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

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11Figure 2.6: The circle represent discrete points in the terrain grid. The solid box isthe users original desired bounding box. The box is enlarged to the nearest discretepoints that still encompass the original set of desired data.straight line through the terrain and the number of points the user wants returnedalong this line. A vector of elevation values is then returned with the desired numberof points. Elevation data points are interpolated as needed to fill out the desired sizedvector.A horizontal profile is generated by defining a region of interest in the terrain byusing a bounding box and giving a cutoff elevation value. This region of interest isthen processed such that all points with elevation greater than or equal to the cutoffelevation remain and all others are dropped. In Figure 2.7 these points are representedby the white dots. Once this is done, polygons are created from the various connectedcomponents of these remaining elevation points. This is done using a simple convex
12hull operation. In Figure 2.7 these polygons are shown as the green lines surroundingsets of white points. This set of polygons is then returned to the user.Figure 2.7: Example of the results produced by a horizontal profile of a region ofterrain. The left side is the original terrain visualized in 3D. The right side are theresults of applying the horizontal profile method.Handling WaterMost digital terrain file formats account for areas of water insome fashion. This terrain library maintains a map of where any water resides withinany loaded terrain data. The user can then query the terrain class using an isWater()function for any point within the grid and get a boolean response.In most digital terrain file formats, the elevation values are measured from sealevel. Any zero valued terrain cells are then considered to be water. Identifyingthese bodies of water is a simple search while loading the terrain data from thefiles. Some digital terrain file formats, such as DTED, specify a means of identifyingbodies of water that are not at sea level, such as lakes, reservoirs, or rivers. These
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: 10Terrain ClassThe Terrain class is
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 and 48: 36(a)(b)(c)(d)Figure 3.3: Citation
Page 49 and 50: 38(a)(b)(c)(d)Figure 3.5: 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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