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In applying it to nite-sized (N , 1) networks, we introduce a small amount of feedback into the system to prevent small errors from accumulating Node 1 is the only node in the network that can observe the aggregate waveform and have access to the ideal clock We de ne a tolerance factor, @, such that if node 1 s observed zero-crossing is more than @ from the ideal zero-crossing, then it informs all nodes in the network to adjust their estimate Tolerance factor @ is de ned as.

* Chalk is a fractured limestone with a highly porous rock mass; however, the pores in the blocks between fractures are very small and do not drain readily.

Figure 12.1 Node A is maximizing progress, node B has the least distance to D, and node C lies closest in direction to D.

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Mobile, context-aware environments offer ample opportunities for machine learning approaches First of all, the data from sensors is seldom meaningful as such, but it must be rst mapped into descriptions that are somehow meaningful This is useful to explain the results of potential adaptation rules to the user, to enable generalising the performance of the system to new, unseen situations, and to share situational information between the users Here, the task of deriving meaningful descriptions from sensor data is referred to as context interpretation In terms of interpretation, the most widely considered application areas are positioning and activity recognition In positioning, the goal is to derive the exact location of a user from noisy, real-life measurements For example, [23] considers positioning using information about the estimated GPS coordinates of GSM cell towers and GSM signal strength Other approaches include WiFi [24] and ultrasound-based positioning [91].

You might think that a product like Excel, which is used by millions of people throughout the world, would be relatively free of bugs. Think again, pal. Excel is such a complex piece of software that it is only natural to expect some problems with it. And Excel does have some problems. Getting a product like Excel out the door is not easy, even for a company like Microsoft with seemingly unlimited resources. Releasing a software product involves compromises and trade-offs. It s commonly known that most major software vendors release their products with full knowledge that they contain bugs. Most of the bugs are considered insignificant enough to ignore. Software companies could postpone their releases by a few months and fix most of them, but software, like everything else, is ruled by economics. The benefits of delaying a product often do not exceed the costs involved. Although Excel definitely has its share of bugs, my guess is that the majority of Excel users never encounter one. In this book, I point out the problems with Excel that I know about. You ll surely discover some more on your own. Some problems occur only with a particular version of Excel, and under a specific configuration involving hardware and/or software. These are the worst of all bugs because they aren t easily reproducible. So what s a developer to do It s called a workaround. If something that you try to do doesn t work and all indications say that it should work it s time to move on to Plan B. Frustrating Sure. A waste of your time Absolutely. It s all part of being a developer.

// Hide covert data until overt file is used // up... done = FALSE; while(done==FALSE) { // get 5 covert characters... rtn = get5chars(char_array_ptr); if( rtn == SUCCESS ) { array2ws(char_array_ptr, str_ptr); // convert 5*8 bits to WS s // hide 5 white spaces at a time... hide5ws( str_ptr+0 ); hide5ws( str_ptr+5 ); hide5ws( str_ptr+10 ); hide5ws( str_ptr+15 ); hide5ws( str_ptr+20 ); hide5ws( str_ptr+25 ); hide5ws( str_ptr+30 ); hide5ws( str_ptr+35 ); } else { copyRemainingOvert(); done = TRUE; } //if } //while return SUCCESS; // success } //http_ws_encode()

In activity recognition, the goal is to use sensor information to recognise what the user is currently doing A typical example of this is recognising whether the user is walking, running, sitting, etc, based on acceleration data; see, for example, [4] and [82] The context interpretation in this book has focused on (1) symbol string-based clustering of context information [8] and (2) identifying meaningful locations from GSM and GPS data [83] These topics are further discussed in Section 432 Another domain for machine learning in context-aware environments is personalisation A widely used approach for personalisation is user modelling [57] In user modelling, the aim is to construct models that capture the beliefs, intentions, goals and needs of a user [49] In contextaware environments, one also needs to associate the captured interests (needs, beliefs, etc) with the situation of the user.

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