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

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13bodies of water must be larger than a certain size in order to be captured in theterrain data. Those bodies of water which meet the size criteria are ”flattened” inthe terrain data. This means that all elevation values belonging to the water body areset to the same elevation. The method of detecting these water bodies then searchesfor these flattened areas of terrain.The size requirements are important in orderto differentiate what could be potential bodies of water from plateaus of land thatmay also be flat. This terrain library implements the this algorithm for identifyingthese bodies of water and allowing the user access to them both for computation andvisualization.(a)(b)Figure 2.8: Figure (a) shows a sample of terrain visualized without having it lakesdetected. Figure (b) then shows the same terrain with its lakes identified and visualized.
14TerrainResolutionChanger ClassDifferent digital terrain file formats deal with data of different resolutions. Someformats are very fine-grained while others are rather course. As an example, DTEDfiles come in varying resolutions, the lowest resolution spaces elevations points everyone kilometer while the highest resolution spaces points every thirty meters. In asimulation the users defined terrain region could be in an arena that spans only a fewkilometers. If using very course terrain data, the number of actual data points theuser would receive is very few. To help the user rectify this problem we have createda tool that allows the users to redefine the resolution of their terrain data very easily.The user simply needs to load their desired data into our terrain class and use theTerrainResolutionChanger to adjust the resolution of the terrain data. The user onlyneeds to tell the tool what they want the new spacing between adjacent elevationpoints to be. The TerrainResolutionChanger then interpolates new data points tomeet this new resolution requirement using bilinear interpolation. Interpolating thevalues between data points offers the user much more valuable data that can be usedin running a simulation. An example of this is illustrated in figure 2.9.Figure 2.9: DTED point interpolation: circles are actual data points, diamonds areinterpolated points. After interpolation the user has much more data to work with.
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: 12hull operation. In Figure 2.7 the
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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Algorithms for the visualization and simulation of mobile ad hoc and ...

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