19 resultados para Stepped-frequency Radar
Time-frequency and time-scale characterisation of the beat-by-beat high-resolution electrocardiogram
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Proceedings of the Sixth Portuguese Conference on Bioemedical Engineering faro, Portugal
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Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação
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We show that the number of merger proposals (frequency-based deterrence) is a more appropriate indicator of underlying changes in merger policy than the relative anti-competitiveness of merger proposals (composition-based deterrence). This has strong implications for the empirical analysis of the deterrence effects of merger policy enforcement, and potential implications regarding how to reduce anti-competitive merger proposals.
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IEEE International Symposium on Circuits and Systems, pp. 2713 – 2716, Seattle, EUA
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Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e Computadores
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Proceedings of the Information Technology Applications in Biomedicine, Ioannina - Epirus, Greece, October 26-28, 2006
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Proceedings Institute of Acoustics (UK); vol. 25, nº2, p. 72-78.
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Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão da Informação
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Dissertação para obtenção do Grau de Mestre em Engenharia Civil – Ramo de Estruturas e Geotecnia
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Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica
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Cash-in-advance models usually require agents to reallocate money and bonds in fixed periods, every month or quarter, for example. I show that fixed periods underestimate the welfare cost of inflation. I use a model in which agents choose how often they exchange bonds for money. In the benchmark specification, the welfare cost of ten percent instead of zero inflation increases from 0.1 percent of income with fixed periods to one percent with optimal periods. The results are robust to different preferences, to different compositions of income in bonds or money, and to the introduction of capital and labor.
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Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.
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Cash-in-advance models usually require agents to reallocate money and bonds in fixed periods. Every month or quarter, for example. I show that fixed periods underestimate the welfare cost of inflation. I use a model in which agents choose how often they exchange bonds for money. In the benchmark specification, the welfare cost of 10 percent instead of 0 inflation increases from 0.1 percent of income with fixed periods to 1 percent with optimal periods. The results are robust to different references, to different compositions of income in bonds or money, and to the introduction of capital and labor.
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The Electrohysterogram (EHG) is a new instrument for pregnancy monitoring. It measures the uterine muscle electrical signal, which is closely related with uterine contractions. The EHG is described as a viable alternative and a more precise instrument than the currently most widely used method for the description of uterine contractions: the external tocogram. The EHG has also been indicated as a promising tool in the assessment of preterm delivery risk. This work intends to contribute towards the EHG characterization through the inventory of its components which are: • Contractions; • Labor contractions; • Alvarez waves; • Fetal movements; • Long Duration Low Frequency Waves; The instruments used for cataloging were: Spectral Analysis, parametric and non-parametric, energy estimators, time-frequency methods and the tocogram annotated by expert physicians. The EHG and respective tocograms were obtained from the Icelandic 16-electrode Electrohysterogram Database. 288 components were classified. There is not a component database of this type available for consultation. The spectral analysis module and power estimation was added to Uterine Explorer, an EHG analysis software developed in FCT-UNL. The importance of this component database is related to the need to improve the understanding of the EHG which is a relatively complex signal, as well as contributing towards the detection of preterm birth. Preterm birth accounts for 10% of all births and is one of the most relevant obstetric conditions. Despite the technological and scientific advances in perinatal medicine, in developed countries, prematurity is the major cause of neonatal death. Although various risk factors such as previous preterm births, infection, uterine malformations, multiple gestation and short uterine cervix in second trimester, have been associated with this condition, its etiology remains unknown [1][2][3].
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Many viruses have developed numerous strategies to recruit and take advantage of cellular protein degradation pathways to evade the cellular viral immune system. One such virus is the Kaposi´s Sarcoma associated herpesvirus (KSHV), first discovered in Kaposi´s Sarcoma lesions found in AIDS patients. Latency-Associated Nuclear Antigen (LANA) is a KSHV multifunctional protein responsible for tethering viral DNA to the chromosome ensuring maintenance and segregation of the viral genome during cell division. Besides its main role of viral maintenance, LANA also physically interacts with several host proteins to modulate cell functions. One such function is to recruit the EC5S ubiquitin-ligase complex by interacting with Elongin BC complex and Cullin 5 protein, which in turn ubiquitinate substrates such as NF-κB and p53 to allow persistent viral infection. Like any other post-translation modifications, ubiquitination is reversible through deubiquitination enzymes (DUBs). LANA also interacts with ubiquitin specific protease 7 (USP7), a deubiquitination enzyme involved in regulation of several proteins including p53. Interaction with USP7 is made through a conserved peptide motif, which is also present in LANA. This work addresses the role of LANA in the recruitment and modulation of the ubiquitination and deubiquitination pathways. Despite the continued efforts in uncovering new LANA interacting partners to form a functional EC5S ubiquitin-ligase complex, only MHV-68 LANA interacted directly with Elongin BC, other interactions were not direct and may require a linker protein. On the other hand, LANA interaction with USP7 was able to be analysed by X-ray structure determination. In addition to a conserved P/AxxS motif, a novel Glutamine (Gln) residue from KSHV LANA was shown to make a specific interaction with USP7. This Gln residue is also present in other herpesvirus protein and hence it might be a conserved motif within herpesviruses.