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Procedural design

Software Procedural Design (SPD) converts and translates structural elements into procedural explanations. SPD starts straight after data design and… Expand
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2017
2017
We present the first realistic, physically based, fully coupled, real-time weather design tool for use in urban procedural… Expand
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2014
2014
We present a system for the lighting design of procedurally modeled buildings. The design is procedurally specified as part of… Expand
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Highly Cited
2011
Highly Cited
2011
Goal-oriented requirements engineering (GORE) has been introduced as a means of modeling and understanding the motivations for… Expand
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2007
2007
This paper presents a novel approach for the automatic creation of vegetation scenarios in real or virtual 3D cities in order to… Expand
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2007
2007
Negotiation over conflicting interests and demands involves two separate but interacting dimensions. One dimension is given by… Expand
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2005
2005
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, February 2004. 
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2003
2003
This paper presents a new procedural analog design tool called PAD. It is a chart-based design environment dedicated to the… Expand
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1999
1999
This paper presents a new approach to the use of design patterns for the reengineering of legacy code, using a mapping from… Expand
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1999
1999
.......................................................................................................................................................... v 1 . INTRODUCTION ............................................................................................................................................ 1 PART ONE ............................................................................................................................................................ 5 2 . LATTICE MODELS ........................................................................................................................................ 7 3 . PROPOSED LATTICE MODEL ................................................................................................................. 13 3.1 MODEL SOLUTIONS .................................................................................................................................... 16 .............................................................................................................................................. 3.2 TABULATION 28 4 . MODEL IMPLEMENTATION ................................................................................................................... 41 .................................................................................................................................................. 4.1 PREAMBLE 41 .................................................................................................................................. 4.2 MODEL ~IMULATION 44 4.2.1 Monte Carlo simulation ..................................................................................................................... 44 ........................................................................................................................ 4.2.2 Construction of chain 47 4.2.3 Energy computation and the Metropolis algorithm ......................................................................... 51 4.2.4 Random number generator (RNG) .................................................................................................... 52 4.2.5 Experimental results .......................................................................................................................... 53 4.3 APPLICATION TO MULTISPECTRAL ANALYSIS .......................................................................................... 55 4.3.1 Problem description and implementation assumptions ................................................................... 55 4.3.2 Algorithm for implementation ........................................................................................................... 58 4.4 RATIONALE FOR USAGE AN EXPERIMENTAL EVIDENCE ....................................................................... 60 .......................................................................................................................... 4.4.1 Experimental design 60 4.4.2 Results interpretation ........................................................................................................................ 65 4.5 EXPERIMENTS ON REMOTE SENSING DATA .............................................................................................. 75 4.5.1 DCJightline data ........................................................................................................................... 75 ......................................................................................................................................... 4.5.2 Forest data 80 PART TWO ......................................................................................................................................................... 83 5 . INTERPRETING REMOTE SENSING DATA ........................................................................................ 85 ................................................................... 6 . A CASE STUDY . ANALYZING THE D.C. FLIGHTLINE 97 6.1 THE FUSION SUITE ..................................................................................................................................... 98 ................................................................................................... 6.1. I Maximum likelihood classification 98 ........................................................................ 6.1.2 Unsupervised segmentation using the lattice model 99 6.1.3 Fusion . HYDICE data + Digital Elevation Map (DEM) ................................................................ 99 .................................................................................................... 6.1.4 Decision tree (or graph) structure 99 ............................................................................................................................................ 6.1.5 Masking 100 6.1.6 Negative training ....................................................................................................................... 100 .................................................................................................................... 6.2 D.C. FLIGHTLINE ANALYSIS 100 ....................................................................................................... 6.2.1 Scrubbing . Removing bad data 100 6.2.2 Root Node Statistical analysis ....................................................................................................... 103 6.2.3 Node 1 Separating WATER + SHADOW ........................................................................................... 106 6.2.4 Node 2 Segmenting SHADOW .......................................................................................................... 108 .............................................................................................. 6.2.5 Node 3 Separating GRASS and TREE I10 ............................................................................................................ 6.2.6 Node 4 Extracting roofops 113 ................................................................................................ 6.2.7 Node 5 Negative training on PATH 1.21 ........................................................................ 6.2.8 Nodes 6, 7 Negative training on WATER^, WATER 124 ........................................................................ 6.2.9 Node 8 Re-assigning SHADOW (spectral method) 128 ............................................................ 6.2.10 Node 9 Negative training on classes WATER + WATER2 130 6.2.11 Node 96 Reassigning class SHADOW (spatial scheme) ............................................................... 133 6.2.12 Synopsis ........................................................................................................................................... 135 ........................................................................................................................................ BIBLIOGRAPHY 1 3 9 APPENDICES ................................................................................................................................................... 147 ....................................................................................................... . APPENDIX A T H E LATTICE MODELS 147 ............................................................................................... A . I MATLAB code for solving 2xN model 147 A.2 MATLAB code for solving 3xN model ............................................................................................... 147 ............................................ A.3 MATLAB code for generating transfer matrices for L-ary 3xN model 149 ....................................................... A.4 MATLAB code for computing magnetization M for 2xN model 149 A.5 MATLAB code for computing correlation C for 2xN model ............................................................. 150 ............................................................. A.6 MATLAB code forperjormance estimation for 2xN model 151 ............................................................................................. A . 7 Correlation calculation for test images 152 ...................................................................... A.8 Model characteristics and performance of estimators I52 GLOSSARY ....................................................................................................................................................... 159 
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1980
1980
This study demonstrated the viability of using a changing criterion procedure for increasing the work production rates of three… Expand
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