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/************************************************* * exeMarker.c * * This is a useless module that saves space in an * exe for later stuffing covert data. * If changes are made in this module, delete all * intermediate build files ( clean ) and do a * complete rebuild (rebuild all) of the project. * * Code Designed and Written by Eric Cole and Jim Conley ************************************************* */

To get maximum performance and security out of your wireless network, you need to spend some time con guring and tweaking your wireless router and wireless cards. Some of these tasks should be done during the installation of your network, while others can be done after you have spent a little time using your wireless network. This section of the book uses simple terms to describe the changes to make and the optimal settings to use.

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Perform contextual personalisation of applications and services for an individual user by collecting and storing user context data streams, and robustly processing and interpreting the intrinsically inaccurate stored context data streams In this way, personalisation architecture components can perform sensible contextual personalisation of applications and services Adapt the user pro le and corresponding user preferences according to her behaviour by learning user preferences and rules The user data can be contextualised and also automatically updated for her if she wishes This learning process is applied in order to distil rules or Bayesian schemes used in application recommendations, including the multimodal interface described in the previous section 522 General Approach The main elements of this contextual personalisation architecture are depicted in Figures 513 and 514 This approach brings the means for providing context-dependent recommendations and control actions; ie.

the process of yielding recommendations that takes into account the user s current context The developed approach also supports the learning of new or updates of existing context-dependent user preferences; ie adaptation of the user s pro le to the user s needs and habits For this purpose, the learning process utilises the user s usage behaviour in correlation with the user s usage context Realisation of these contextual personalisation tasks into concrete building blocks is depicted in Figure 515 The Contextual Personalisation Architecture consists of Context Gathering and Interpretation (CGI), Usage Record Provider (URP), Individual Pro le Management (IPM), the Pro le Editor and the Recommender The Contextual Personalisation Architecture is a receiver of raw sensor data data about available services, devices and modalities; and application requests from the Modality Function and other services and applications.

Subsequently, the architecture processes this data in several steps yielding recommendations and control actions as output to any interested service and application needing this kind of information In order to obtain the recommendations and control actions, the above input data has to pass through different functional blocks The rst one is the Context Gathering and Interpretation.

RF AND ESD COMPUTER-AIDED DESIGN (CAD)

11.6.2.2 11.6.2.2.1

As we mentioned earlier, the platform provides several facilities and subplatforms. One of these facilities is the Microsoft Message Queuing (MSMQ) service. This facility can be used for Remote Messaging and in many respects is similar to the Java Message Service (JMS), which we discussed in 15. The platform also provides access to lower-level networking constructs and facilities, including sockets and channels. We can also use the .NET Remoting and marshalled by value server objects we discussed in the previous section to implement Remote Messaging. However, since the DNETSKIT (like the J2EE-SKIT) is not focused on Remote Messaging, we will not discuss any of these facilities further.

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