Subdivision surface fitting to a dense mesh using ridges and umbilics

Fitting a sparse surface to approximate vast dense data is of interest for many applications: reverse engineering, recognition and compression, etc. The present work provides an approach to fit a Loop subdivision surface to a dense triangular mesh of arbitrary topology, whilst preserving and aligning the original features. The natural ridge-joined connectivity of umbilics and ridge-crossings is used as the connectivity of the control mesh for subdivision, so that the edges follow salient features on the surface. Furthermore, the chosen features and connectivity characterise the overall shape of the original mesh, since ridges capture extreme principal curvatures and ridges start and end at umbilics. A metric of Hausdorff distance including curvature vectors is proposed and implemented in a distance transform algorithm to construct the connectivity. Ridge-colour matching is introduced as a criterion for edge flipping to improve feature alignment. Several examples are provided to demonstrate the feature-preserving capability of the proposed approach

User-guided free-form asset modelling

In this paper a new system for piecewise primitive surface recovery on point clouds is presented, which allows a novice user to sketch areas of interest in order to guide the fitting process. The algorithm is demonstrated against a benchmark technique for autonomous surface fitting, and, contrasted against existing literature in user guided surface recovery, with empirical evidence. It is concluded that the system is an improvement to the current documented literature for its visual quality when modelling objects which are composed of piecewise primitive shapes, and, in its ability to fill large holes on occluded surfaces using free-form input

Flexible G1 Interpolation of Quad Meshes

International audienceTransforming an arbitrary mesh into a smooth G1 surface has been the subject of intensive research works. To get a visual pleasing shape without any imperfection even in the presence of extraordinary mesh vertices is still a challenging problem in particular when interpolation of the mesh vertices is required. We present a new local method, which produces visually smooth shapes while solving the interpolation problem. It consists of combining low degree biquartic Bézier patches with minimum number of pieces per mesh face, assembled together with G1-continuity. All surface control points are given explicitly. The construction is local and free of zero-twists. We further show that within this economical class of surfaces it is however possible to derive a sufficient number of meaningful degrees of freedom so that standard optimization techniques result in high quality surfaces

A Windows program for airfoil design using B-splines

The objective of this thesis has been threefold. First, the formulation of splines has been studied and their development into a computer algorithm has been implemented. Splines represent a powerful concept in computer modeling and geometric representation, for, as parametric curves, they provide a compact way to store the information defining a curve or surface. B-splines have been exclusively used in this thesis, although other types of splines exist. The second goal of this thesis was to learn and utilize C++ as a programming tool in the demonstration of B-spline techniques. C++ was chosen because it is object-oriented, and because it is the chosen language of the Microsoft Windows PC platform. Many languages are object-oriented, but C++ was chosen to make use of its libraries to build standard Windows interfaces and objects. The third piece of this thesis is an effort to explore the fundamentals of inter-language communications. Many old scientific codes are already written in older languages like FORTRAN, so it is advantageous to re-use those codes where possible. Digital Visual FORTRAN, a module of the Microsoft Visual Studio, has provided a powerful tool in their integration of multiple programming languages for Windows applications. Using Visual Studio, it is possible to re-use existing FORTRAN code and envelop it in a C interface using a dynamic link library (DLL) file. This thesis uses a C++ application for defining any typical airfoil using B-splines. The software package calls XFOIL, a code written in FORTRAN to evaluate the aerodynamic characteristics of those airfoils. Further, those characteristics have been compared to those of the original geometry to evaluate the interpolation process used by the splines

Surface fitting three-dimensional bodies

The geometry of general three-dimensional bodies is generated from coordinates of points in several cross sections. Since these points may not be smooth, they are divided into segments and general conic sections are curve fit in a least-squares sense to each segment of a cross section. The conic sections are then blended in the longitudinal direction by fitting parametric cubic-spline curves through coordinate points which define the conic sections in the cross-sectional planes. Both the cross-sectional and longitudinal curves may be modified by specifying particular segments as straight lines and slopes at selected points. Slopes may be continuous or discontinuous and finite or infinite. After a satisfactory surface fit has been obtained, cards may be punched with the data necessary to form a geometry subroutine package for use in other computer programs. At any position on the body, coordinates, slopes and second partial derivatives are calculated. The method is applied to a blunted 70 deg delta wing, and it was found to generate the geometry very well

The Multilevel Structures of NURBs and NURBlets on Intervals

This dissertation is concerned with the problem of constructing biorthogonal wavelets based on non-uniform rational cubic B-Splines on intervals. We call non-uniform rational B-Splines ``NURBs , and such biorthogonal wavelets ``NURBlets . Constructing NURBlets is useful in designing and representing an arbitrary shape of an object in the industry, especially when exactness of the shape is critical such as the shape of an aircraft. As we know presently most popular wavelet models in the industry are approximated at boundaries. In this dissertation a new model is presented that is well suited for generating arbitrary shapes in the industry with mathematical exactness throughout intervals; it fulfills interpolation at boundaries as well

Implementation of an hybrid machine learning methodology for pharmacological modeling

Tese de mestrado, Bioinformática e Biologia Computacional (Bioinformática) Universidade de Lisboa, Faculdade de Ciências, 2017Hoje em dia, especialmente na area biomedica, os dados contem milhares de variaveis de fontes diferentes e com apenas algumas instancias ao mesmo tempo. Devido a este facto, as abordagens da aprendizagem automatica enfrentam dois problemas, nomeadamente a questao da integracao de dados heterogeneos e a selecao das caracteristicas. Este trabalho propoe uma solucao eficiente para esta questao e proporciona uma implementacao funcional da metodologia hibrida. A inspiracao para este trabalho veio do desafio proposto no ambito da competicao AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge em 2016, e da solucao vencedora desenvolvida por Yuanfang Guan. Relativamente a motivacao do concurso, e observado que os tratamentos combinatorios para o cancro sao mais eficientes do que as terapias habituais de agente unico, desde que tem potencial para superar as desvantagens dos outros (limitado espetro de acao e desenvolvimento de resistencia). No entanto, o efeito combinatorio de drogas nao e obvio, produzindo possivelmente o resultado aditivo, sinergico ou antagonico. Assim, o objetivo da competicao era prever in vitro a sinergia dos compostos, sem ter acesso aos dados experimentais da terapia combinatoria. No ambito da competicao foram fornecidos ficheiros de varias fontes, contendo o conhecimento farmacologico tanto experimental como obtido de ajustamento das equacoes, a informacao sobre propriedades quimicas e estruturais de drogas, e por fim, os perfis moleculares de celulas, incluindo expressao de RNA, copy variants, sequencia e metilacao de DNA. O trabalho referido envolveu uma abordagem muito bem sucedida de integração dos dados heterogeneos, estendendo o modelo com conhecimento disponivel dentro do projeto The Cancer Cell Line Encyclopedia, e tambem introduzindo o passo decisivo de simulacao que permite imitar o efeito de terapia combinatoria no cancro. Apesar das descricoes pouco claras e da documentacao da solucao vencedora ineficiente, a reproducao da abordagem de Guan foi concluida, tentando ser o mais fiel possivel. A implementacao funcional foi escrita nas linguagens R e Python, e o seu desempenho foi verificado usando como referencia a matriz submetida no concurso. Para melhorar a metodologia, o workflow de selecao dos caracteristicas foi estabelecido e executado usando o algoritmo Lasso. Alem disso, o desempenho de dois metodos alternativos de modelacao foi experimentado, incluindo Support Vector Machine and Multivariate Adaptive Regression Splines (MARS). Varias versoes da equacao de integracao foram consideradas permitindo a determinacao de coeficientes aparentemente otimos. Como resultado, a compreensao da melhor solucao de competição foi desenvolvida e a implementacao funcional foi construida com sucesso. As melhorias foram propostas e no efeito o algoritmo SVM foi verificado como capaz de superar os outros na resolução deste problema, a equacao de integracao com melhor desempenho foi estabelecida e finalmente a lista de 75 variaveis moleculares mais informativas foi fornecida. Entre estes genes, poderiam ser encontrados possiveis candidatos de biomarcadores de cancro.Nowadays, especially in the biomedical field, the data sets usually contain thousands of multi-source variables and with only few instances in the same time. Due to this fact, Machine Learning approaches face two problems, namely the issue of heterogenous data integration and the feature selection. This work proposes an efficient solution for this question and provides a functional implementation of the hybrid methodology. The inspiration originated from the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge from 2016 and the winning solution by Yuanfang Guan. Regarding to the motivation of competition, the combinatory cancer treatments are believed to be more effective than standard single-agent therapies since they have a potential to overcome others weaknesses (narrow spectrum of action and development of the resistance). However, the combinatorial drug effect is not obvious bringing possibly additive, synergistic or antagonistic treatment result. Thus, the goal of the competition was to predict in vitro compound synergy, without the access to the experimental combinatory therapy data. Within the competition, the multi-source files were supplied, encompassing the pharmacological knowledge from experiments and equation-fitting, the information on chemical properties and structure of drugs, finally the molecular cell profiles including RNA expression, copy variants, DNA sequence and methylation. The referred work included very successful approach of heterogenous data integration, extending additionally the model with prior knowledge outsourced from The Cancer Cell Line Encyclopedia, as well as introduced a key step of simulation that allows to imitate effect of a combinatory therapy on cancer. Despite unexplicit descriptions and poor documentation of the winning solution, as accurate as possible, reproduction of Guan’s approach was accomplished. The functional implementation was written in R and Python languages, and its performance was verified using as a reference the submitted in challenge prediction matrix. In order to improve the methodology feature selection workflow was established and run using a Lasso algorithm. Moreover, the performance of two alternative modeling methods was experimented including Support Vector Machine and Multivariate Adaptive Regression Splines (MARS). Several versions of merging equation were considered allowing determination of apparently optimal coefficients. As the result, the understanding of the best challenge solution was developed and the functional implementation was successfully constructed. The improvements were proposed and in the effect the SVM algorithm was verified to surpass others in solving this problem, the best-performing merging equation was established, and finally the list of 75 most informative molecular variables was provided. Among those genes, potential cancer biomarker candidates could be found