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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.

In this modi ed state diagram branches emerging and arriving at the states are denoted by the term X i , where i is the weight of the code sequence that corresponds to that branch For the example of the convolutional code Cconv (2, 1, 2),introduced in previous sections, the modi ed state diagram is shown in Figure 615 Paths starting and arriving at the all-zero state Sa have a weight that can be calculated by adding the exponents i of the corresponding terms of the form X i In this case, for instance, the path Sa Sb Sc Sa has a total weight 5, and path Sa Sb Sd Sc Sa is of weight 6 The remaining paths include the loops Sd Sd and Sb Sc Sb that only add weight to the paths described above.

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.

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).

Therefore the minimum distance in this code is equal to 5, and it is usually called the minimum free distance, df = 5 The adjective free comes from the fact that there are no restrictions on the length of the paths of the corresponding trellis or state diagram involved in its calculation For convolutional codes of more complex structure, the above calculation of the modi ed state diagram, and its solution to determine the minimum free distance, is not that simple..

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.