Uma análise das diferenças em termos da cultura e dos interesses profissionais a partir do projeto de substituiçao das IAS 39: Financial Instruments - Recognition and Measurement

Mestrado em Contabilidade

ECG Biometrics: A Template Selection Approach

Electrocardiography (ECG) biometrics is emerging as a viable biometric trait. Recent developments at the sensor level have shown the feasibility of performing signal acquisition at the fingers and hand palms, using one-lead sensor technology and dry electrodes. These new locations lead to ECG signals with lower signal to noise ratio and more prone to noise artifacts; the heart rate variability is another of the major challenges of this biometric trait. In this paper we propose a novel approach to ECG biometrics, with the purpose of reducing the computational complexity and increasing the robustness of the recognition process enabling the fusion of information across sessions. Our approach is based on clustering, grouping individual heartbeats based on their morphology. We study several methods to perform automatic template selection and account for variations observed in a person's biometric data. This approach allows the identification of different template groupings, taking into account the heart rate variability, and the removal of outliers due to noise artifacts. Experimental evaluation on real world data demonstrates the advantages of our approach.

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.

Logarithmically proportional objective function for planar surfaces recognition in 3D point cloud

3D laser scanning is becoming a standard technology to generate building models of a facility's as-is condition. Since most constructions are constructed upon planar surfaces, recognition of them paves the way for automation of generating building models. This paper introduces a new logarithmically proportional objective function that can be used in both heuristic and metaheuristic (MH) algorithms to discover planar surfaces in a point cloud without exploiting any prior knowledge about those surfaces. It can also adopt itself to the structural density of a scanned construction. In this paper, a metaheuristic method, genetic algorithm (GA), is used to test this introduced objective function on a synthetic point cloud. The results obtained show the proposed method is capable to find all plane configurations of planar surfaces (with a wide variety of sizes) in the point cloud with a minor distance to the actual configurations. © 2014 IEEE.

Chiroptical and emissive properties of a calix[4]arene-containing chiral poly (P-phenylene ethynylene) with enantioselective recognition ability

Supramolecular chirality was achieved in solutions and thin films of a calixarene-containing chiral aryleneethynylene copolymer. The observed chiroptical activity, which is primarily allied with the formation of aggregates of high molecular weight polymer chains, is the result of a combination of intrachain and interchain effects. The former arises by the adoption of an induced helix-sense by the polymer main-chain while the latter comes from the exciton coupling of aromatic backbone transitions. The co-existence of bulky bis-calixKlarene units and chiral side-chains on the polymer skeleton prevents efficient pi-stacking of neighbouring chains, keeping the chiral assembly highly emissive. In contrast, for a model polymer lacking calixarene moieties, the chiroptical activity is dominated by strong interchain exciton couplings as a result of more favourable packing of polymer chains, leading to a marked decrease of photoluminescence in the aggregate state. The enantiomeric recognition abilities of both polymers towards (R)- and (S)-alpha-methylbenzylamine were examined. It was found that a significant enantiodiscrimination is exhibited by the calixarene-based polymer in the aggregate state.

Cultura da (in) segurança nos contextos de trabalho em enfermagem: o caso do Hospital do Espírito Santo de Évora - E.P.E.

Mestrado em Intervenção Sócio-Organizacional na Saúde - Área de especialização: Políticas de Administração e Gestão de Serviços de Saúde

A percepção dos profissionais de saúde sobre o trabalho em equipa no contexto do Hospital do Espírito Santo de Évora, E.P.E.

Mestrado em Intervenção Sócio-Organizacional na Saúde - Área de especialização: Políticas de Administração e Gestão de Serviços de Saúde

O trabalho individual e o trabalho de grupo no processo de ensino-aprendizagem do estudo do meio no 1º Ciclo

Relatório Final apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ensino do 1º e 2º ciclo do Ensino Básico

Biometric recognition system using low bandwidth ECG signals

Biometric recognition has recently emerged as part of applications where the privacy of the information is crucial, as in the health care field. This paper presents a biometric recognition system based on the Electrocardiographic signal (ECG). The proposed system is based on a state-of-the-art recognition method which extracts information from the frequency domain. In this paper we propose a new method to increase the spectral resolution of low bandwidth ECG signals due to the limited bandwidth of the acquisition sensor. Preliminary results show that the proposed scheme reveals a higher identification rate and lower equal error rate when compared to previous approaches.