Wi-Fi fingerprinting in the real world – RTLS@UM at the EvAAL competition

Research and development around indoor positioning and navigation is capturing the attention of an increasing number of research groups and labs around the world. Among the several techniques being proposed for indoor positioning, solutions based on Wi-Fi fingerprinting are the most popular since they exploit existing WLAN infrastructures to support software-only positioning, tracking and navigation applications. Despite the enormous research efforts in this domain, and despite the existence of some commercial products based on Wi-Fi fingerprinting, it is still difficult to compare the performance, in the real world, of the several existing solutions. The EvAAL competition, hosted by the IPIN 2015 conference, contributed to fill this gap. This paper describes the experience of the RTLS@UM team in participating in track 3 of that competition.

HEP-Frame: A software engineered framework to aid the development and efficient multicore execution of scientific code

This work was supported by FCT (Fundação para a Ciência e Tecnologia) within Project Scope (UID/CEC/00319/2013), by LIP (Laboratório de Instrumentação e Física Experimental de Partículas) and by Project Search-ON2 (NORTE-07-0162- FEDER-000086), co-funded by the North Portugal Regional Operational Programme (ON.2 - O Novo Norte), under the National Strategic Reference Framework, through the European Regional Development Fund.

Monitoring energy consumption in android applications

Dissertação de Mestrado em Engenharia Informática

Unfuzzying fuzzy parsing

Dissertação de Mestrado em Engenharia Informática

Spectrum-based energy leak localization

Dissertação de Mestrado em Engenharia Informática

Implementation and comparison of different switching techniques for shunt active power filters

This paper presents a comparison between three switching techniques that can be used in three-phase four-wire Shunt Active Power Filters (SAPFs). The implemented switching techniques are: Periodic-Sampling (PS), Triangular Carrier Pulse-Width Modulation (TC-PWM) and Space Vector PWM (SVPWM). The comparison between them is made in terms of the compensated currents THD%, implementation complexity, necessary CPU time and SAPF efficiency. To perform this comparison are presented and analyzed several experimental results, obtained with a 20 kVA Shunt Active Power Filter prototype, specially developed for this purpose. The control system of the developed SAPF is based in the p-q Theory with a grid synchronization algorithm p-PLL.

Clustering-based approach for categorizing pregnant women in obstetrics and maternity care

When a pregnant woman is guided to a hospital for obstetrics purposes, many outcomes are possible, depending on her current conditions. An improved understanding of these conditions could provide a more direct medical approach by categorizing the different types of patients, enabling a faster response to risk situations, and therefore increasing the quality of services. In this case study, the characteristics of the patients admitted in the maternity care unit of Centro Hospitalar of Porto are acknowledged, allowing categorizing the patient women through clustering techniques. The main goal is to predict the patients’ route through the maternity care, adapting the services according to their conditions, providing the best clinical decisions and a cost-effective treatment to patients. The models developed presented very interesting results, being the best clustering evaluation index: 0.65. The evaluation of the clustering algorithms proved the viability of using clustering based data mining models to characterize pregnant patients, identifying which conditions can be used as an alert to prevent the occurrence of medical complications.

Improving quality of medical service with mobile health software

An increasing number of m-Health applications are being developed benefiting health service delivery. In this paper, a new methodology based on the principle of calm computing applied to diagnostic and therapeutic procedure reporting is proposed. A mobile application was designed for the physicians of one of the Portuguese major hospitals, which takes advantage of a multi-agent interoperability platform, the Agency for the Integration, Diffusion and Archive (AIDA). This application allows the visualization of inpatients and outpatients medical reports in a quicker and safer manner, in addition to offer a remote access to information. This project shows the advantages in the use of mobile software in a medical environment but the first step is always to build or use an interoperability platform, flexible, adaptable and pervasive. The platform offers a comprehensive set of services that restricts the development of mobile software almost exclusively to the mobile user interface design. The technology was tested and assessed in a real context by intensivists.

Gamification e game-based learning: uma abordagem lúdica à aprendizagem

Relatório de estágio de mestrado em Ensino de Informática

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Single-function approach to calibrating whatman n.º 42 filter paper based on suction versus water content relationships

Several suction–water-content (s-w) calibrations for the filter paper method (FPM) used for soil-suction measurement have been published. Most of the calibrations involve a bilinear function (i.e., two different equations) with an inflection point occurring at 60 kPatechniques for two different soils for the suction range of 50 kPa

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

Personalized-based dietary recommendation for weightlifting

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores

Estudo e implementação de testes de software em desenvolvimento ágil

Dissertação de mestrado em Engenharia de Sistemas

A integração de técnicas BIM (Building Information Modeling) no projeto e na construção de estruturas de madeira

Dissertação de mestrado integrado em Engenharia Civil