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

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15For certain methods of access, the terrain class will also automatically interpolatenew data as needed. The user only needs to specify the size of the desired vector ormatrix they wish to fill with terrain data, and give the bounding regions of the desiredarea. The terrain library will then automatically fill the given vector or matrix withthe necessary amount of data, interpolating data as needed.TerrainReader ClassThe TerrainReader class is what handles reading the data from specific digitalterrain file formats. The TerrainReader makes use of a specification file in order todefine what data to load and how it should be loaded. Below is a list of the definableparameters for this specification file.• path:Should contain a full path to the desired terrain data files. Any standardformat for specifying file paths can be used.• bottom left:Specifies the lower left corner of the desired viewport into thegiven terrain set. This is specified in latitude and longitude coordinates in theformat W76.6 N39.9.• top right:Specifies the upper right corner of the desired viewport into thegiven terrain set. This is specified in latitude and longitude coordinates in theformat W76.6 N39.9.All files that contribute to the desired viewport will be loaded in their entirety.Each file is first loaded into a TerrainPatch object, which are simple temporarycontainers for the file data.As each file is loaded its TerrainPatch is added to aTerrain object. Once all files have been loaded, the Terrain object performs a sortingof all the TerrainPatch objects based on their latitude and longitude. Figure 2.10illustrates the proper sorted order of an example set. Once sorted, the TerrainPatch
16objects are collapsed together into a single matrix within the Terrain object, thisremoves all boundaries that once existed between adjacent files. If a bounding boxwas defined in the specification file then this matrix is then trimmed down such thatonly data that belongs to that desired bounding box remains in memory.Figure 2.10: DTED file ordering2.4.2 Integration with OMANThe design of the terrain library allowed for easy integration with OMAN. OMANis able to use the terrain library to read in specific terrain to match the desiredsimulation arena. The terrain can then be visualized along with the simulated networksetup in either 2D or 3D. In both 2D and 3D visualizations the terrain data is turnedinto a height map that can be colored in a series of different color schemes. Figure2.11 shows a sample of the different color schemes available to visualize the terrain.Users can also choose what minimum and maximum elevation values to use whencomputing the color schemes. They can choose to use the current terrain’s minimumand maximum values, the known highest and lowest terrain values in the UnitedStates, or the known highest and lowest terrain values in the world. Once the terrain
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: 14TerrainResolutionChanger ClassDif
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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