Development and implementation of strategies for the incorporation of reinforcing elements in aluminium alloys by solid state processing

Dissertação para obtenção do Grau de Mestre em Engenharia Mecânica

Gesture based interface for image annotation

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

Parallel programming in biomedical signal processing

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Distributed processing of large remote sensing images using MapReduce - A case of Edge Detection

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Sensitivity correction of images obtained with the prototype Clear-PEM in pre-clinical environment

Dissertation presented at Faculdade de Ciências e Tecnologia Universidade Nova de Lisboa to obtain a Master Degree in Biomedical Engineering

Microscopy image segmentation by active contour models

In this thesis a semi-automated cell analysis system is described through image processing. To achieve this, an image processing algorithm was studied in order to segment cells in a semi-automatic way. The main goal of this analysis is to increase the performance of cell image segmentation process, without affecting the results in a significant way. Even though, a totally manual system has the ability of producing the best results, it has the disadvantage of taking too long and being repetitive, when a large number of images need to be processed. An active contour algorithm was tested in a sequence of images taken by a microscope. This algorithm, more commonly known as snakes, allowed the user to define an initial region in which the cell was incorporated. Then, the algorithm would run several times, making the initial region contours to converge to the cell boundaries. With the final contour, it was possible to extract region properties and produce statistical data. This data allowed to say that this algorithm produces similar results to a purely manual system but at a faster rate. On the other hand, it is slower than a purely automatic way but it allows the user to adjust the contour, making it more versatile and tolerant to image variations.

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.

Ionic liquids as catalysts of lignocellulosic biomass processing

The present work is devoted to study the pre-treatment of lignocellulosic biomass, especially wheat straw, by the application of the acidic ionic liquid (IL) such as 1-butyl-3-methylimidazolium hydrogen sulphate. The ability of this IL to hydrolysis and conversion of biomass was scrutinised. The pre-treatment with hydrogen sulphate-based IL allowed to obtain a liquor rich in hemicellulosic sugars, furans and organic acids, and a solid fraction mainly constituted by cellulose and lignin. Quantitative and qualitative analyses of the produced liquors were made by capillary electrophoresis and high-performance liquid chromatography. Pre-treatment conditions were set to produce xylose or furfural. Specific range of temperatures from 70 to 175 °C and residence times from 20.0 to 163.3 min were studied by fixing parameters such as biomass/IL ratio (10 % (w/w)) and water content (1.25 % (w/w)) in the pre-treatment process. Statistical modelling was applied to maximise the xylose and furfural concentrations. For the purpose of reaction condition comparison the severity factor for studied ionic liquid was proposed and applied in this work. Optimum conditions for xylose production were identified to be at 125 °C and 82.1 min, at which 16.7 % (w/w) xylose yield was attained. Furfural was preferably formed at higher pre-treatment temperatures and longer reaction time (161 °C and 104.5 min) reaching 30.7 % (w/w) maximum yield. The influence of water content on the optimum xylose formation was also studied. Pre-treatments with 5 and 10 % (w/w) water content were performed and an increase of 100 % and 140 % of xylose yield was observed, respectively, while the conversion into furfural maintained unchanged.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Pultrusion of fibre reinforced thermoplastic pre-impregnated materials

Fibre reinforced thermoplastic pre impregnated materials produced continuously by diverse methods and processing conditions were used to produce composites using pultrusion. The processing windows used to produce these materials and composites profiles were optimized by using the Taguchi / DOE (Design of Experiments) methods. Composites were manufactured by pultrusion and compression moulding and subsequently submitted to mechanical testing and microscopy analysis. The obtained results were compared with the expected theoretical ones predicted from the Rule of Mixtures (ROM) and with those of similar engineering conventional available materials. The results obtained shown that produced composites have adequate properties for applications in common and structural engineering markets.

Development of procedures for the design, optimization and manufacturing of customized orthopaedic and trauma implants: Geometrical/anatomical modelling from 3D medical imaging

Tese de Doutoramento (Programa Doutoral em Engenharia Biomédica)

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

MSight: an image analysis software for liquid chromatography-mass spectrometry.

Images obtained from high-throughput mass spectrometry (MS) contain information that remains hidden when looking at a single spectrum at a time. Image processing of liquid chromatography-MS datasets can be extremely useful for quality control, experimental monitoring and knowledge extraction. The importance of imaging in differential analysis of proteomic experiments has already been established through two-dimensional gels and can now be foreseen with MS images. We present MSight, a new software designed to construct and manipulate MS images, as well as to facilitate their analysis and comparison.

L'apôtre, père et mère de la communauté (1 Th 2,1-12)

La correspondance paulinienne réserve, dans ses passages oubliés, quelques surprises propres à nous faire réviser une image figée de Paul. Ainsi 1 Th 2/1-12, où l'apôtre présente son investissement dans la mission sous la double figure de la mère et du père. Daniel MARGUERAT montre que le vocabulaire affectif afflue dans ce texte où Paul appelle les chrétiens de Thessalonique à identifier dans leur histoire personnelle les traces de la puissance de l'évangile. Conscience collective de l'apostolat et lecture théologique des relations interpersonnelles constituent deux accents forts de ce plus ancien écrit de l'apôtre de Tarse.

How multiple repetitions influence the processing of self-, famous and unknown names and faces: an ERP study.

Because we live in an extremely complex social environment, people require the ability to memorize hundreds or thousands of social stimuli. The aim of this study was to investigate the effect of multiple repetitions on the processing of names and faces varying in terms of pre-experimental familiarity. We measured both behavioral and electrophysiological responses to self-, famous and unknown names and faces in three phases of the experiment (in every phase, each type of stimuli was repeated a pre-determined number of times). We found that the negative brain potential in posterior scalp sites observed approximately 170 ms after the stimulus onset (N170) was insensitive to pre-experimental familiarity but showed slight enhancement with each repetition. The negative wave in the inferior-temporal regions observed at approximately 250 ms (N250) was affected by both pre-experimental (famous>unknown) and intra-experimental familiarity (the more repetitions, the larger N250). In addition, N170 and N250 for names were larger in the left inferior-temporal region, whereas right-hemispheric or bilateral patterns of activity for faces were observed. The subsequent presentations of famous and unknown names and faces were also associated with higher amplitudes of the positive waveform in the central-parietal sites analyzed in the 320-900 ms time-window (P300). In contrast, P300 remained unchanged after the subsequent presentations of self-name and self-face. Moreover, the P300 for unknown faces grew more quickly than for unknown names. The latter suggests that the process of learning faces is more effective than learning names, possibly because faces carry more semantic information.