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as dotted-decimal notation; for example, 132.1.8.10. An IPv6 address is written in hexadecimal and consists of groupings of 8 containing 4 hexadecimal digits or 8 groups of 16 bits each. This takes the form: xxxx : xxxx : xxxx : xxxx : xxxx : xxxx : xxxx : xxxx FEDC:BA98:7654:3210:FEDC:BA98:7654:3210 is an example of an IPv6 address. Another example is the following unicast address: 1080:0000:0000:0000:0008:0800:200C:417A Since writing an IPv6 address has become unwieldy (DNS becomes very important here), there are provisions allowed to condense the address into its smallest available form. Therefore, the preceding unicast address can be compressed into: 1080:0:0:0:8:800:200C:417A or even 1080::8:800:200C:417A The double colon has special significance. It is a demarcation point for compressing leading 0s. Notice here that only leading 0s can be compressed and the :: symbol can be used only once during the compression. Therefore if you had the address of: 1080:0:0:5698:0:0:9887:1234 you cannot write it as:

272 PERVASIVE COMPUTING APPLICATIONS Characteristics of pervasive computing applications have been identified as interaction transparency, context awareness, and automated capture of experiences [2] Pervasive computing aims at nonintrusiveness It contrasts with the actual nontransparency of current interactions with computers Neither input output devices nor user manipulations are natural Input output devices such as mouses, keyboards, and monitors are pure artifacts of computing So are manipulations such as launching a browser, selecting elements in a Web page, setting up an audio or video encoding mechanism, and entering authentication information (eg, a log-in and a password) Biometrics security is a field aimed at making authentication of users natural It removes the log-in and password intermediate between the user and the computer To identify an individual, it exploits the difference between human bodies Authentication is based on physical measurements To be usable, however, the measurements must be noninvasive and fast.

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There are two senders and one receiver for this channel. Sender 1 chooses an index W1 uniformly from the set {1, 2, . . . , 2nR1 } and sends the corresponding codeword over the channel. Sender 2 does likewise. Assuming that the distribution of messages over the product set W1 W2 is uniform (i.e., the messages are independent and equally likely), we de ne the average probability of error for the ((2nR1 , 2nR2 ), n) code as follows: Pe(n) = 1 2n(R1 +R2 ) Pr g(Y n ) = (w1 , w2 )|(w1 , w2 ) sent .

DNA analysis does not meet that criteria, but fingerprint identification does Other alternatives include facial characteristics, voice printing, and retinal and typing rhythm recognition Input biometric information hardware and software are being marketed It is interesting to note that practical evaluations have reported that biometric input is often not recognized and needs to be accompanied by a conventional authentication procedure (log-in and password) in case the biometric authentication fails [12] Another example of interaction transparency is the electronic white-board project called Classroom 2000 [12] An electronic white-board has been designed that looks and feels like a white-board rather than a computer With ideal transparency of interaction, the writer would just pick up a marker and start writing with no plug-in, no log-in, and no configuration.

Time 1 and membership in latent class c (e.g., the probability of responding yes to felt depressed, given membership in the female group and membership in the depression latent status); similarly, j |b,c represents the same quantity for Time 2, and so on. represents the probability of response p to Item 8 at Time 1, conditional on membership in latent status a at Time 1 and membership in latent class c (e.g., the probability of responding yes to life not worth living, given membership in the female group and membership in the depression latent status); similarly, p |b,c represents the same quantity for Time 2. b|a,c represents the probability of membership in latent status b at Time 2, conditional on membership in latent status a at Time 1 and membership in latent class c (e.g., the probability of membership in the depression latent status in Grade 12, given membership in the female group and membership in the no depression latent status in Grade 11). The parameters are usually arranged in the form of a transition probability matrix.

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