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

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vList of Tables3.1 This table lists the Weight Range to # of Nodes ratios for each metric andeach graph they were run against............................................... 334.1 Timing results from Matlab execution of Welch’s Method of averaged periodogramsfor estimation of PSD. Timings showed for varying segmentand overlap sizes. ................................................................ 624.2 Timing results from CUDA execution of Welch’s Method of averaged periodogramsfor estimation of PSD. Timings showed for varying segmentand overlap sizes. ................................................................ 634.3 Times speedup of CUDA over Matlab for each segment size and overlaypercentage. ....................................................................... 634.4 Break down of the timings of the Matlab based FAM code. N indicatesthe size of the signal being processed. All times given are in milliseconds. . 654.5 Break down of the timings of the CUDA based FAM code. N indicatesthe size of the signal being processed. All times given are in milliseconds. . 664.6 Shows how many times faster the GPU executed the FAM algorithm thanthe CPU. ......................................................................... 66
viList of Figures2.1 Signal radiation patterns for the two channel state models, (a) fully knownchannel state, (b) highly unstable channel state. .............................. 62.2 Simplified versions of the signal radiation pattern visualizations for the twochannel state models, (a) fully known channel state, (b) highly unstablechannel state. .................................................................... 72.3 Acceptable SINR regions with signal radiation patterns for the two channelstate models, (a) fully known channel state, (b) highly unstable channelstate............................................................................... 82.4 Acceptable SINR regions with signal radiation patterns for the two channelstate models, (a) fully known channel state, (b) highly unstable channelstate............................................................................... 92.5 UML diagram of the design of the terrain library. ............................ 92.6 The circle represent discrete points in the terrain grid. The solid box is theusers original desired bounding box. The box is enlarged to the nearestdiscrete points that still encompass the original set of desired data. ........ 112.7 Example of the results produced by a horizontal profile of a region ofterrain. The left side is the original terrain visualized in 3D. The rightside are the results of applying the horizontal profile method. ............... 122.8 Figure (a) shows a sample of terrain visualized without having it lakesdetected. Figure (b) then shows the same terrain with its lakes identifiedand visualized. ................................................................... 132.9 DTED point interpolation: circles are actual data points, diamonds areinterpolated points. After interpolation the user has much more data towork with. ........................................................................ 142.10 DTED file ordering .............................................................. 162.11 These figures illustrate 4 of the different possible color maps that can beapplied to the visualization of the terrain in 2D. (a) is a standard terraincolor gradient, (b) is a forest gradient shifting from white to green, (c) isa standard gray scale gradient, and (d) is an HSV color gradient startingat blue and sweeping to red. .................................................... 17
Page 2 and 3: c○ Copyright 2009Daniel Hennessey
Page 4 and 5: iiiTable of ContentsList of Tables
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 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
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50Figure 4.1: Basic diagrams compar
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52Figure 4.3: Graph showing a compa
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54part to the lack of a capable hig
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56in [44]. This method breaks the s
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58L ≤ N ′ /4 in order to provid
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604.5.2 Test DataIn testing the imp
Page 73 and 74:
62MatlabM 512 1024 2048 409625% Ove
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644.6.2 FFT Accumulation MethodThe
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66CUDAN 1024 2048 4096 8192Memory T
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684.7 ConclusionThe computational c
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70Bibliography[1] Ravindra K. Ahuja
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72[25] M.Y. Kao, N. Occhiogrosso, a
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