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and the mean value of output target channel y. This measure is obviously normalized to y the unit interval and is nearer to 1 the smaller ssres is. Furthermore, it punishes more complex models as integrating the degrees of freedom in the denominator. Moreover, improved fault detection using local error bars can signi cantly outperform the basic one, as two faults more could be detected with the improved approach, where one of these two was a major one and hence mandatory to be detected. Note that rows 2 and 3 represent the results obtained when integrating the approach for overcoming the unlearning effect as outlined in section 5.4.2, that is, only to update the signi cantly ring rules in Step 9 of FLEXFIS Algorithm 1. This is necessary as the online recorded (dynamic) measurement data contain quite a lot of steady-state samples, that is, samples showing an almost constant behavior in a lot of measurement channels over time (for balancing out a new operating condition to get a valid and robust stationary data sample). Rows 4 and 5 show the results suffering quite a bit when the approach for overcoming the unlearning effect is omitted. The threshold here was set to 0.1, which gave best results over various others (varied between 0 and 0.8 in steps of 0.05). A complete sensitivity analysis of this threshold versus the change in fault detection rate achieved when using adaptive local error bars is shown in Figure 5.12. It can be seen that varying the threshold between 0.05 and 0.2 does not affect the fault detection rate signi cantly. The average complexity measured in terms of the number of rules of the nally evolved fuzzy models is 12.2 with a range of between 10 and 22 rules.

Handbook of Wireless Networks and Mobile Computing, Edited by Ivan Stojmenovic Copyright 2002 John Wiley & Sons, Inc. ISBNs: 0-471-41902-8 (Paper); 0-471-22456-1 (Electronic)

Exit Function End If Assign variables RCount = rng.Rows.Count CCount = rng.Columns.Count ReDim V(1 To RCount, 1 To CCount) ReDim ValArray(1 To 2, 1 To CellCount) Fill ValArray with random numbers and values from rng For i = 1 To CellCount ValArray(1, i) = Rnd ValArray(2, i) = rng(i) Next i Sort ValArray by the random number dimension For i = 1 To CellCount For j = i + 1 To CellCount If ValArray(1, i) > ValArray(1, j) Then Temp1 = ValArray(1, j) Temp2 = ValArray(2, j) ValArray(1, j) = ValArray(1, i) ValArray(2, j) = ValArray(2, i) ValArray(1, i) = Temp1 ValArray(2, i) = Temp2 End If Next j Next i

To get you started, a whole host of MT tags have been ported from Perl to PHP. These would be the core MT tag set. Both the Perl and PHP source is available with the 3.1 (and later) distribution, so that should give you a good feel for how to do a number of different types of tags. If you re comfortable with how the Perl API works, you can compare the Perl and PHP implementations for standard MT tags that are similar to your own and see how the Perl code was translated to work for PHP.

about the message such as when the message expires, the message size, and optionally the sender s address. 3. The noti cation triggers in the recipient s terminal a WAP GET (using WSP or HTTP) operation that fetches the message (using its URL) from the MMSC to the mobile device. That transaction contains information about the terminal

Figure 2.9 View of computer screen using Data Desk software for selecting, brushing, and linking across multiple plots. Several plots have been enhanced with multiple regression lines that vary by subsets of selected data.

measure of the very small amount of time the current had been on Now armed with this galvanometric stopwatch, Helmholtz measured the speed of the neural impulse in a frog s leg He knew that mild electrical stimulation of the motor nerve that ran the length of the leg would cause a twitch in the foot muscle, and by balancing the foot on a switch, this movement could be used to turn off a current When the current was turned on the galvanometer was set in motion, but when the foot twitched it was turned off He now compared the times when the nerve was stimulated at different locations along the nerve ber He found that a point four inches from the muscle took 0003 seconds longer than a point only one inch away, meaning that the nerve impulse was traveling at about 83 feet per second.