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Among the four algorithms, WFPQ has the least overall delay (76.16 ms), followed by DFPQ (76.24 ms), followed by max-min fair allocation (76.47 ms), and WFQ (76.54 ms). DFPQ has the maximum throughput (maximum number of received packets) and the least delay in the case of BES and nrtPS. Thus, in this scenario, we can say that DFPQ performs best. The throughput for the nrtPS and BES ows keeps in view the higher load offered by them. Yet, UGS has the least delay and BES the maximum. Thus, the heavy load from the lower priority traf c does not effect the delays of higher priority traf c. Comparing with GPSS, we nd that UGS delays are almost the same. GPSS has lower delays for rtPS and nrtPS, whereas GPC has lower delay for BES. Also, throughput for UGS and RTPS is the same. GPSS has more throughput (number of packets received) for nrtPS, whereas GPC has more throughput for BES.

MythTV was designed with 800 600 pixel display resolution in mind (at least, 800 600 before being converted by a TV-out encoder on a video card), with an analog television for display. Default font sizes are tailored for this setup, but for one reason or another, you might want or need to change them. The easiest place to make changes is within the MythTV frontend itself, under the Setup section, which contains a few places where you can adjust font sizes. The first page of the Appearance configuration section includes an option to move your relative font sizes from default, which you might recall is geared for use on a standard-definition TV, to small, designed for use on a computer monitor, and big, which might come in handy for anyone with limited vision. Page 5 of the same section enables you to set actual point sizes to small, medium, or big (medium == default). On the last page under TV Settings Playback, you can again select default, small, or big, this time for the OSD.

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monitoring its distance to the target When the distance becomes larger than a certain threshold d, it will be replaced by the node that is closest to the center of the current monitoring region 1383 Mobicast Protocol for Tracking Mobile Objects One particular application of geocasting is tracking mobile objects Mobile objects create geocast regions that are time dependent, and data collection is performed by the sensors in the vicinity of a moving object The sink may collect reports from the sensors in the vicinity of the object, and may send periodic signals to the sensors adjusting the geocasting region, following the trajectory along with the object advances In mobicast application [45], however, the sensors themselves adjust the geocasting region.

Next, we turn to cluster analysis In Discovering Knowledge in Data: An Introduction to Data Mining [2] we demonstrated hierarchical clustering, k-means clustering, and Kohonen clustering For this case study, however, we shall apply the BIRCH clustering algorithm [5] The BIRCH algorithm requires only one pass through the data set and therefore represents a scalable solution for very large data sets The algorithm contains two main steps and hence is termed two-step clustering in Clementine In the rst step, the algorithm preclusters the records into a large number of small subclusters by constructing a cluster feature tree In the second step, the algorithm then combines these subclusters into higher-level clusters, which represent the algorithm s clustering solution One bene t of Clementine s implementation of the algorithm is that unlike k-means and Kohonen clustering, the analyst need not prespecify the desired number of clusters.

2.03 1.61 1.27 1.01 0.71 0.46

Huang, Lu, and Roman [45,46] proposed a mobicast protocol where the nodes that belong to the forward, time-dependent region, or belong or are about to enter the geocast region, retransmit the message Their algorithm presented in ref [45] is an improved version of the one in ref [46] In their problem formulation, the MC is not used to decide and inform about the geocast region Instead, the sensors themselves cooperate, follow the movement of an object, and inform the sensors, which are approached by the object, to start monitoring This is achieved by considering the planar graph of covering sensors, and forwarding messages to the faces of the planar graph in the direction of object movement, with proper timing corresponding to the arrival time of the object at the considered face This is illustrated in Figure 137.