Identificação e análise de movimento humano com ultrassons

Faz-se nesta dissertação a análise do movimento humano utilizando sinais de ultrassons refletidos pelos diversos membros do corpo humano, designados por assinaturas de ultrassons. Estas assinaturas são confrontadas com os sinais gerados pelo contato dos membros inferiores do ser humano com o chão, recolhidos de forma passiva. O método seguido teve por base o estudo das assinaturas de Doppler e micro-Doppler. Estas assinaturas são obtidas através do processamento dos ecos de ultrassons recolhidos, com recurso à Short-Time Fourier Transform e apresentadas sobre a forma de espectrograma, onde se podem identificar os desvios de frequência causados pelo movimento das diferentes partes do corpo humano. É proposto um algoritmo inovador que, embora possua algumas limitações, é capaz de isolar e extrair de forma automática algumas das curvas e parâmetros característicos dos membros envolvidos no movimento humano. O algoritmo desenvolvido consegue analisar as assinaturas de micro-Doppler do movimento humano, estimando diversos parâmetros tais como o número de passadas realizadas, a cadência da passada, o comprimento da passada, a velocidade a que o ser humano se desloca e a distância percorrida. Por forma a desenvolver, no futuro, um classificador capaz de distinguir entre humanos e outros animais, são também recolhidas e analisadas assinaturas de ultrassons refletidas por dois animais quadrúpedes, um canino e um equídeo. São ainda estudadas as principais características que permitem classificar o tipo de animal que originou a assinatura de ultrassons. Com este estudo mostra-se ser possível a análise de movimento humano por ultrassons, havendo características nas assinaturas recolhidas que permitem a classificação do movimento como humano ou não humano. Do trabalho desenvolvido resultou ainda uma base de dados de assinaturas de ultrassons de humanos e animais que permitirá suportar trabalho de investigação e desenvolvimento futuro.

Analysing organochlorine pesticides in strawberry jams using GC-ECD, GC-MS/MS and QuEChERS sample preparation

This paper describes a comparison of adaptations of the QuEChERS (quick, easy, cheap, effective, rugged and safe) approach for the determination of 14 organochlorine pesticide (OCP) residues in strawberry jam by concurrent use of gas chromatography (GC) coupled to electron capture detector (ECD) and GC tandem mass spectrometry (GC-MS/MS). Three versions were tested based on the original QuEChERS method. The results were good (overall average of 89% recoveries with 15% RSD) using the ultrasonic bath at five spiked levels. Performance characteristics, such as accuracy, precision, linear range, limits of detection (LOD) and quantification (LOQ), were determined for each pesticide. LOD ranged from 0.8 to 8.9 microg kg-1 ; LOQ was in the range of 2.5–29.8 microg kg- 1; and calibration curves were linear (r2>0.9970) in the whole range of the explored concentrations (5–100 microg kg- 1). The LODs of these pesticides were much lower than the maximum residue levels (MRLs) allowed in Europe for strawberries. The method was successfully applied to the quantification of OCP in commercially available jams. The OCPs were detected lower than the LOD.

Global and local detection of liver steatosis from ultrasound

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. Steatosis is usually a diffuse liver disease, since it is globally affected. However, steatosis can also be focal affecting only some foci difficult to discriminate. In both cases, steatosis is detected by laboratorial analysis and visual inspection of ultrasound images of the hepatic parenchyma. Liver biopsy is the most accurate diagnostic method but its invasive nature suggest the use of other non-invasive methods, while visual inspection of the ultrasound images is subjective and prone to error. In this paper a new Computer Aided Diagnosis (CAD) system for steatosis classification and analysis is presented, where the Bayes Factor, obatined from objective intensity and textural features extracted from US images of the liver, is computed in a local or global basis. The main goal is to provide the physician with an application to make it faster and accurate the diagnosis and quantification of steatosis, namely in a screening approach. The results showed an overall accuracy of 93.54% with a sensibility of 95.83% and 85.71% for normal and steatosis class, respectively. The proposed CAD system seemed suitable as a graphical display for steatosis classification and comparison with some of the most recent works in the literature is also presented.

The usefulness of ultrasound in the classification of chronic liver disease

Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.

Estudo de uma tomada de água em modelo reduzido

Dissertação para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização em hidráulica

ECO-betão com adição de resíduo da indústria petrolífera

Dissertação de natureza Científica para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização em Edificações

Robótica móvel: sistema de condução autónoma

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo Automação e Electrónica Industrial

Nanohybrid materials as transducer surfaces for electrochemical sensing applications

A nanohybrid electrochemical transducer surface was developed using carbon and gold nanomaterials. The strategy relayed on casting multiwalled carbon nanotubes or carbon nanofibers onto a screen-printed carbon electrode surface, followed by in situ generation of gold nanoparticles by electrochemical deposition of ionic gold, in a reproducible manner. These transducers, so fabricated, were characterized using both electrochemical and microscopic techniques. Biofunctionality was evaluated using the streptavidin-biotin interaction system as the biological reaction model. These platforms allow to achieve low detection limits (in the order of pmoles), are reproducible and stable at least for a month after their preparation, being a perfect candidate to be used as transducer of different sensor devices.

Estágio na empresa SPECMAN para a prática de técnicas de controlo da condição

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Diagnóstico de avarias em máquinas rotativas utilizando a análise de órbitas

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica Perfil Manutenção e Produção

Incorporação de resíduo da indústria petrolífera em argamassas de cal hidráulica para a reabilitação de edifícios

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Edificações

A scalable and efficient approach for obtaining measurements in CAN-Based control systems

The availability of small inexpensive sensor elements enables the employment of large wired or wireless sensor networks for feeding control systems. Unfortunately, the need to transmit a large number of sensor measurements over a network negatively affects the timing parameters of the control loop. This paper presents a solution to this problem by representing sensor measurements with an approximate representation-an interpolation of sensor measurements as a function of space coordinates. A priority-based medium access control (MAC) protocol is used to select the sensor messages with high information content. Thus, the information from a large number of sensor measurements is conveyed within a few messages. This approach greatly reduces the time for obtaining a snapshot of the environment state and therefore supports the real-time requirements of feedback control loops.

Autonomous behaviors for an unmanned mini-aerial vehicle

This thesis aims at addressing the development of autonomous behaviors, for search and exploration with a mini-UAV (Unmanned Aerial Vehicle), or also called MAV (Mini Aerial Vehicle) prototype, in order to gather information in rescue scenarios. The platform used in this work is a four rotor helicopter, known as quad-rotor from the German company Ascending Technologies GmbH, which is later assembled with a on-board processing unit (i.e. a tiny light weight computer) and a on-board sensor suite (i.e. 2D-LIDAR and Ultrasonic Sonar). This work can be divided into two phases. In the first phase an Indoor Position Tracking system was settled in order to obtain the Cartesian coordinates (i.e. X, Y, Z) and orientation (i.e.heading) which provides the relative position and orientation of the platform. The second phase was the design and implementation of medium/high level controllers on each command input in order to autonomously control the aircraft position, which is the first step towards an autonomous hovering flight, and any autonomous behavior (e.g. Landing, Object avoidance, Follow the wall). The main work is carried out in the Laboratory ”Intelligent Systems for Emergencies and Civil Defense”, in collaboration with ”Dipartimento di Informatica e Sistemistica” of Sapienza Univ. of Rome and ”Istituto Superiore Antincendi” of the Italian Firemen Department.

Enhancing Multimodal Silent Speech Interfaces With Feature Selection

In research on Silent Speech Interfaces (SSI), different sources of information (modalities) have been combined, aiming at obtaining better performance than the individual modalities. However, when combining these modalities, the dimensionality of the feature space rapidly increases, yielding the well-known "curse of dimensionality". As a consequence, in order to extract useful information from this data, one has to resort to feature selection (FS) techniques to lower the dimensionality of the learning space. In this paper, we assess the impact of FS techniques for silent speech data, in a dataset with 4 non-invasive and promising modalities, namely: video, depth, ultrasonic Doppler sensing, and surface electromyography. We consider two supervised (mutual information and Fisher's ratio) and two unsupervised (meanmedian and arithmetic mean geometric mean) FS filters. The evaluation was made by assessing the classification accuracy (word recognition error) of three well-known classifiers (knearest neighbors, support vector machines, and dynamic time warping). The key results of this study show that both unsupervised and supervised FS techniques improve on the classification accuracy on both individual and combined modalities. For instance, on the video component, we attain relative performance gains of 36.2% in error rates. FS is also useful as pre-processing for feature fusion. Copyright © 2014 ISCA.