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We should be able to do this now by inspection. Remember that +3 dB is double the power; the power at the output of a network with +3-dB gain has 2 the power level at the input. Obviously the output of the rst network is 12 mW (point A above). The input to the second network is now 12 mW and this network again doubles the power. The power level at point B, the output of the second network, is 24 mW. The third network double the power still again. The power level at point C is 48mW. Thus we see that a network with an input of 6 mW and a 9-dB gain, will have an output of 48 mW. It multiplied the input by 8 times (8 6 48). That is what a 9-dB gain does. Let us remember: +3 dB is a two-times multiplier; +6 dB is a four-times multiplier, and +9 dB is an eight-times multiplier. Let us carry this thinking one step still further. We now know how to handle 3 dB, whether + or , and 10 dB (+ or ), and all the multiples of 10 such as 100,000 and 0.000001. Here is a simple network. Let us see what we can do with it.

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Law 2. The logarithm of the quotient of two numbers equals the logarithm of the dividend minus the logarithm of the divisor. That is:

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In the section, we analyze noti cation logs. In the following discussion, we use the term noti cation document to refer to a unique document that may be sent to multiple users; we refer to each such transmission as a noti cation message, which includes duplicates. Table 5.3 shows the overall statistics for the noti cation logs. In one week, the server sent out 3.25 million noti cation messages for a total of 295 MB. Onefourth of the messages sent out were distinct, while the remaining messages had the same content but sent to different users (in some cases, the same message is sent to a user multiple times, e.g., if a user has registered for information to be delivered at speci c times and the information has not changed during that period). The signi cant amount of duplication in messages sent to different users suggests that sending noti cation via application-level multicast would be useful; Section 5.5.1.3 examines this issue in greater depth. There were 200,860 distinct users, of which 99.02% were wireless users. The noti cations were sent at the average rate

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Notice that a square is still being drawn, but what turns it into a diamond is the rotate(PI/4) method, which rotates all subsequent drawing by 45 degrees. (In Processing rotation is measured in radians. 2 PI radians == 360 degrees, thus PI/4 radians = 45 degrees. PI is = ~3.14.) This is the exact technique used to rotate and move the Roomba icon without needing to parameterize all the Roomba drawing code.

There are several important points to note regarding the features obtained using EMD First, it is clear that by comparing the frequency amplitude plots for patient 1 and patient 2, the brain activity within the range of 0 to 13 Hz is signi cantly weaker for patient 2 As mentioned earlier, activity within this range is related to the low level and meditative brain activity It can therefore be suggested that there is little or no activity within this crucial range and that it can be used as an indication of inevitable brain death Also note that the same observations can be made from the data given in Figure 108, patient 2 at a later date The second point to note is that there exists strong similarity between the spectra obtained from EEG channels of patient 1.

which becomes a pH pH matrix that ts the result indicated in equation (10) only if its determinant is zero: j1 . . . jpH

For example, compare the frequency amplitude plots of FP1 and FP2 in Figure 106 However, it is clear that this is not the case for patient 2 It can be thus concluded that strong similarities in the range 0 to 13 Hz (particularly in channel FP1 and FP2) indicate that the patient will not succumb to brain death 10541 EEG Phase Analysis using EMD It is clear that if similarities exist between speci c frequency ranges (8 to 13 Hz) among different EEG channels (FP1 and FP2), then this is an indication that the patient will not succumb to brain death For this reason, it is proposed to extract relevant channel features using EMD and to compare these features using complex extensions of the algorithm.

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