Acompanhamento do reparo endovascular de aneurisma de aorta abdominal através da pressão intra-saco:resultados de uso de sensor de pressão em cursto e médio prazo.

Trabalho de Conclusão de Curso - Universidade Federal de Santa Catarina. Curso de Medicina. Dapartamento de Clínica Cirúrgica

Comparative multivariate forecast performance for the G7 Stock Markets: VECM Models vs deep learning LSTM neural networks

[EN] The prediction of stock prices dynamics is a challenging task since these kind of financial datasets are characterized by irregular fluctuations, nonlinear patterns and high uncertainty dynamic changes.The deep neural network models, and in particular the LSTM algorithm, have been increasingly used by researchers for analysis, trading and prediction of stock market time series, appointing an important role in today’s economy.The main purpose of this paper focus on the analysis and forecast of the Standard & Poor’s index by employing multivariate modelling on several correlated stock market indexes and interest rates with the support of VECM trends corrected by a LSTM recurrent neural network.Mendes, D.; Ferreira, NR.; Mendes, V. (2020). Comparative multivariate forecast performance for the G7 Stock Markets: VECM Models vs deep learning LSTM neural networks. Editorial Universitat Politècnica de València. 163-171. https://doi.org/10.4995/CARMA2020.2020.11616OCS16317

Equity valuation : Under Armour

The present dissertation aims to value Under Armour, an American sportswear company. Since Valuation is not an exact science, during the literature review will be presented several valuation methods. Most of the authors mention DCF Valuation as one of the best but it seems impossible for them to reach a consensus about which one is in fact the best. In order to get Under Armour’s target price, a DCF valuation will be made and accompanied by a Relative Valuation that, when it is properly used, it can perform the perfect complement. In the end, a comparison between the dissertation’s result and JP Morgan report will be made. In January 2016, JP Morgan was predicting a target price of $90 per share.A presente dissertação tem como objetivo avaliar a Under Armour, uma empresa americana de têxtil desportivo. Uma vez que uma avaliação não é uma ciência exata, durante a revisão de literatura serão apresentados alguns métodos de avaliação. Para muitos autores uma avaliação feita através do DCF é considerada como uma das melhores, mas parece impossível para os mesmos chegar a um consenso sobre que é realmente o melhor método. Para chegar ao preço final da Under Armour, uma avaliação usando o DCF irá ser feita e acompanhada por uma avaliação por múltiplos que quando usada adequadamente, pode desempenhar o papel de perfeito complemento para a primeira. No final irá ser realizado uma comparação entre o resultado obtido na dissertação e o relatório feito pela JP Morgan. Em Janeiro de 2016 a JP Morgan estava a prever um preço final de$90 por ação

Speech emotion recognition through statistical classification

O propósito desta dissertação é a discussão do reconhecimento de emoção na voz. Para este fim, criou-se uma base de dados validada de discurso emocional simulado Português, intitulada European Portuguese Emotional Discourse Database (EPEDD) e foram operados algoritmos de classificação estatística nessa base de dados. EPEDD é uma base de dados simulada, caracterizada por pequenos discursos (5 frases longas, 5 frases curtas e duas palavras), todos eles pronunciados por 8 atores—ambos os sexos igualmente representados—em 9 diferentes emoções (raiva, alegria, nojo, excitação, apatia, medo, surpresa, tristeza e neutro), baseadas no modelo de emoções de Lövheim. Concretizou-se uma avaliação de 40% da base de dados por avaliadores inexperientes, filtrando 60% dos pequenos discursos, com o intuito de criar uma base de dados validada. A base de dados completa contem 718 instâncias, enquanto que a base de dados validada contém 116 instâncias. A qualidade média de representação teatral, numa escala de a 5 foi avaliada como 2,3. A base de dados validada é composta por discurso emocional cujas emoções são reconhecidas com uma taxa média de 69,6%, por avaliadores inexperientes. A raiva tem a taxa de reconhecimento mais elevada com 79,7%, enquanto que o nojo, a emoção cuja taxa de reconhecimento é a mais baixa, consta com 40,5%. A extração de características e a classificação estatística foi realizada respetivamente através dos softwares Opensmile e Weka. Os algoritmos foram operados na base dados original e na base de dados avaliada, tendo sido obtidos os melhores resultados através de SVMs, respetivamente com 48,7% e 44,0%. A apatia obteve a taxa de reconhecimento mais elevada com 79,0%, enquanto que a excitação obteve a taxa de reconhecimento mais baixa com 32,9%.The purpose of this dissertation is to discuss speech emotion recognition. It was created a validated acted Portuguese emotional speech database, named European Portuguese Emotional Discourse Database (EPEDD), and statistical classification algorithms have been applied on it. EPEDD is an acted database, featuring 12 utterances (2 single-words, 5 short sentences and 5 long sentences) per actor and per emotion, 8 actors, both genders equally represented, and 9 emotions (anger, joy, disgust, excitement, fear, apathy, surprise, sadness and neutral), based on Lövheim’s emotion model. We had 40% of the database evaluated by unexperienced evaluators, enabling us to produce a validated one, filtering 60% of the evaluated utterances. The full database contains 718 instances, while the validated one contains 116 instances. The average acting quality of the original database was evaluated, in a scale from 1 to 5, as 2,3. The validated database is composed by emotional utterances that have their emotions recognized on average at a 69,6% rate, by unexperienced judges. Anger had the highest recognition rate at 79,7%, while disgust had the lowest recognition rate at 40,5%. Feature extraction and statistical classification algorithms were performed respectively applying Opensmile and Weka software. Statistical classification algorithms operated in the full database and in the validated one, best results being obtained by SVMs, respectively the emotion recognition rates being 48,7% and 44,0%. Apathy had the highest recognition rate: 79.0%, while excitement had the lowest emotion recognition rate: 32.9%

Analysis of the Coupling Coefficient in Inductive Energy Transfer Systems

In wireless energy transfer systems, the energy is transferred from a power source to an electrical load without the need of physical connections. In this scope, inductive links have been widely studied as a way of implementing these systems. Although high efficiency can be achieved when the system is operating in a static state, it can drastically decrease if changes in the relative position and in the coupling coefficient between the coils occur. In this paper, we analyze the coupling coefficient as a function of the distance between two planar and coaxial coils in wireless energy transfer systems. A simple equation is derived from Neumann’s equation for mutual inductance, which is then used to calculate the coupling coefficient. The coupling coefficient is computed using CST Microwave Studio and compared to calculation and experimental results for two coils with an excitation signal of up to 10 MHz. The results showed that the equation presents good accuracy for geometric parameters that do not lead the solution of the elliptic integral of the first kind to infinity