Development of mixed matrix membranes with metal - organic framework and ionic liquids for biogas upgrading

Dissertation presented to Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa for obtaining the master degree in Membrane Engineering

The additive effects in matrix dispersed liquid crystals

Dissertation presented at Faculdade de Ciências e Tecnologia from Universidade Nova de Lisboa to obtain the degree of Master in Chemical and Biochemical Engineering

Coherent structures in open channel flows with bed load transport over an hydraulically rough bed

Dissertação para obtenção do Grau de Mestre em Engenharia Civil – Perfil de Estruturas

Implementing the SC-FDMA transmission technique using the GNURadio platform

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

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.

State detection in a financial portfolio: a self-organizing maps approach for financial time series

This study analyses financial data using the result characterization of a self-organized neural network model. The goal was prototyping a tool that may help an economist or a market analyst to analyse stock market series. To reach this goal, the tool shows economic dependencies and statistics measures over stock market series. The neural network SOM (self-organizing maps) model was used to ex-tract behavioural patterns of the data analysed. Based on this model, it was de-veloped an application to analyse financial data. This application uses a portfo-lio of correlated markets or inverse-correlated markets as input. After the anal-ysis with SOM, the result is represented by micro clusters that are organized by its behaviour tendency. During the study appeared the need of a better analysis for SOM algo-rithm results. This problem was solved with a cluster solution technique, which groups the micro clusters from SOM U-Matrix analyses. The study showed that the correlation and inverse-correlation markets projects multiple clusters of data. These clusters represent multiple trend states that may be useful for technical professionals.

Cellulose nanorods in liquid crystalline elastomers for improved actuators

Nanotechnology plays a central role in ‘tailoring’ materials’ properties and thus improving its performances for a wide range of applications. Coupling nature nano-objects with nanotechnology results in materials with enhanced functionalities. The main objective of this master thesis was the synthesis of nanocrystalline cellulose (NCCs) and its further incorporation in a cellulosic matrix, in order to produce a stimuli-responsive material to moisture. The induced behaviour (bending/unbending) of the samples was deeply investigated, in order to determine relationships between structure/properties. Using microcrystalline cellulose as a starting material, acid hydrolysis was performed and the NCC was obtained. Anisotropic aqueous solutions of HPC and NCC were prepared and films with thicknesses ranging from 22μm to 61μm were achieved, by using a shear casting technique. Microscopic and spectroscopic techniques as well as mechanical and rheological essays were used to characterize the transparent and flexible films produced. Upon the application of a stimulus (moisture), the bending/unbending response times were measured. The use of NCC allowed obtaining films with response times in the order of 6 seconds for the bending and 5 seconds for the unbending, improving the results previously reported. These promising results open new horizons for building up improved soft steam engines.

A built-in self-test technique for high speed analog-to-digital converters

Fundação para a Ciência e a Tecnologia (FCT) - PhD grant (SFRH/BD/62568/2009)