Estimating Local Part Thickness in Midplane Meshes for Finite Element Analysis

Within the development of motor vehicles, crash safety (e.g. occupant protection, pedestrian protection, low speed damageability), is one of the most important attributes. In order to be able to fulfill the increased requirements in the framework of shorter cycle times and rising pressure to reduce costs, car manufacturers keep intensifying the use of virtual development tools such as those in the domain of Computer Aided Engineering (CAE). For crash simulations, the explicit finite element method (FEM) is applied. The accuracy of the simulation process is highly dependent on the accuracy of the simulation model, including the midplane mesh. One of the roughest approximations typically made is the actual part thickness which, in reality, can vary locally. However, almost always a constant thickness value is defined throughout the entire part due to complexity reasons. On the other hand, for precise fracture analysis within FEM, the correct thickness consideration is one key enabler. Thus, availability of per element thickness information, which does not exist explicitly in the FEM model, can significantly contribute to an improved crash simulation quality, especially regarding fracture prediction. Even though the thickness is not explicitly available from the FEM model, it can be inferred from the original CAD geometric model through geometric calculations. This paper proposes and compares two thickness estimation algorithms based on ray tracing and nearest neighbour 3D range searches. A systematic quantitative analysis of the accuracy of both algorithms is presented, as well as a thorough identification of particular geometric arrangements under which their accuracy can be compared. These results enable the identification of each technique’s weaknesses and hint towards a new, integrated, approach to the problem that linearly combines the estimates produced by each algorithm.

Criminal Liability of Organizations, Corporations, Legal Persons, and Similar Entities on Law of Portuguese Cybercrime: A Brief Discussion on the Issue of Crimes of “False Information,” the “Damage on Other Programs or Computer Data,” the “Computer-Software Sabotage,” the “Illegitimate Access,” the “Unlawful Interception,” and “Illegitimate Reproduction of the Protected Program”

Abstract: in Portugal, and in much of the legal systems of Europe, «legal persons» are likely to be criminally responsibilities also for cybercrimes. Like for example the following crimes: «false information»; «damage on other programs or computer data»; «computer-software sabotage»; «illegitimate access»; «unlawful interception» and «illegitimate reproduction of protected program». However, in Portugal, have many exceptions. Exceptions to the «question of criminal liability» of «legal persons». Some «legal persons» can not be blamed for cybercrime. The legislature did not leave! These «legal persons» are v.g. the following («public entities»): legal persons under public law, which include the public business entities; entities utilities, regardless of ownership; or other legal persons exercising public powers. In other words, and again as an example, a Portuguese public university or a private concessionaire of a public service in Portugal, can not commit (in Portugal) any one of cybercrime pointed. Fair? Unfair. All laws should provide that all legal persons can commit cybercrimes. PS: resumo do artigo em inglês.

A Methodology for GUI Layer Redefinition Through Virtualization and Computer Vision

Nowadays despite improvements in usability and intuitiveness users have to adapt to the proposed systems to satisfy their needs. For instance, they must learn how to achieve tasks, how to interact with the system, and fulfill system's specifications. This paper proposes an approach to improve this situation enabling graphical user interface redefinition through virtualization and computer vision with the aim of increasing the system's usability. To achieve this goal the approach is based on enriched task models, virtualization and picture-driven computing.

Computer-aided recognition of dental implants in X-ray images

Dental implant recognition in patients without available records is a time-consuming and not straightforward task. The traditional method is a complete user-dependent process, where the expert compares a 2D X-ray image of the dental implant with a generic database. Due to the high number of implants available and the similarity between them, automatic/semi-automatic frameworks to aide implant model detection are essential. In this study, a novel computer-aided framework for dental implant recognition is suggested. The proposed method relies on image processing concepts, namely: (i) a segmentation strategy for semi-automatic implant delineation; and (ii) a machine learning approach for implant model recognition. Although the segmentation technique is the main focus of the current study, preliminary details of the machine learning approach are also reported. Two different scenarios are used to validate the framework: (1) comparison of the semi-automatic contours against implant’s manual contours of 125 X-ray images; and (2) classification of 11 known implants using a large reference database of 601 implants. Regarding experiment 1, 0.97±0.01, 2.24±0.85 pixels and 11.12±6 pixels of dice metric, mean absolute distance and Hausdorff distance were obtained, respectively. In experiment 2, 91% of the implants were successfully recognized while reducing the reference database to 5% of its original size. Overall, the segmentation technique achieved accurate implant contours. Although the preliminary classification results prove the concept of the current work, more features and an extended database should be used in a future work.

SAR calculation using FDTD simulations

The main intend of this work, is to determinate the Specific Absorption Rate (SAR) on human head tissues exposed to radiation caused by sources of 900 and 1800MHz, since those are the typical frequencies for mobile communications systems nowadays. In order to determinate the SAR, has been used the FDTD (Finite Difference Time Domain), which is a numeric method in time domain, obtained from the Maxwell equations in differential mode. In order to do this, a computational model from the human head in two dimensions made with cells of the smallest possible size was implemented, respecting the limits from computational processing. It was possible to verify the very good efficiency of the FDTD method in the resolution of those types of problems.

The folding of knotted proteins: insights from lattice simulations

We carry out systematic Monte Carlo simulations of Go lattice proteins to investigate and compare the folding processes of two model proteins whose native structures differ from each other due to the presence of a trefoil knot located near the terminus of one of the protein chains. We show that the folding time of the knotted fold is larger than that of the unknotted protein and that this difference in folding time is particularly striking in the temperature region below the optimal folding temperature. Both proteins display similar folding transition temperatures, which is indicative of similar thermal stabilities. By using the folding probability reaction coordinate as an estimator of folding progression we have found out that the formation of the knot is mainly a late folding event in our shallow knot system.

Evaluation of the respiratory motion effect in small animal PET images with GATE Monte Carlo simulations

The rapid growth in genetics and molecular biology combined with the development of techniques for genetically engineering small animals has led to increased interest in in vivo small animal imaging. Small animal imaging has been applied frequently to the imaging of small animals (mice and rats), which are ubiquitous in modeling human diseases and testing treatments. The use of PET in small animals allows the use of subjects as their own control, reducing the interanimal variability. This allows performing longitudinal studies on the same animal and improves the accuracy of biological models. However, small animal PET still suffers from several limitations. The amounts of radiotracers needed, limited scanner sensitivity, image resolution and image quantification issues, all could clearly benefit from additional research. Because nuclear medicine imaging deals with radioactive decay, the emission of radiation energy through photons and particles alongside with the detection of these quanta and particles in different materials make Monte Carlo method an important simulation tool in both nuclear medicine research and clinical practice. In order to optimize the quantitative use of PET in clinical practice, data- and image-processing methods are also a field of intense interest and development. The evaluation of such methods often relies on the use of simulated data and images since these offer control of the ground truth. Monte Carlo simulations are widely used for PET simulation since they take into account all the random processes involved in PET imaging, from the emission of the positron to the detection of the photons by the detectors. Simulation techniques have become an importance and indispensable complement to a wide range of problems that could not be addressed by experimental or analytical approaches.

Avaliação da dose absorvida na tiróide, em cintigrafias com 123I e Na99mTcO4 utilizando modelos biocinéticos e métodos de Monte Carlo

Mestrado em Radiações Aplicadas às Tecnologias da Saúde. Área de especialização: Protecção contra Radiações

QoS em WIMAX – Análise e avaliação de módulos para simulação com o ns-2

Mestrado em Engenharia Electrotécnica e de Computadores

Sistema de correlação de eventos e notificações - SCEN

Mestrado em Engenharia Electrotécnica e de Computadores

An ultrasound based computer-aided diagnosis tool for steatosis detection

Liver steatosis is a common disease usually associated with social and genetic factors. Early detection and quantification is important since it can evolve to cirrhosis. In this paper, a new computer-aided diagnosis (CAD) system for steatosis classification, in a local and global basis, is presented. Bayes factor is computed from objective ultrasound textural features extracted from the liver parenchyma. The goal is to develop a CAD screening tool, to help in the steatosis detection. Results showed an accuracy of 93.33%, with a sensitivity of 94.59% and specificity of 92.11%, using the Bayes classifier. The proposed CAD system is a suitable graphical display for steatosis classification.

Geração de simuladores de filas para diferentes contextos com estudo de casos para redes de computadores

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Avaliação da qualidade do ar interior e estudos CFD em espaços do ISEP

Mestrado em Engenharia Química. Ramo Tecnologias de Protecção Ambiental.