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See Multifunctional transport satellite (MTSAT) Multiconstellation, 338t multifrequency cycling method, 338 Multiconstellation products Javad, 139 Multifrequency approach Galileo signals, 339f Multifrequency concept Galileo, 338 Multifrequency cycling, 338t Multifunctional transport satellite (MTSAT), 120 Multipath evaluation curve, 192f Multitransport Satellite-Base Augmentation System (MSAS), 83 integrity, 262, 365 Japan, 89t method implementation, 154 United States, 195 National Geospatial Agency (NGA), 132 National Marine Electronics Association (NMEA), 374 Nautical navigation art logical evolution, 31 Navigation Arabian navigation, 5 COMPASS, 122t data generation, 207 eighteenth century travels, 9 fteenth century travels, 6f instruments, 2 message, 166, 252 origins, 1 Phoenicians, 2 Portuguese techniques, 5 QZSS satellites, 119 radio electric signals, 13 14 signals, 119 sixteenth century travels, 6 three-dimensional display example, 388f Navigation history, 1 26 age of great navigators, 5 10 arti cial satellites, 19 22 cartography, lighthouses, and astronomical positioning, 11 rst age of navigation, 1 4 rst terrestrial positioning systems, 15 18 radio age, 12 14 real-time satellite navigation constellations, 23 26 Navigation land earth stations (NLES), 158 Navigation positioning data required, 175 industrial communities, 279 scienti c communities, 279 Navigation satellite systems, 86t, 122t, 186t 187t, 346 positioning techniques, 36 38 second generation, 39 third generation, 40 Navigation techniques displacement measurement, 30 Middle Ages, 6 positioning, 29 re ective mirrors, 8 NAVSTAR GPS, 24 NavTeq, 354 Navy Navigation Satellite System (NNSS), 22 Near-far effect, 334 Newton, Isaac, 8 NGA.

MT::Util::start_background_task(sub { # code for some lengthy, involved process });

See National Geospatial Agency (NGA) NLES See Navigation land earth stations (NLES) NLOS See Non line of sight (NLOS) NMEA See National Marine Electronics Association (NMEA) NNSS See Navy Navigation Satellite System (NNSS) Non-GNSS indoor positioning problem and main techniques, 273 306 global comparisons, 306 hybridization, 302 303 indoor environments, 285 286 indoor positioning, 274 283 inertial systems, 304 infrared radiation (IR), 287 288.

Image Communications 14.1 Background and Objectives 14.2 Appreciation of Video Transmission 14.2.1 Additional De nitions 14.3 Composite Signal 14.4 Critical Video Parameters 14.4.1 General 14.4.2 Transmission Standard Level 14.4.3 Other Parameters 14.5 Video Transmission Standards (Criteria for

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TextPad comes with several clip libraries, its lists of HTML tags or JavaScript properties. Unfortunately, when you select a tag from the clip library, you get every attribute for that tag, with the value of the attribute blank, or a question mark where you might want to insert a value (see Figure 8-5). This feature is confusing unless you know what you re doing and what s worse, it enters a lot of code into your documents you don t really need. Still, TextPad is fast and stable, and it s a genuine improvement over non-HTML-savvy text editors. To get your copy of TextPad, access the program s Web site at www.textpad.com. A shareware product, TextPad, at the time of this writing, costs $27 for the downloadable version and $15 for the floppy disks. It also requires licenses for multiple users; the cost for 5 users is $120, and goes up incrementally depending on the number of users.

Note: A user could get to this detailed and enlarged view by clicking on a speedometer on the first screen of the dashboard, as shown in Exhibit 12.2.

Following this, the post-boot installation process kicks off, and begins another round of Q and A before finally dropping you onto the KnoppMyth desktop.

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interaction, and then evaluate the goodness of t of the transformed data to the model One method that does this is Rasch scaling (Rasch, 1980), but there are others (see Cliff, 1973, 1992 for reviews) Rasch scaling is widely used in mental testing (Wright, 1999) It begins with an additive conjoint model and uses a speci c monotonic transformation, a single-parameter logistic transformation, as a basis for interval scaling (strictly speaking, this is only possible for a dichotomous response; see Fischer, 1995) Operating under some assumptions (eg, unidimensionality), an additive model is t to item responses (the data) and evaluated for goodness-of- t using probabilitybased statistics Suf cient t is evidence that the probability of a response can be expressed as an additive function of a person parameter (usually an index of ability) and an item parameter (usually an index of item dif culty; Wright, 1999).

As with any modeling effort, we start with the simple, see how far it takes us, and then either complicate or discard it as it gets tested against reality. The data available thus far are certainly suggestive and lead to the hope that more will shortly be forthcoming, so that tests and possible falsi cation can be carried out. In the meanwhile, as Darwin said when he rst read Malthus, at least we have something to work with! (p. 8)

itoring. Exhibit 9.14 shows a manufacturing dashboard with operations metrics for daily shifts and a description for production line interruption, if any.

The logistic transformation serves as a basis for an interval scale of the latent person variable on which we can locate individuals (the items are simultaneously scaled as well) Assuming the Rasch model holds (which may be a rare occurrence in applied research; see Embretson & Hershberger, 1999), individuals can be assigned interval-scale scores on the latent variable that are monotonically related to raw scores, yet have more desirable properties (eg, extreme score unbiasedness, sample independence, and linearity) There are problems with Rasch scaling The most common one is that items vary in discrimination, so that more than one item parameter is necessary This usually leads to a violation of one or more of the axioms of conjoint measurement and compromises the interval properties of the scale Another problem is the controversy over evaluating the goodness-of t of the model to sample data (see Hambleton, 1989).

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