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location. In the pro ling step, at each interest location, multiple samples from each AP are collected and the marginal conditional probability is calculated that value ei [ E as is observed from an AP given current object location is qj [ Q. After all locations are sampled, the marginal conditional probability p(EjQ) of each AP is stored in a separate leaf node of the Bayesian network. Then in the matching step, to estimate a location, a mobile object at rst samples and quantizes signals from each AP. The result from each AP forms a vector R fr1, r2, . . . , rjjrj [ Eg, which is used by the Bayesian network to calculate p(QjR), the a posteriori probability distribution over location set Q. And the conditionally probability distribution of each location is used to de ne closeness. Another issue of the matching problem is in the domain of computation geometry. Because the location database can be quite large for wide area coverage, the ef cient organization of the data and corresponding search algorithm are important for real-time performance. There is a fair amount of literature dealing with these issues, and they are not repeated here because they are considered beyond the scope of this chapter.

Close inspection of the Standage, Treasure, et al (2003) data supports this However, the data also reveal that overall the factorial structure of the 16-item scale is still appropriate Because the use of the scale in sport has just begun and support for the full-scale version has been obtained in several studies, it is recommended that researchers use the full-scale and not the 14-item version at this point Scales assessing motivation at the contextual level have also been developed Because we were mainly interested in college students and because research revealed that college students rated education, leisure, and interpersonal relationships as their three main life contexts (Blais, Vallerand, Gagnon, Bri re, & Pelletier, 1990), scales were developed.

Figure 2.13 Release 6 study item, enhanced uplink DCH, with proposed node B based operation for HARQ in the uplink.

including frequency hopping (FH), direct-sequence spread spectrum (DSSS) and its high rate (HR) counterpart (i.e., HR/DSSS), and orthogonal frequency division multiplexing (OFDM), recommended for the IEEE 802.11 physical layer are then explored. As a result, this chapter can be assumed as a comprehensive overview of the IEEE 802.11 standard.

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There are many ways to classify applications, depending on the criteria one wants to choose: accuracy required, coverage needed, technique employed (dual frequency, autonomous, phase or code measurements, one or multisystem), autonomy, and so on The classi cation that is often suggested by the professionals in the GPS sector is aimed at helping the user to be directed to the right product, whereas the classi cation obtained from the of cial lea ets of the Galileo program is clearly aimed at describing all the possibilities of the future GNSS in order to create revenues Another classi cation could be made according to the type of entities that are equipped, and it appears that the main classes are humans and transportation vehicles The only exceptions to this are related to time-dependent applications that are a little speci c A domain interested in the instrumentation of objects is the transportation of goods.