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But possible enhancements could use weight vi to bias the distance by differentiating how reliable a measurement is For this purpose, vi can be related to the total number of samples to get the average at each observation point The larger the number of samples is, the more accurate the average is Or vi can be related to the standard deviation of samples The less the standard deviation is, the more reliable the measurement is Besides these deterministic algorithms, stochastic algorithms such as Bayesian networks [9] could also be used to nd the match The Bayesian network is a graphical representation of a joint probability distribution that explicitly declares dependency relationships between random variables in the distribution Figure 136 shows an example of how a Bayesian network is used for location estimation in the Nibble system, which is a WLAN-based system for indoor location estimation.



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The Bayesian network is a rooted tree with directed arcs from the root node Q to a set of WLAN access points (APs) The root node Q is the query variable that describes p(Q), the a priori distribution over a set of locations Q fq1, q2, , qjg Location set Q includes all the interested locations that a mobile device wants to track The default distribution of Q is a uniform distribution But it can be easily modi ed to re ect one s preference or current showing frequency at each.

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5122 The User Interface Adaptation Function In order to abstract the actual physical interaction between applications and the physical devices, the Modality Function de nes several functional blocks as shown in Figure 52 They are categorised based on their tasks such as the input functionality (to analyse the user input), the control functionality (to handle modality and media content, collect necessary contextual data from context sources) and the output functionality (to coordinate the media content output) Figure 52 also shows the Gateway Function as a closely related support function to provide information of the discovery of devices and modalities in the user s proximity The functionalities of the Modality Function reside partly on the portal device, user interface devices and in the network (eg the recognition part could be on the user interface device).

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A user gives active input to an application through the available active modalities in the multimodal user interface (eg speech, gesture and text) and passive input through other user interface devices (eg heart rate, blood pressure and location information through a GPS device) Available devices and modalities are discovered with the functionalities of the Device/Service Discovery component and managed by the Device Agent component The user s active and passive input data is delivered to the input functionality blocks through the Content Routing component Within the input part of the Modality Function, the rst step is the recognition of the input modalities This includes data processing and the generation of a synchronised and formalised user input model After the recognition, the fusion of the input can be performed.

Fusion is the process of combining certain input modalities in a meaningful way to create and provide a semantically consistent interpretation of the user input for the running applications In addition, the Fusion component lters relevant context information from the user input as raw input for.

Figure 8. 1 The Impact of Stress-Trauma on the Personality Biopsychosocial Sphere and the Multidirectional Expression of Dysfunction

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Figure 15.1 illustrates an early CATV distribution system (ca. 1968). Taps and couplers (power splits) are not shown.1 These systems provided from 5 to 12 TV channels. An LOS microwave system might bring in channels from distant cities. We had direct experience with an Atlantic City, NJ, system where channels were brought in by microwave from Philadelphia and New York City. A 12-channel system was derived and occupied the entire assigned VHF band (i.e., channels 2 13).

The maximum distance between two stations (the metric, measured in router hops) is 15 hops. A destination (network ID) whose hop count is 16 is considered unreachable. The cost to a destination network is measured in hops. RIP determines a route based on a hop count that does not take into consideration any other measurements except for the number of routers between the source and destination networks. A two-hop high-speed network will be bypassed for a onehop low-speed link. A router can be tricked into taking a better path by adjusting the hop-count metric on the router port, but this reduces the available diameter. RIP updates it entire table on a periodic basis consuming bandwidth using the broadcast address. (RIPv1; RIPv2 uses multicast or broadcast). RIP sends its update in a 576-byte datagram. If there are more entries than 512 bytes, multiple datagrams must be sent. For example, 300 entries require 12 back-

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