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Concerning the clock biases, the evolution over time of these parameters is almost j linear. Thus, when computing the double difference (the variation of di 2 dk ), k this parameter is almost totally eliminated. The same applies to the difference of the satellite to outdoor-receiving-antenna distances, which is also negligible. The variation of the atmosphere delays, from one instant to the next, is neglected as well. With this technique, it is possible to obtain quite accurate two-dimensional positioning (in this case, the zr coordinate is considered as known): (



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The printSensors() function is a simplified version of the RoombaComm.sensorsAsString() method, which dumps out all the sensor data in a human-readable form. When running Spy.java, you ll notice that transitory events (like quickly tapping the bump sensor) will get missed. This is due to the fact that if an action happens between calls to updateSensors(), it will be missed. To minimize this problem, you can decrease the pausetime setting between sensor readings, at the expense of the output scrolling by very quickly. If you re recording the output of Spy.java to a file for later analysis, set pausetime to whatever you feel is appropriate for your application. You may also notice the time between sensor readings varies a little. If you want a more accurate fixed time between readings, compute how long updateSensors() takes to execute by wrapping it in System.currentTimeMillis() calls, and subtract the execution time from pausetime.





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The receiver replies to this with a CTS packet to indicate readiness to receive the data This mechanism can be used to capture the channel control before the transmission of long packets, thus avoiding long collisions In addition, the RTS/CTS mechanism solves the hidden station problem during the transmission of the user data [21] Further considerations on the protection provided by the RTS/CTS mechanism against the hidden terminal problem can be found in 27 of this handbook 732 IEEE 80211 Performance The physical layer technology determines some network parameter values, eg, SIFS, DIFS, and backoff slot time Results presented below are obtained by assuming the fre-.

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These upper bounds are achieved when X1 N(0, P1 ) and X2 = N(0, P2 ) and de ne the capacity region. The surprising fact about these +P inequalities is that the sum of the rates can be as large as C P1N 2 , which is that rate achieved by a single transmitter sending with a power equal to the sum of the powers. The interpretation of the corner points is very similar to the interpretation of the achievable rate pairs for a discrete multiple-access channel for a xed input distribution. In the case of the Gaussian channel, we can consider decoding as a two-stage process: In the rst stage, the receiver decodes the second sender, considering the rst sender as part of the noise. This decoding will have low probability of error if R2 < C( P1P2 ). After +N the second sender has been decoded successfully, it can be subtracted out and the rst sender can be decoded correctly if R1 < C( P1 ). Hence, this N argument shows that we can achieve the rate pairs at the corner points of the capacity region by means of single-user operations. This process, called onion-peeling, can be extended to any number of users. If we generalize this to m senders with equal power, the total rate is C mP , which goes to as m . The average rate per sender, N 1 C( mP ), goes to 0. Thus, when the total number of senders is very large, m N

Because the system provides for location, fragmentation, and replication transparencies, we don t need to worry about any of the distribution details. As a result, our query

Select the proper frame rate. Frame rate indicates how many frames are played each second. If you have a higher data rate clip, a lower frame rate can improve playback on lower-end computers. For example, if you are compressing a talking head clip with little motion, cutting the frame rate in half will probably only save you 20% of the data rate. However, if you are compressing high-motion video, reducing the frame rate has a much greater effect on the data rate.

procedures are active during the learning process. The con guration for the online learning process is basically the same (r 0:3 and W diag 750 ) with the exception of the new parameter for the similarity threshold between fuzzy sets, l 0:9. The merging of similar fuzzy sets takes place when the similarity is greater than l. Figure 6.3 shows information about the evolution of the online learning process. Table 6.2 contains T A B L E 6.2. Fuzzy Model Parameters with Online Interpretability

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