.g.dendai.ac.jp/ s_forum/pdf/2002/10_suh.pdf. Sun G, Chen J, Guo W, Ray Liu KJ. Signal processing techniques in network-aided positioning: a survey of state-of-the-art positioning designs. IEEE Signal Process Magazine, July 2005; Volume 22, Issue 4, pp 12 23. Syrjarinne J, Wirola L. Setting a new standard assisted GNSS receivers that use wireless networks. InsideGNSS 2006;1(7):26 31. Van Diggelen F, Abraham C. Indoor GPS technology. Global Locate Inc. Available at: http:// www.globallocate.com/SEMICONDUCTORS/Semi_Libr_Piece/IndoorGPSTechnology.pdf.



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Sensor networks most often have a much more complicated topology than the simple examples shown in Figures 7.4 and 7.5, and not all sensor nodes can communicate with each other directly. Thus, multihop synchronization is required, which adds an additional layer of complexity. Clearly, this could be avoided by using an overlay network that provides virtual, single-hop communication from every sensor node to a single master node. But as we saw in Section 7.4.1, the synchronization error directly depends on the message delay, which is very dif cult to control on a logical link that is composed of many physical hops. Therefore, performant synchronization schemes have to deal with the multihop problem explicitly. Figure 7.7 illustrates various approaches to multihop synchronization. We now describe these four schemes and use them as examples to discuss the main problems of multihop synchronization.





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rival two-factor schemes: one based on Pleasantness and Engagement (to use the terminology shown in Figure 14.1), and the other de ned by Negative Affect and Positive Affect. We shall review measures of all four constructs later. A Hierarchical Synthesis Watson and Tellegen (1985) further clari ed the underlying structure of the domain by pointing out that these two basic approaches that is, general dimensions and discrete affects are not incompatible or mutually exclusive; rather, they re ect different levels of a single, integrated hierarchical scheme (see also Berenbaum et al., 1995; Diener et al., 1995; Watson & Clark, 1992, 1994, 1997). Speci cally, they proposed that the higher order dimensions of Negative Affect and Positive Affect each can be decomposed into several correlated yet ultimately distinct affective states; the general dimension of Negative Affect, for instance, can be subdivided into speci c negative affects such as fear, hostility, and sadness. In this hierarchical scheme, the lower level re ects the speci c content of the mood descriptors (i.e., the distinctive qualities of the individual discrete affects), whereas the higher order level re ects their valence (i.e., whether they re ect negative or positive states). Subsequent studies have reported evidence that strongly supports this hierarchical scheme. Watson and Clark (1992), for example, reported four studies demonstrating the hierarchical arrangement of the negative affects. In their Study 1,

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Not all systems support sound. To determine whether a system supports sound, use the CanPlaySounds method. Here s an example:

Limitations of forecasting techniques, such as those illustrated in this section, include: Their use provides precise mathematical results that are only as good as the data used. For example, if the guest night forecasts are not very good, then the forecast for restaurant meals served will not be very good. The mathematical approaches used in forecasting do not consider variables that can be controlled by management. For example, a forecast of restaurant volume based on historic sales would need to be adjusted for an anticipated increase in demand resulting from an increased advertising campaign that the restaurant manager is planning to implement. No mathematical forecasting technique can substitute for experience and individual judgment. Indeed, in some cases (such as opening a new property or expanding an existing one) there may be only limited data available on which to base mathematical forecasting techniques, such as moving averages or regression analysis. In such cases, judgment and other qualitative considerations have to play a greater role.

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