Modelling tyre-road noise with data mining techniques

The research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and uneven- ness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic variables and another with complex variables, such as megatexture and damping, all as a function of vehicles speed. More detailed models were additionally set by the speed level. As a result, several models with very good tyre-road noise predictive capacity were achieved. The most relevant variables were Speed, Temperature, Aggregate size, Mean Profile Depth, and Damping, which had the highest importance, even though influenced by speed. Megatexture and IRI had the lowest importance. The applicability of the models developed in this work is relevant for trucks tyre-noise prediction, represented by the AVON V4 test tyre, at the early stage of road pavements use. Therefore, the obtained models are highly useful for the design of pavements and for noise prediction by road authorities and contractors.

Método de desagregação temporal para a previsão da taxa de desemprego em Portugal

Dissertação de mestrado em Estatística

Poly lactic acid based biodegradable stents and functionalization techniques: brief review

Commercial stents, especially metallic ones, present several disadvantages, and this gives rise to the necessity of producing or coating stents with different materials, like natural polymers, in order to improve their biocompatibility and minimize the disadvantages of metallic ones. This review paper discusses some applications of natural-based polymers in stents, namely polylactic acid (PLA) for stent development and chitosan for biocompatible coatings of stents . Furthermore, some effective stent functionalization techniques will be discussed, namely Layer by Layer (LBL) technique.

Desenvolvimento de um filtro ativo paralelo com CSI e controlo de corrente no barramento CC

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores

Big data e data analysis: visualização de informação

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Apoio à decisão na análise inteligente de reclamações

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Quantifying, generating and mitigating radio interference in Low-Power Wireless Networks

Doctoral Programme in Telecommunication - MAP-tele

Neurocognitive profile and cognitive intervention in Alzheimer's disease

Tese de Doutoramento em Psicologia (Especialidade de Psicologia Clínica)

Monitoring for a decidable fragment of MTL-∫

Temporal logics targeting real-time systems are traditionally undecidable. Based on a restricted fragment of MTL-R, we propose a new approach for the runtime verification of hard real-time systems. The novelty of our technique is that it is based on incremental evaluation, allowing us to e↵ectively treat duration properties (which play a crucial role in real-time systems). We describe the two levels of operation of our approach: offline simplification by quantifier removal techniques; and online evaluation of a three-valued interpretation for formulas of our fragment. Our experiments show the applicability of this mechanism as well as the validity of the provided complexity results.

Flexible gastrointestinal motility pressure sensors based on aluminum thin-film strain-gauge arrays

This paper reports on an innovative approach to measuring intraluminal pressure in the upper gastrointestinal (GI) tract, especially monitoring GI motility and peristaltic movements. The proposed approach relies on thin-film aluminum strain gauges deposited on top of a Kapton membrane, which in turn lies on top of an SU-8 diaphragm-like structure. This structure enables the Kapton membrane to bend when pressure is applied, thereby affecting the strain gauges and effectively changing their electrical resistance. The sensor, with an area of 3.4 mm2, is fabricated using photolithography and standard microfabrication techniques (wet etching). It features a linear response (R2 = 0.9987) and an overall sensitivity of 2.6 mV mmHg−1. Additionally, its topology allows a high integration capability. The strain gauges’ responses to pressure were studied and the fabrication process optimized to achieve high sensitivity, linearity, and reproducibility. The sequential acquisition of the different signals is carried out by a microcontroller, with a 10-bit ADC and a sample rate of 250 Hz. The pressure signals are then presented in a user-friendly interface, developed using the Integrated Development Environment software, QtCreator IDE, for better visualization by physicians.

Deteção e classificação de regiões de interesse em vídeos de endoscopia

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Informática Médica)

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.

Predicting pre-triage waiting time in a maternity emergency room through data mining

An unsuitable patient flow as well as prolonged waiting lists in the emergency room of a maternity unit, regarding gynecology and obstetrics care, can affect the mother and child’s health, leading to adverse events and consequences regarding their safety and satisfaction. Predicting the patients’ waiting time in the emergency room is a means to avoid this problem. This study aims to predict the pre-triage waiting time in the emergency care of gynecology and obstetrics of Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto, situated in the north of Portugal. Data mining techniques were induced using information collected from the information systems and technologies available in CMIN. The models developed presented good results reaching accuracy and specificity values of approximately 74% and 94%, respectively. Additionally, the number of patients and triage professionals working in the emergency room, as well as some temporal variables were identified as direct enhancers to the pre-triage waiting time. The imp lementation of the attained knowledge in the decision support system and business intelligence platform, deployed in CMIN, leads to the optimization of the patient flow through the emergency room and improving the quality of services.