Algorithms for the visualization and simulation of mobile ad hoc and ...

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37range.The ratio of weight range to number of nodes on the other hand is quite large.For the citation graph the Shortest Path metric generated a ratio of 83.2 : 1 and forthe inet graph a ratio of 2872.6 : 1 was generated. With such large weight-rangeto-node-countratios the difference between simplifying with two distinct thresholdvalues may be only one or two nodes.Figures 3.4 show an example of the visual result generated by applying the numberof shortest paths metric on the Inet graph. Figures 3.5 show the result of applyingthis metric to the Citation graph.(a)(b)(c)(d)Figure 3.4: Inet graph with 4500 Nodes: Shortest Paths metric with threshold values(a)1000, (b)40000, (c)100000, (d)645000.
38(a)(b)(c)(d)Figure 3.5: Citation Graph with 1025 Nodes: Shortest Paths metric with thresholdvalues (a)1404, (b)4212, (c)28828, (d)48828.Overall the metric does not fit very well into our definition of an effective metric.The weight distribution tends to be heavily biased to the lower end of what is typicallya very large range of weight values. Such a large range of weight values actually givestoo much granularity between each distinct threshold value in the higher weightednodes.The removal of one or two nodes rarely makes any distinguishable visibledifference in the graphs.This metric could be made more effective by scaling ornormalizing the generated weight values. Doing this should even out the distributionand contract the overly large range of weight values.
Page 2 and 3: c○ Copyright 2009Daniel Hennessey
Page 4 and 5: iiiTable of ContentsList of Tables
Page 6 and 7: vList of Tables3.1 This table lists
Page 8 and 9: vii2.12 Figures showing OMAN networ
Page 10: ixAbstractAlgorithms for the Visual
Page 13 and 14: 2provement of the simulations. Chap
Page 15 and 16: 4on the clarity, quality, and richn
Page 17 and 18: 6(a)(b)Figure 2.1: Signal radiation
Page 19 and 20: 8(a)(b)Figure 2.3: Acceptable SINR
Page 21 and 22: 10Terrain ClassThe Terrain class is
Page 23 and 24: 12hull operation. In Figure 2.7 the
Page 25 and 26: 14TerrainResolutionChanger ClassDif
Page 27 and 28: 16objects are collapsed together in
Page 29 and 30: 18its results. An example of this w
Page 31 and 32: 20(a)(b)(c)(d)Figure 2.14: Figures
Page 33 and 34: 22library that was implemented in s
Page 35 and 36: 24connected at the outset, we ensur
Page 37 and 38: 26types of graph data. Our goal is
Page 39 and 40: 28metric is similar to the shortest
Page 41 and 42: 30cause the graph to fragment. In o
Page 43 and 44: 323.5 Metric Performance CriteriaOn
Page 45 and 46: # Nodes200 600 1000 Citation 800 12
Page 47: 36(a)(b)(c)(d)Figure 3.3: Citation
Page 51 and 52: 40(a)(b)(c)(d)Figure 3.6: Inet grap
Page 53 and 54: 42(a)(b)(c)(d)Figure 3.8: Inet grap
Page 55 and 56: 44(a)(b)(c)(d)Figure 3.10: Inet gra
Page 57 and 58: 46(a)(b)(c)(d)Figure 3.12: Inet gra
Page 59 and 60: 48sensing algorithms.This chapter i
Page 61 and 62: 50Figure 4.1: Basic diagrams compar
Page 63 and 64: 52Figure 4.3: Graph showing a compa
Page 65 and 66: 54part to the lack of a capable hig
Page 67 and 68: 56in [44]. This method breaks the s
Page 69 and 70: 58L ≤ N ′ /4 in order to provid
Page 71 and 72: 604.5.2 Test DataIn testing the imp
Page 73 and 74: 62MatlabM 512 1024 2048 409625% Ove
Page 75 and 76: 644.6.2 FFT Accumulation MethodThe
Page 77 and 78: 66CUDAN 1024 2048 4096 8192Memory T
Page 79 and 80: 684.7 ConclusionThe computational c
Page 81 and 82: 70Bibliography[1] Ravindra K. Ahuja
Page 83 and 84: 72[25] M.Y. Kao, N. Occhiogrosso, a

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