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technologies available, such as Assisted-GNSS or High-Sensitivity GNSS, allow light indoor positioning, but exhibit worse performances than outdoors. However, the opposite would have been required as usual indoor dimensions are of a few meters. An accuracy that is not compatible with the automatic determination of oor level is not acceptable for many indoor applications.
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Let a whole le of information be fragmented into K data packets dp1 dp2 . . . dp K . Each transmitted data packet contains m bits, and is going to be correctly received or not, depending on the channel noise. Transmission is synchronous, and successive transmitted packets tpn are ordered by a time index n. At time instant n, the encoder generates a random binary K -tuple {G kn }, and then the transmitted packet tpn is the exclusive-OR sum of all the information data packets for which the random bits in {G kn } are equal to 1 :
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enclosed in , p . , /p . tags. These tags represent the beginning and the end-ofparagraph element. WML has an associated scripting language called WMLScript. Similar to JavaScript and the Web browser (Internet Explorer, Netscape, etc), WMLScript is fully integrated into the WML microbrowser; and therefore it can access the WML state model as well as the WML variables. This adds intelligence to the thin client by allowing it to check for user input, such as validating the input values for variables before sending the request OTA, and thus reducing latency. However, and unlike JavaScript and HTML, WMLScript is not part of a WML deck. It contains separate les that can be referred to from the WML deck using a URI mechanism. WMLScript also provides extensibility to the WAE client by allowing future services to access device peripherals, such as phonebook, calendar, and list of WTA messages. A typical WMLScript is shown in Figure 3.9. The script function name is currencyConvertion and takes two variables from the calling entity: the exchangeRate and the currency. This script sets a variable ret to the result of multiplying the exchange rate and the currency variables. These variables have been passed to the function. It then calls a function from the WMLBrowser library to set WML variable $(CONV_RESULT) to the number in variable ret. Finally, it calls another function from the WMLBrowser library to display the third card where the CONV_RESULT variable will be displayed with its new value. WMLScript supports several categories of operations including assignment, arithmetic, logical, and comparisons. Further, WMLScript de nes several libraries including a language library, a browser library, a string library, a oating library, a URL library, and a dialog library. Every library contains some prede ned functions that are stored on the device and may be considered as part of the microbrowser. The intention behind the libraries is to make WMLScript extensible. Wireless Session Layer (WSP) The WSP is the session layer of the WAP stack. Like HTTP, it provides a request response protocol but in a compact OTA encoding. In short, it enables data exchange between mobile service and applications [28]. WSP has two different services: the connection-oriented mode and the connectionless mode. The connection-oriented service mode operates over the transaction layer protocol (WTP), while the connectionless service mode operates over datagram service (WDP). In the connection-oriented mode, the service keeps track of the context between consecutive requests. While in the connectionless mode, every request is handled independently and there is no guarantee in what order two requests will be served. Therefore, in the latter, the client marks the requests
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Edited by Esko Kurvinen (Helsinki University of Technology, Finland), Renata Guarneri (Siemens, Italy) and Jukka T Salo (Nokia, Finland)
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New menu items can be inserted into existing menus at run-time with the function InsertMenu: InsertMenu(hMenu, uPosition, uFlags, uIDNewItem,lpNewItem); The hMenu parameter specifies the HMENU of the menu into which the new menu item is inserted. uPosition specifies the menu item which the new menu item is to be inserted before. This value is interpreted depending on the uFlags parameter.
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Binary code 1011 1000 1011 1001 1011 1010 1011 1011 1011 1100 1011 1101 1011 1110 1011 1111 1100 0000 1100 0001 1100 0010 1100 0011 1100 0100 1100 0101 1100 0110 1100 0111 1100 1000 1100 1001 1100 1010 1100 1011 1100 1100 1100 1101 1100 1110 1100 1111 1101 0000 1101 0001 1101 0010 1101 0011 1101 0100 1101 0101
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Figure 4.9 The Color Properties dialog box showing the RGB or red, green, blue values for the first circle in Figure 4.8. Notice that the values are different for each of the circles.
Turbo Codes Table 7.1 Code vectors of the block code Cb (5, 3) 0 1 0 1 1 0 1 0 0 1 1 0 0 1 1 0 0 0 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0 1
There are different ways of defining such an order (e.g., Rost, 2004): Order assumption a: The widths (i.e., distance among categories) are different for all categories but the same for all items. Order assumption b: All widths are the same for one item (equidistance assumption) but vary from item to item. This results in a different dispersion for each item. Order assumption c: This variant is a combination of the first two assumptions obtained by adding different dispersions across the items. Order assumption d: Widths differ for each item and each category. Probabilistic models are used to make formal connections between latent and manifest variables and to estimate the values of a person on the latent variables, as well as the values of the item parameters from the observable manifest variables (e.g., item responses). These formal models can be conceived as theories about the response behavior of persons. As a result, they are also called IRT models (IRT = item response theory; see, e.g., Hambleton et al., 1991). Of course, IRT models are not always probabilistic models, and the differentiation between latent and manifest variables is not just a property of probabilistic models. For example, it is also found in structural equation models. However, the particular property of probabilistic models is that they connect manifest variables with latent variables through a probability function, and in most cases, this leads to the major and attractive principles of local independence and monotonicity (e.g., von Davier & Strauss, 2003). The first basic assumption is local independence; that is, for a certain person with given ability, all his or her item responses will be independent. This means that subsequent item responses are not influenced by whether prior tasks have been completed successfully. The assumption implies that if there are any learning effects, and these are considered valid for all, these are only overall learning effects. The second basic assumption in the majority of probabilistic models is that of monotonicity in person and item parameters. This means that the probability of successfully solving an item increases as a function of an increasing person parameter (e.g., ability). Similarly, the item parameter also increases monotonically as a function of the prob-
The calculation: $1,175,200 / $10,680
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