Thesis (MScEng)--Stellenbosch University 2012 / ENGLISH ABSTRACT: Reverse engineering (RE) has emerged as an important tool in the design stages of a product. The demand for better performance of hardware and software has spawned many different technologies that fall under RE. The diversity of technologies is linked to the different application areas of industry. It is critical to understand what the exact capability of each individual technology is, in order to choose the appropriate RE system. The objective of this study is to develop capability profiles of different RE technologies available, such as: Coordinate Measuring Machine, Articulated Arm (Cimcore), Non-contact scanner (GOM), and contact scanner (Renishaw). To achieve the objective, the different characteristics of each technology are measured and quantified. A capability profile can be regarded as defined criteria that represent the performance of a RE technology and in this study, is defined by quantifying the following characteristics:  Accuracy  Repeatability  Speed of Measurement  Work Envelope  User-friendliness. The significance of developing these capability profiles is so that they may be compared to one another. This is important, especially for the accuracy criterion, as each technology is manufactured by a different company, making an acceptable accuracy comparison amongst the different technologies impossible. The study also suggests an evaluation tool which will help a decision maker choose the appropriate technology for a specified objective. Guidelines are also given to potential end users of RE technologies on how they should go about acquiring the right system. On a more general level, the study contributes to research in recent trends, in the RE industry in terms of application, hardware, software and the selection of RE systems. By developing these capability profiles, the study aims to contribute to its value in academia, as well as for industry partners and potential RE end-users. / AFRIKAANSE OPSOMMING: RE het ontwikkel as n belangrike hulpmiddel in die ontwerp fase van ‘n produk. Die aanvraag na beter prestasie van hardeware en sagteware het gelei tot die ontwikkeling van baie verskillende tegnologie. Die diversiteit van die tegnologie gaan hand aan hand met die verskillende toepassings areas in die industrie. Dit is belangrik om die spesifieke eienskappe van elke individuele tegnologie te verstaan om die regte RE sisteem te kies. The doel van hierdie studie is om die vermoë profiele van die verskillende beskikbare RE tegnologie te ontwerp: Coordinate Measuring Machine, Articulated Arm (Cimcore), nie-kontak skandeerder (GOM) en kontak skandeerder (Renishaw). Die verskillende eienskappe van elke tegnologie word gemeet en gekwantifiseer. ‘n Vermoë profiel kan gesien word as ‘n vaste kriteria wat die prestasie van die RE tegnologie verteenwoordig en in hierdie studie word dit gedefinieer deur die volgende eienskappe:  Akkuraatheid  Herhaalbaarheid  Spoed van meeting  Meet volume  Gebruikers-vriendelikheid Die relevansie van die ontwikkeling van hierdie vermoë profiele is dat dit wedersyds vergelyk kan word. Dit is belangrik, spesifiek vir die akkuraatheids kriteria, omdat elke tegnologie by ‘n verskillende maatskappy vervaardig word. ‘n Aanvaarbare akkuraatsheid vergelyking onder die verskillende tegnologie is dus onmoontlik. Die studie stel ook ‘n evaluasie hulpmiddel voor wat die besluitnemer sal lei om die mees toepaslike tegnologie te kies vir die spesiefieke doeleindes. Verder word riglyne aan die potensiele gebruikers van RE tegnologie gegee oor hoe om te werk te gaan om die regte sisteem te kies indien die tegnologie nie in besit is nie. Op ‘n meer algemene vlak dra die studie tot navorsing by deur die nuutste tendense in toepassing, hardeware, sagteware en die keuse van RE sisteme in die RE industrie te beskryf. Deur hierdie vermoë profile te ontwikkel beoog die studie om waarde toe te voeg aan die akademie, vennote in die industrie en potensiele RE gebruikers.

Au cours de la dernière décennie, l'utilisation croissante de nombreuses nouvelles technologies comme des systèmes de livraison contrôlée a incité un développement empirique coûteux de systèmes nouveaux et efficaces. Pour faciliter une conception plus raisonnable et une optimisation, faisant face à l'ensemble de possibilités existantes, n'importe quelle solution qui pourrait semi-automatiser le développement de produits apporteraient l'aide précieuse aux utilisateurs (des scientifiques de formulation et des éducateurs). Ceci faciliterait l'importance essentielle de choisir les matériels justes pour l'application correcte. Dans cette thèse, un projet à long terme concernant une génie inverse est proposé, commençant d'une propriété d'utilisation finale (libération contrôlée), la cible mondiale est de développer une méthodologie de conception de produit qui nous permet de déterminer les caractéristiques optimales d'une formulation à préparer: les phases, la composition, le type d'interface, la taille et la distribution d'objets actuels, l'équilibre de phase, la diffusion dans des phases et le caractère évolutionnaire du matériel. Dans la considération d'un exemple de commodité de système structuré et dispersé: des émulsions fortement concentrées, le problème de design a été décomposé dans un ordre hiérachique de sous-problèmes ou des boîtes, combinant les modèles constitutifs qui évaluent le transport de masse de principe actif comme une fonction de paramètres de formulation et des techniques assistées par ordinateur comme la modélisation moléculaire pour le volume/surface de molécules, ou des modèles d'UNIFAC pour des prédictions d'équilibre aussi bien que pour des évaluations de viscosités de mélange. Un ultérieur design de factoriel d'expériences virtuelles a permis d'obtenir une description quantitative de la sortie selon les paramètres modèles et une analyse composante principale a évalué l'importance des variables. En utilisant une cartographie basée sur trois tensio-actifs (pour SPAN 80, PGPR et BRIJ 93), quatre huiles (dodecane, hexadecane, isopropyl myristate et isopropyl palmitate) et l'acide mandelic comme le principe actif, le modèle ab initio physicochimique a été expérimentalement validé. Les résultats montrent que le modèle mécaniste systématiquement prévoit la diffusion du principe actif d'émulsions à un moyen récepteur dans des parfaites 'sink' conditions. Cette approche de génie inverse a montré pour être d'intérêt très élevé dans le domaine de formulation en permettant des études préliminaires examinantes rapides et robustes sur une large gamme de composants aussi bien que des outils de prédiction précis et rigoureux optimiser la sortie contrôlée d'un système identifié. Il est souhaitable de mettre en œuvre ces extensions à d'autre systèmes semblables / In the past decade, the growing use of numerous novel technologies as controlled-delivery systems has prompted a costly trial-and-error development of new and effective systems. In order to facilitate a more rational design and optimization, facing the set of existing possibilities, any solution that could semiautomate the product development would bring precious help to the users (formulation scientists and educators). This would facilitate the essential importance of choosing the right materials for the correct application. In this thesis, a long-term project concerning a reverse engineering is proposed, starting from a final usage property (controlled release), the global target is to develop a product design methodology which allows us to determine the optimal features of a formulation to prepare: phases in presence, composition, interface type, size and distribution of current objects, phase equilibrium, diffusion within phases and evolutionary character of the material. Considering a convenience example of structured-dispersed system: highly concentrated emulsions, the design problem has been decomposed into a hierarchical sequence of subproblems or boxes, combining constitutive models that estimate the active ingredient mass transport as a function of formulation parameters and computer-aided techniques such as molecular modeling for volume/area of molecules, or UNIFAC models for equilibria predictions as well as for mixture viscosities estimations. A subsequent full factorial design of virtual experiments has allowed to obtain a quantitative description of the release depending on the model parameters, and a principal component analysis has assessed the importance of the variables. Using a cartography focused on three surfactants (SPAN 80, PGPR and BRIJ 93), four oils (dodecane, hexadecane, isopropyl myristate and isopropyl palmitate) and mandelic acid as an active ingredient, the ab-initio physicochemical model has been experimentally validated. Results show that the mechanistic model consistently predicts the diffusion of the active ingredient from emulsions to a release medium in perfect sink conditions. This reverse engineering approach is showing to be of very high interest in the domain of formulation by allowing fast and robust screening preliminary studies on a broad range of components as well as precise and rigorous prediction tools to optimize controlled release from an identified system. It is fully recommended to implement its extensions to other similar disperse systems

Orientador: Ruis Camargo Tokimatsu / Resumo: Na área da ortopedia a busca por novos avanços tecnológicos tem sido muito pouco e as pessoas que necessitam de dispositivos como órteses acabam sofrendo mais devido ao alto custo dos aparelhos ortopédicos. A maioria da população que possui patologias sofre por não ter renda suficiente para adquirir esses dispositivos e acabam agravando essas patologias. Este estudo busca inovar e propor a utilização de novas tecnologias para desenvolver órteses a essa população de baixa renda. Uma tecnologia de custo baixo e que possa ser implementada na rede pública. Através das análises feitas pelos profissionais da área como ortopedistas e terapeutas nas imagens bidimensionais dos exames do paciente consultados, com a utilização de aparelhos com a técnica de Engenharia Reversa será possível digitalizar o membro afetado ou posicioná-los de forma adequada para que seja tirado o molde da órtese personalizada. Esses aparelhos permitem utilizar dados de exames já feitos como Ultrassom, Ressonância Magnética, Tomografia ou Raio X e convertê-los em modelos tridimensionais. O objetivo deste projeto é utilizar essas técnicas de Engenharia Reversa para digitalizar o membro que necessite de auxílio e confeccionar as órteses com a tecnologia de Manufatura Aditiva, uma tecnologia que vem se desenvolvendo rapidamente nesses últimos anos e permitindo fabricar diretamente qualquer peça ou objeto através de um arquivo tridimensional modelado. Nos dias atuais, a Manufatura Aditiva tem sido utilizada em div... (Resumo completo, clicar acesso eletrônico abaixo) / Mestre

Nesta tese estamos especificamente interessados no problema de engenharia re- versa de redes regulatórias genéticas a partir de dados de pós-genômicos, um grande desafio na área de Bioinformática. Redes regulatórias genéticas são complexos cir- cuitos biológicos responsáveis pela regulação do nível de expressão dos genes, desem- penhando assim um papel fundamental no controle de inúmeros processos celulares, incluindo diferenciação celular, ciclo celular e metabolismo. Decifrar a estrutura destas redes é crucial para possibilitar uma maior compreensão à nível de sistema do desenvolvimento e comportamento dos organismos, e eventualmente esclarecer os mecanismos de doenças causados pela desregulação dos processos acima mencio- nados. Devido ao expressivo aumento da disponibilidade de dados experimentais de larga escala e da grande dimensão e complexidade dos sistemas biológicos, métodos computacionais têm sido ferramentas essenciais para viabilizar esta investigação. No entanto, seu desempenho ainda é bastante deteriorado por importantes desafios com- putacionais e biológicos impostos pelo cenário. Em particular, o ruído e esparsidade inerentes aos dados biológicos torna este problema de inferência de redes um difícil problema de otimização combinatória, para o qual métodos computacionais dispo- níveis falham em relação à exatidão e robustez das predições. Esta tese tem como objetivo investigar o uso de técnicas de ensemble learning como forma de superar as limitações existentes e otimizar o processo de inferência, explorando a diversidade entre um conjunto de modelos. Com este intuito, desenvolvemos métodos computa- cionais tanto para gerar redes diversificadas, como para combinar estas predições em uma solução única (solução ensemble ), e aplicamos esta abordagem a uma série de cenários com diferentes fontes de diversidade a fim de compreender o seu potencial neste contexto específico. Mostramos que as soluções propostas são competitivas com algoritmos tradicionais deste campo de pesquisa e que melhoram nossa capa- cidade de reconstruir com precisão as redes regulatórias genéticas. Os resultados obtidos para a inferência de redes de regulação transcricional e pós-transcricional, duas camadas adjacentes e complementares que compõem a rede de regulação glo- bal, tornam evidente a eficiência e robustez da nossa abordagem, encorajando a consolidação de ensemble learning como uma metodologia promissora para decifrar a estrutura de redes regulatórias genéticas. / In this thesis we are concerned about the reverse engineering of gene regulatory networks from post-genomic data, a major challenge in Bioinformatics research. Gene regulatory networks are intricate biological circuits responsible for govern- ing the expression levels (activity) of genes, thereby playing an important role in the control of many cellular processes, including cell differentiation, cell cycle and metabolism. Unveiling the structure of these networks is crucial to gain a systems- level understanding of organisms development and behavior, and eventually shed light on the mechanisms of diseases caused by the deregulation of these cellular pro- cesses. Due to the increasing availability of high-throughput experimental data and the large dimension and complexity of biological systems, computational methods have been essential tools in enabling this investigation. Nonetheless, their perfor- mance is much deteriorated by important computational and biological challenges posed by the scenario. In particular, the noisy and sparse features of biological data turn the network inference into a challenging combinatorial optimization prob- lem, to which current methods fail in respect to the accuracy and robustness of predictions. This thesis aims at investigating the use of ensemble learning tech- niques as means to overcome current limitations and enhance the inference process by exploiting the diversity among multiple inferred models. To this end, we develop computational methods both to generate diverse network predictions and to combine multiple predictions into an ensemble solution, and apply this approach to a number of scenarios with different sources of diversity in order to understand its potential in this specific context. We show that the proposed solutions are competitive with tra- ditional algorithms in the field and improve our capacity to accurately reconstruct gene regulatory networks. Results obtained for the inference of transcriptional and post-transcriptional regulatory networks, two adjacent and complementary layers of the overall gene regulatory network, evidence the efficiency and robustness of our approach, encouraging the consolidation of ensemble systems as a promising methodology to decipher the structure of gene regulatory networks.

Nesta tese estamos especificamente interessados no problema de engenharia re- versa de redes regulatórias genéticas a partir de dados de pós-genômicos, um grande desafio na área de Bioinformática. Redes regulatórias genéticas são complexos cir- cuitos biológicos responsáveis pela regulação do nível de expressão dos genes, desem- penhando assim um papel fundamental no controle de inúmeros processos celulares, incluindo diferenciação celular, ciclo celular e metabolismo. Decifrar a estrutura destas redes é crucial para possibilitar uma maior compreensão à nível de sistema do desenvolvimento e comportamento dos organismos, e eventualmente esclarecer os mecanismos de doenças causados pela desregulação dos processos acima mencio- nados. Devido ao expressivo aumento da disponibilidade de dados experimentais de larga escala e da grande dimensão e complexidade dos sistemas biológicos, métodos computacionais têm sido ferramentas essenciais para viabilizar esta investigação. No entanto, seu desempenho ainda é bastante deteriorado por importantes desafios com- putacionais e biológicos impostos pelo cenário. Em particular, o ruído e esparsidade inerentes aos dados biológicos torna este problema de inferência de redes um difícil problema de otimização combinatória, para o qual métodos computacionais dispo- níveis falham em relação à exatidão e robustez das predições. Esta tese tem como objetivo investigar o uso de técnicas de ensemble learning como forma de superar as limitações existentes e otimizar o processo de inferência, explorando a diversidade entre um conjunto de modelos. Com este intuito, desenvolvemos métodos computa- cionais tanto para gerar redes diversificadas, como para combinar estas predições em uma solução única (solução ensemble ), e aplicamos esta abordagem a uma série de cenários com diferentes fontes de diversidade a fim de compreender o seu potencial neste contexto específico. Mostramos que as soluções propostas são competitivas com algoritmos tradicionais deste campo de pesquisa e que melhoram nossa capa- cidade de reconstruir com precisão as redes regulatórias genéticas. Os resultados obtidos para a inferência de redes de regulação transcricional e pós-transcricional, duas camadas adjacentes e complementares que compõem a rede de regulação glo- bal, tornam evidente a eficiência e robustez da nossa abordagem, encorajando a consolidação de ensemble learning como uma metodologia promissora para decifrar a estrutura de redes regulatórias genéticas. / In this thesis we are concerned about the reverse engineering of gene regulatory networks from post-genomic data, a major challenge in Bioinformatics research. Gene regulatory networks are intricate biological circuits responsible for govern- ing the expression levels (activity) of genes, thereby playing an important role in the control of many cellular processes, including cell differentiation, cell cycle and metabolism. Unveiling the structure of these networks is crucial to gain a systems- level understanding of organisms development and behavior, and eventually shed light on the mechanisms of diseases caused by the deregulation of these cellular pro- cesses. Due to the increasing availability of high-throughput experimental data and the large dimension and complexity of biological systems, computational methods have been essential tools in enabling this investigation. Nonetheless, their perfor- mance is much deteriorated by important computational and biological challenges posed by the scenario. In particular, the noisy and sparse features of biological data turn the network inference into a challenging combinatorial optimization prob- lem, to which current methods fail in respect to the accuracy and robustness of predictions. This thesis aims at investigating the use of ensemble learning tech- niques as means to overcome current limitations and enhance the inference process by exploiting the diversity among multiple inferred models. To this end, we develop computational methods both to generate diverse network predictions and to combine multiple predictions into an ensemble solution, and apply this approach to a number of scenarios with different sources of diversity in order to understand its potential in this specific context. We show that the proposed solutions are competitive with tra- ditional algorithms in the field and improve our capacity to accurately reconstruct gene regulatory networks. Results obtained for the inference of transcriptional and post-transcriptional regulatory networks, two adjacent and complementary layers of the overall gene regulatory network, evidence the efficiency and robustness of our approach, encouraging the consolidation of ensemble systems as a promising methodology to decipher the structure of gene regulatory networks.

Laser scanners in combination with devices for accurate orientation like Coordinate Measuring Machines (CMM) are often used in Geometric Reverse Engineering (GRE) to measure point data. The industrial robot as a device for orientation has relatively low accuracy but the advantage of being numerically controlled, fast, flexible, rather cheap and compatible with industrial environments. It is therefore of interest to investigate if it can be used in this application. This thesis will describe a measuring system consisting of a laser profile scanner mounted on an industrial robot with a turntable. It will also give an introduction to Geometric Reverse Engineering (GRE) and describe an automatic GRE process using this measuring system. The thesis also presents a detailed accuracy analysis supported by experiments that show how 2D profile data can be used to achieve a higher accuracy than the basic accuracy of the robot. The core topic of the thesis is the investigation of a new technique for planar segmentation. The new method is implemented in the GRE system and compared with an implementation of a more traditional method. Results from practical experiments show that the new method is much faster while equally accurate or better.

Cette thèse propose une approche pratique pour la rétro-conception automatisée de protocoles de communication non-documentés. Les travaux existants dans ce domaine ne permettent qu'un apprentissage incomplet des spécifications ou exigent trop de stimulation de l'implémentation du protocol cible avec le risque d'être vaincu par des techniques de contre-inférence. Cette thèse adresse ces problématiques en s'appuyant sur la sémantique du protocole cible pour améliorer la qualité, la rapidité et la furtivité du processus d'inférence. Nous appliquons cette approche à la rétro-conception des deux principaux aspects de la définition d'un protocole à savoir l'inférence de sa syntaxe et de sa grammaire. Nous proposons un outil open-source, appelé Netzob, qui implémente nos contributions pour aider les experts en sécurité dans leur lutte contre les dernières menaces informatiques. Selons nos recherches, Netzob apparait comme l'outil publié le plus avancé pour la rétro-conception et la simulation de protocoles de communications non-documentés. / This thesis exposes a practical approach for the automatic reverse engineering of undocumented communication protocols. Current work in the field of automated protocol reverse engineering either infer incomplete protocol specifications or require too many stimulation of the targeted implementation with the risk of being defeated by counter-inference techniques. We propose to tackle these issues by leveraging the semantic of the protocol to improve the quality, the speed and the stealthiness of the inference process. This work covers the two main aspects of the protocol reverse engineering, the inference of its syntactical definition and of its grammatical definition. We propose an open-source tool, called Netzob, that implements our work to help security experts in their work against latest cyber-threats. We claim Netzob is the most advanced published tool that tackles issues related to the reverse engineering and the simulation of undocumented protocols.

Nesta tese estamos especificamente interessados no problema de engenharia re- versa de redes regulatórias genéticas a partir de dados de pós-genômicos, um grande desafio na área de Bioinformática. Redes regulatórias genéticas são complexos cir- cuitos biológicos responsáveis pela regulação do nível de expressão dos genes, desem- penhando assim um papel fundamental no controle de inúmeros processos celulares, incluindo diferenciação celular, ciclo celular e metabolismo. Decifrar a estrutura destas redes é crucial para possibilitar uma maior compreensão à nível de sistema do desenvolvimento e comportamento dos organismos, e eventualmente esclarecer os mecanismos de doenças causados pela desregulação dos processos acima mencio- nados. Devido ao expressivo aumento da disponibilidade de dados experimentais de larga escala e da grande dimensão e complexidade dos sistemas biológicos, métodos computacionais têm sido ferramentas essenciais para viabilizar esta investigação. No entanto, seu desempenho ainda é bastante deteriorado por importantes desafios com- putacionais e biológicos impostos pelo cenário. Em particular, o ruído e esparsidade inerentes aos dados biológicos torna este problema de inferência de redes um difícil problema de otimização combinatória, para o qual métodos computacionais dispo- níveis falham em relação à exatidão e robustez das predições. Esta tese tem como objetivo investigar o uso de técnicas de ensemble learning como forma de superar as limitações existentes e otimizar o processo de inferência, explorando a diversidade entre um conjunto de modelos. Com este intuito, desenvolvemos métodos computa- cionais tanto para gerar redes diversificadas, como para combinar estas predições em uma solução única (solução ensemble ), e aplicamos esta abordagem a uma série de cenários com diferentes fontes de diversidade a fim de compreender o seu potencial neste contexto específico. Mostramos que as soluções propostas são competitivas com algoritmos tradicionais deste campo de pesquisa e que melhoram nossa capa- cidade de reconstruir com precisão as redes regulatórias genéticas. Os resultados obtidos para a inferência de redes de regulação transcricional e pós-transcricional, duas camadas adjacentes e complementares que compõem a rede de regulação glo- bal, tornam evidente a eficiência e robustez da nossa abordagem, encorajando a consolidação de ensemble learning como uma metodologia promissora para decifrar a estrutura de redes regulatórias genéticas. / In this thesis we are concerned about the reverse engineering of gene regulatory networks from post-genomic data, a major challenge in Bioinformatics research. Gene regulatory networks are intricate biological circuits responsible for govern- ing the expression levels (activity) of genes, thereby playing an important role in the control of many cellular processes, including cell differentiation, cell cycle and metabolism. Unveiling the structure of these networks is crucial to gain a systems- level understanding of organisms development and behavior, and eventually shed light on the mechanisms of diseases caused by the deregulation of these cellular pro- cesses. Due to the increasing availability of high-throughput experimental data and the large dimension and complexity of biological systems, computational methods have been essential tools in enabling this investigation. Nonetheless, their perfor- mance is much deteriorated by important computational and biological challenges posed by the scenario. In particular, the noisy and sparse features of biological data turn the network inference into a challenging combinatorial optimization prob- lem, to which current methods fail in respect to the accuracy and robustness of predictions. This thesis aims at investigating the use of ensemble learning tech- niques as means to overcome current limitations and enhance the inference process by exploiting the diversity among multiple inferred models. To this end, we develop computational methods both to generate diverse network predictions and to combine multiple predictions into an ensemble solution, and apply this approach to a number of scenarios with different sources of diversity in order to understand its potential in this specific context. We show that the proposed solutions are competitive with tra- ditional algorithms in the field and improve our capacity to accurately reconstruct gene regulatory networks. Results obtained for the inference of transcriptional and post-transcriptional regulatory networks, two adjacent and complementary layers of the overall gene regulatory network, evidence the efficiency and robustness of our approach, encouraging the consolidation of ensemble systems as a promising methodology to decipher the structure of gene regulatory networks.

The automated reverse engineering of wireframes is a common task in topology optimization, fast concept design, bionic and point cloud reconstruction. This article deals with the usage of skeleton-based reconstruction of sketches in 2D images. The result leads to a flexible at least C₁ continuous shape description.

This master thesis deals with the 3D scanning. This thesis explain methods of reversal engineering with a special aim for technical disciplines. Also some examples are being given. Next part deals with the 3D scanning technology and focuses on separation of scanners to a different groups and associated technology. Basically the scanning methods are separable to destructive and non-destructive groups. An images are obtained by 3D scan, its problematics and z-axis acquisition follow in next part of the thesis. Afterwards some key parameters suitable for selection of scanning systems are selected. A scanner "David SLS2" and its basic parameters, manipulation, service and calibrating process is being described. Last part deals with function and construction of rotating table used together with the 3D scanner David SLS2.