Portfolio optimization with options

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Grout optimization for masonry consolidation

Tese apresentada para obtenção do Grau de Doutor em Engenharia Civil na especialidade de Reabilitação do Património Edificado, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Business model optimization of Prego Gourmet

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

An RF LC Q-enhanced CMOS iter using integrated inductors with layout optimization

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Exopolysaccharide production by Enterobacter A47: optimization of cultivation conditions and study of polymer functional properties

Dissertação para obtenção do Grau de Mestre em Biotecnologia

Optimization based design of LC voltage controlled oscilators

Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores

Optimization of ring oscillators

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

The optimization of the complaints management system

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Optimization of investment promotion tools for Portugal: Specific recommendations to attract investments from Germany

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Optimization of randomly amplified polymorphic DNA-polymerase chain reaction for molecular typing of Salmonella enterica serovar Typhi

Optimization of the RAPD reaction for characterizing Salmonella enterica serovar Typhi strains was studied in order to ensure the reproducibility and the discriminatory power of this technique. Eight Salmonella serovar Typhi strains isolated from various regions in Brazil were examined for the fragment patterns produced using different concentrations of DNA template, primer, MgCl2 and Taq DNA polymerase. Using two different low stringency thermal cycle profiles, the RAPD fingerprints obtained were compared. A set of sixteen primers was evaluated for their ability to produce a high number of distinct fragments. We found that variations associated to all of the tested parameters modified the fingerprinting patterns. For the strains of Salmonella enterica serovar Typhi used in this experiment, we have defined a set of conditions for RAPD-PCR reaction, which result in a simple, fast and reproducible typing method.

Evaluation and optimization of a session-based middleware for data management

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Constraint-based modelling of mixed microbial populations: Application to polyhydroxyalkanoates production

Dissertação para obtenção do Grau de Doutor em Engenharia Química e Bioquímica

Optimization of breast tomosynthesis image reconstruction using parallel computing

Breast cancer is the most common cancer among women, being a major public health problem. Worldwide, X-ray mammography is the current gold-standard for medical imaging of breast cancer. However, it has associated some well-known limitations. The false-negative rates, up to 66% in symptomatic women, and the false-positive rates, up to 60%, are a continued source of concern and debate. These drawbacks prompt the development of other imaging techniques for breast cancer detection, in which Digital Breast Tomosynthesis (DBT) is included. DBT is a 3D radiographic technique that reduces the obscuring effect of tissue overlap and appears to address both issues of false-negative and false-positive rates. The 3D images in DBT are only achieved through image reconstruction methods. These methods play an important role in a clinical setting since there is a need to implement a reconstruction process that is both accurate and fast. This dissertation deals with the optimization of iterative algorithms, with parallel computing through an implementation on Graphics Processing Units (GPUs) to make the 3D reconstruction faster using Compute Unified Device Architecture (CUDA). Iterative algorithms have shown to produce the highest quality DBT images, but since they are computationally intensive, their clinical use is currently rejected. These algorithms have the potential to reduce patient dose in DBT scans. A method of integrating CUDA in Interactive Data Language (IDL) is proposed in order to accelerate the DBT image reconstructions. This method has never been attempted before for DBT. In this work the system matrix calculation, the most computationally expensive part of iterative algorithms, is accelerated. A speedup of 1.6 is achieved proving the fact that GPUs can accelerate the IDL implementation.

Probabilistic constraint reasoning with Monte Carlo integration to robot localization

This work studies the combination of safe and probabilistic reasoning through the hybridization of Monte Carlo integration techniques with continuous constraint programming. In continuous constraint programming there are variables ranging over continuous domains (represented as intervals) together with constraints over them (relations between variables) and the goal is to find values for those variables that satisfy all the constraints (consistent scenarios). Constraint programming “branch-and-prune” algorithms produce safe enclosures of all consistent scenarios. Special proposed algorithms for probabilistic constraint reasoning compute the probability of sets of consistent scenarios which imply the calculation of an integral over these sets (quadrature). In this work we propose to extend the “branch-and-prune” algorithms with Monte Carlo integration techniques to compute such probabilities. This approach can be useful in robotics for localization problems. Traditional approaches are based on probabilistic techniques that search the most likely scenario, which may not satisfy the model constraints. We show how to apply our approach in order to cope with this problem and provide functionality in real time.

Efficiency optimization of the transportation supply chain activity: the maritime option for Volkswagen Autoeuropa

Field Lab of Entrepreneurial Innovative Ventures