On the cloud deployment of a session abstraction for service/data aggregation

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Human activity data discovery based on accelerometry

Dissertation to Obtain Master Degree in Biomedical Engineering

Modeling and visualization of medical anesthesiology acts

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

New visualization model for large scale biosignals analysis

Benefits of long-term monitoring have drawn considerable attention in healthcare. Since the acquired data provides an important source of information to clinicians and researchers, the choice for long-term monitoring studies has become frequent. However, long-term monitoring can result in massive datasets, which makes the analysis of the acquired biosignals a challenge. In this case, visualization, which is a key point in signal analysis, presents several limitations and the annotations handling in which some machine learning algorithms depend on, turn out to be a complex task. In order to overcome these problems a novel web-based application for biosignals visualization and annotation in a fast and user friendly way was developed. This was possible through the study and implementation of a visualization model. The main process of this model, the visualization process, comprised the constitution of the domain problem, the abstraction design, the development of a multilevel visualization and the study and choice of the visualization techniques that better communicate the information carried by the data. In a second process, the visual encoding variables were the study target. Finally, the improved interaction exploration techniques were implemented where the annotation handling stands out. Three case studies are presented and discussed and a usability study supports the reliability of the implemented work.

Desenvolvimento de um sensor colorimétrico em papel para a deteção de bactérias eletroquimicamente ativas

Bactérias eletroquimicamente ativas possuem a capacidade de transferir eletrões extracelularmente, durante a respiração celular. Esta característica tem sido atualmente explorada para aplicação na produção de eletricidade, tratamento de águas residuais, biorremediação e em diversas áreas da biotecnologia, onde a transferência eletrónica ocorre na presença de aceitadores insolúveis (tais como, óxidos metálicos e ânodos metálicos). Contudo, o número de espécies identificadas, isoladas e caracterizadas até à data é bastante reduzido. Os métodos atualmente disponíveis para deteção de bactérias eletroquimicamente ativas são morosos, dispendiosos e complexos de operar, tornando-se necessário o desenvolvimento de outros métodos mais rápidos, simples e menos dispendiosos que auxiliem na otimização das aplicações mencionadas. O objetivo principal deste trabalho foi o desenvolvimento de um sensor colorimétrico de papel utilizando um material eletrocrómico, trióxido de tungsténio, como camada ativa para a deteção destas bactérias. Para isso, foram definidos no papel poços delimitados por barreiras hidrofóbicas, através da impressão e difusão de uma camada de cera. As várias amostras de nanopartículas de WO3, sintetizadas por um método hidrotermal assistido por micro-ondas, foram depositadas nos poços por drop casting. As nanopartículas com estrutura cristalográfica hexagonal, impregnadas no sensor de papel, foram capazes de detetar com sucesso uma bactéria eletroquimicamente ativa, Geobacter sulfurreducens, desde uma fase de crescimento bastante inicial (Abs600 nm = 0,1, correspondente a 0,07 g/L com um rácio RGB de 1,10 ± 0,040) até à fase exponencial-tardia (Abs600 nm = 0,5, correspondente a 0,33 g/L com um rácio RGB de 1,33 ± 0,005), com P <0,0001. O sensor de papel e respetivo método de deteção colorimétrico desenvolvido neste trabalho, revelou ser sensível e específico à deteção destas bactérias, de uma forma rápida, simples e pouco dispendiosa.

Analysis and visualization of energy use for university campus

The reduction of greenhouse gas emissions is one of the big global challenges for the next decades due to its severe impact on the atmosphere that leads to a change in the climate and other environmental factors. One of the main sources of greenhouse gas is energy consumption, therefore a number of initiatives and calls for awareness and sustainability in energy use are issued among different types of institutional and organizations. The European Council adopted in 2007 energy and climate change objectives for 20% improvement until 2020. All European countries are required to use energy with more efficiency. Several steps could be conducted for energy reduction: understanding the buildings behavior through time, revealing the factors that influence the consumption, applying the right measurement for reduction and sustainability, visualizing the hidden connection between our daily habits impacts on the natural world and promoting to more sustainable life. Researchers have suggested that feedback visualization can effectively encourage conservation with energy reduction rate of 18%. Furthermore, researchers have contributed to the identification process of a set of factors which are very likely to influence consumption. Such as occupancy level, occupants behavior, environmental conditions, building thermal envelope, climate zones, etc. Nowadays, the amount of energy consumption at the university campuses are huge and it needs great effort to meet the reduction requested by European Council as well as the cost reduction. Thus, the present study was performed on the university buildings as a use case to: a. Investigate the most dynamic influence factors on energy consumption in campus; b. Implement prediction model for electricity consumption using different techniques, such as the traditional regression way and the alternative machine learning techniques; and c. Assist energy management by providing a real time energy feedback and visualization in campus for more awareness and better decision making. This methodology is implemented to the use case of University Jaume I (UJI), located in Castellon, Spain.

Design, implementation, and performance of a distributed and scalable sensor system for critical distance measurements in the CMS detector at LHC

The “CMS Safety Closing Sensors System” (SCSS, or CSS for brevity) is a remote monitoring system design to control safety clearance and tight mechanical movements of parts of the CMS detector, especially during CMS assembly phases. We present the different systems that makes SCSS: its sensor technologies, the readout system, the data acquisition and control software. We also report on calibration and installation details, which determine the resolution and limits of the system. We present as well our experience from the operation of the system and the analysis of the data collected since 2008. Special emphasis is given to study positioning reproducibility during detector assembly and understanding how the magnetic fields influence the detector structure.

Using an LED as a sensor and visible light communication device in a smart illumination system

The need for more efficient illumination systems has led to the proliferation of Solid-State Lighting (SSL) systems, which offer optimized power consumption. SSL systems are comprised of LED devices which are intrinsically fast devices and permit very fast light modulation. This, along with the congestion of the radio frequency spectrum has paved the path for the emergence of Visible Light Communication (VLC) systems. VLC uses free space to convey information by using light modulation. Notwithstanding, as VLC systems proliferate and cost competitiveness ensues, there are two important aspects to be considered. State-of-the-art VLC implementations use power demanding PAs, and thus it is important to investigate if regular, existent Switched-Mode Power Supply (SMPS) circuits can be adapted for VLC use. A 28 W buck regulator was implemented using a off-the-shelf LED Driver integrated circuit, using both series and parallel dimming techniques. Results show that optical clock frequencies up to 500 kHz are achievable without any major modification besides adequate component sizing. The use of an LED as a sensor was investigated, in a short-range, low-data-rate perspective. Results show successful communication in an LED-to-LED configuration, with enhanced range when using LED strings as sensors. Besides, LEDs present spectral selective sensitivity, which makes them good contenders for a multi-colour LED-to-LED system, such as in the use of RGB displays and lamps. Ultimately, the present work shows evidence that LEDs can be used as a dual-purpose device, enabling not only illumination, but also bi-directional data communication.

Building 3D City Models: Testing and Comparing Laser Scanning and Low-Cost UAV Data Using FOSS Technologies

Contém resumo

Comuns: An Open-Data Provider, Explorer and Analytic Toolbox Based on FOSS

Contém resumo

A hybrid MAC Scheme to improve the transmission performance in body sensor networks

Wireless body sensor networks (WBSNs) constitute a key technology for closing the loop between patients and healthcare providers, as WBSNs provide sensing ability, as well as mobility and portability, essential characteristics for wide acceptance of wireless healthcare technology. However, one important and difficult aspect of WBSNs is to provide data transmissions with quality of service, among other factors due to the antennas being small size and placed close to the body. Such transmissions cannot be fully provided without the assumption of a MAC protocol that solves the problems of the medium sharing. A vast number of MAC protocols conceived for wireless networks are based on random or scheduled schemes. This paper studies firstly the suitability of two MAC protocols, one using CSMA and the other TDMA, to transmit directly to the base station the signals collected continuously from multiple sensor nodes placed on the human body. Tests in a real scenario show that the beaconed TDMA MAC protocol presents an average packet loss ratio lower than CSMA. However, the average packet loss ratio is above 1.0 %. To improve this performance, which is of vital importance in areas such as e-health and ambient assisted living, a hybrid TDMA/CSMA scheme is proposed and tested in a real scenario with two WBSNs and four sensor nodes per WBSN. An average packet loss ratio lower than 0.2 % was obtained with the hybrid scheme. To achieve this significant improvement, the hybrid scheme uses a lightweight algorithm to control dynamically the start of the superframes. Scalability and traffic rate variation tests show that this strategy allows approximately ten WBSNs operating simultaneously without significant performance degradation.

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.

Big data e data analysis: visualização de informação

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

Data analytics: abordagem para visualização da informação

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

Analysis of the Atmosphere Aerosol and Ozone Condition Over Tbilisi Using Satellite Data and Ground Truth Measurements

Ground-based measurements of the parameters of atmosphere in Tbilisi during the same period, which are provided by the Mikheil Nodia Institute of geophysics, were used as calibration data. Satellite data monthly averaging, preprocessing, analysis and visualization was performed using Giovanni web-based application. Maps of trends and periodic components of the atmosphere aerosol optical thickness and ozone concentration over the study area were calculated.