Is ambient intelligence a truly human-centric paradigm in industry? Current research and application scenario

Based on the paper presented at the International Conference “Autonomous Systems: inter-relations of technical and societal issues”, organized by IET with the support of the Portuguese-German collaboration project on “Technology Assessment of Autonomous Robotics” (DAAD/CRUP) at FCT-UNL, Biblioteca da UNL, Campus de Caparica on 5-6 November 2009.

Fast mobility support in low-power wireless networks: smart-HOP over RPL/6LoWPAN

With the emergence of low-power wireless hardware new ways of communication were needed. In order to standardize the communication between these low powered devices the Internet Engineering Task Force (IETF) released the 6LoWPAN stand- ard that acts as an additional layer for making the IPv6 link layer suitable for the lower-power and lossy networks. In the same way, IPv6 Routing Protocol for Low- Power and Lossy Networks (RPL) has been proposed by the IETF Routing Over Low power and Lossy networks (ROLL) Working Group as a standard routing protocol for IPv6 routing in low-power wireless sensor networks. The research performed in this thesis uses these technologies to implement a mobility process. Mobility management is a fundamental yet challenging area in low-power wireless networks. There are applications that require mobile nodes to exchange data with a xed infrastructure with quality-of-service guarantees. A prime example of these applications is the monitoring of patients in real-time. In these scenarios, broadcast- ing data to all access points (APs) within range may not be a valid option due to the energy consumption, data storage and complexity requirements. An alternative and e cient option is to allow mobile nodes to perform hand-o s. Hand-o mechanisms have been well studied in cellular and ad-hoc networks. However, low-power wireless networks pose a new set of challenges. On one hand, simpler radios and constrained resources ask for simpler hand-o schemes. On the other hand, the shorter coverage and higher variability of low-power links require a careful tuning of the hand-o parameters. In this work, we tackle the problem of integrating smart-HOP within a standard protocol, speci cally RPL. The simulation results in Cooja indicate that the pro- posed scheme minimizes the hand-o delay and the total network overhead. The standard RPL protocol is simply unable to provide a reliable mobility support sim- ilar to other COTS technologies. Instead, they support joining and leaving of nodes, with very low responsiveness in the existence of physical mobility.

Decision Support Concerning Demand Response Programs Design and Use – A Conceptual Framework and Simulation Tool

Demand response can play a very relevant role in the context of power systems with an intensive use of distributed energy resources, from which renewable intermittent sources are a significant part. More active consumers participation can help improving the system reliability and decrease or defer the required investments. Demand response adequate use and management is even more important in competitive electricity markets. However, experience shows difficulties to make demand response be adequately used in this context, showing the need of research work in this area. The most important difficulties seem to be caused by inadequate business models and by inadequate demand response programs management. This paper contributes to developing methodologies and a computational infrastructure able to provide the involved players with adequate decision support on demand response programs and contracts design and use. The presented work uses DemSi, a demand response simulator that has been developed by the authors to simulate demand response actions and programs, which includes realistic power system simulation. It includes an optimization module for the application of demand response programs and contracts using deterministic and metaheuristic approaches. The proposed methodology is an important improvement in the simulator while providing adequate tools for demand response programs adoption by the involved players. A machine learning method based on clustering and classification techniques, resulting in a rule base concerning DR programs and contracts use, is also used. A case study concerning the use of demand response in an incident situation is presented.

Algorithms and methodologies for decision support in energy efficiency on buildings

Dissertação apresentada na faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

Georeferencing Road Accidents with Google Earth: Transforming Information into Knowledge for Decision Support

Over the last fifty years mobility practices have changed dramatically, improving the way travel takes place, the time it takes but also on matters like road safety and prevention. High mortality caused by high accident levels has reached untenable levels. But the research into road mortality stayed limited to comparative statistical exercises which go no further than defining accident types. In terms of sharing information and mapping accidents, little progress has been mad, aside from the normal publication of figures, either through simplistic tables or web pages. With considerable technological advances on geographical information technologies, research and development stayed rather static with only a few good examples on dynamic mapping. The use of Global Positioning System (GPS) devices as normal equipments on automobile industry resulted in a more dynamic mobility patterns but also with higher degrees of uncertainty on road traffic. This paper describes a road accident georeferencing project for the Lisbon District involving fatalities and serious injuries during 2007. In the initial phase, individual information summaries were compiled giving information on accidents and its majour characteristics, collected by the security forces: the Public Safety Police Force (Polícia de Segurança Pública - PSP) and the National Guard (Guarda Nacional Republicana - GNR). The Google Earth platform was used to georeference the information in order to inform the public and the authorities of the accident locations, the nature of the location, and the causes and consequences of the accidents. This paper also gives future insights about augmented reality technologies, considered crucial to advances to road safety and prevention studies. At the end, this exercise could be considered a success because of numerous consequences, as for stakeholders who decide what to do but also for the public awareness to the problem of road mortality.

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

Intelligent decision support systems for collaboration in industrial plants

Dissertação apresentada para obtenção do Grau de Doutor em Sistemas de Informação Industriais, Engenharia Electrotécnica, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Short-term Wind Speed Forecasting using Support Vector Machines

Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented.

Mental health promotion during pregnancy and early childhood : an action- research project in primary health care

ABSTRACT – Background: Primary Health Care (PHC) is usually the first contact with the health system, and health professionals are key mediators for enabling citizens to take care of their health. In Portugal, great improvements have been achieved in the biometric indicators of maternal and child health during the last decades. Nevertheless, scant attention has been paid to the mental health dimension, in spite of the recognition of its importance, being pregnancy and early childhood crucial opportunities in the lifecycle for mental health promotion, especially in the early years of life, with a strong impact in the health of the child. The impact of early attachment between mother and baby on maternal and child health has long been recognized. This attachment can be influenced by some factors, as the mother’s emotional adjustment. Attention to these factors may facilitate implementation of both positive conditions and preventative measures. Family support during the transition to parenthood has been highlighted as an effective measure and PHC professionals are in a privileged position as information sources as well as mediators. Aims: The project we present describes an action-research process developed together among academic researchers and health professionals to embrace these issues. We intend to enable health professionals to support families in the transition to parenthood thereby promoting children’s mental health. Approach: The project is driven by a participatory approach intended to lead to reorganization of health care during pregnancy and early childhood. Effective change happens when those involved are interested and motivated, what makes their participation so important. Reflection about current practices and needs, and knowledge about evidence-based interventions have been guiding the selection of changes to introduce in clinical practice for family support and development of parenthood skills and self-confidence. Development: We summarize the main steps in development: the initial assessment and the picture taken from the community under study; the decision making process; the training programme of PHC professionals in action; the review of the protocols of maternal consultation, home visits and antenatal education; the implementation planning; the plan for evaluation the effectiveness of the changes introduced in the delivery of maternal and child health care units. The already developed work has shown that motivation, leadership and organizational issues are decisive for process development.-------------------------- RESUMO - Os Cuidados de Saúde Primários são habitualmente o primeiro contacto com o sistema de saúde e os profissionais de saúde são mediadores chave na capacitação dos cidadãos para cuidarem da sua saúde. Em Portugal, nas últimas décadas, têm-se alcançado grandes melhorias nos indicadores biométricos de saúde materno-infantil. Contudo, tem-se dedicado pouca atenção à dimensão de saúde mental, apesar do reconhecimento da sua importância. A gravidez e primeira infância têm sido apontadas como uma oportunidade crucial no ciclo de vida para a promoção da saúde mental. É dado especial enfoque aos primeiros tempos de vida, dado o forte impacto na saúde da criança. O impacte da vinculação precoce entre a mãe e o bebé na saúde da mãe e da criança há muito que é reconhecido. Esta vinculação pode ser influenciada por vários factores, nomeadamente pelo ajustamento emocional da mãe. A focalização nestes aspectos pode facilitar a criação de condições favoráveis e a implementação de medidas preventivas. O suporte familiar durante o período de transição para a parentalidade tem sido enfatizado como uma medida eficaz e os Cuidados de Saúde Primários estão numa posição privilegiada como fontes de informação e como mediadores. O projecto que apresentamos descreve um processo de investigação- acção desenvolvido em parceria entre investigadores académicos e profissionais de saúde para abordar os aspectos referidos. Pretende-se capacitar os profissionais de saúde para apoiarem as famílias na transição para a parentalidade, promovendo assim a saúde mental das crianças. O projecto baseia-se numa abordagem participativa, direccionada para a reorganização dos cuidados durante a gravidez e primeiros tempos de vida. A mudança efectiva acontece quando os envolvidos estão interessados e motivados, o que torna a sua participação tão importante. A reflexão acerca das práticas e necessidades actuais e o conhecimento acerca de intervenções baseadas na evidência têm guiado a selecção das alterações a introduzir na prática clínica, no sentido de promover o suporte familiar e o desenvolvimento de competências parentais e auto-confiança. Neste artigo, apresentamos as etapas principais do desenvolvimento do projecto: avaliação inicial da comunidade em estudo; processo de tomada de decisão; programa de formação dos profissionais dos Cuidados de Saúde Primários; revisão dos protocolos da consulta de saúde materna, visita domiciliária e educação pré-natal; planeamento da implementação; plano de avaliação da efectividade das alterações introduzidas na prestação de cuidados. O trabalho já desenvolvido tem mostrado que a motivação, liderança e aspectos

TEC4SEA - A Modular Platform for Research, Test and Validation of Technologies Supporting a Sustainable Blue Economy

This paper presents the TEC4SEA research infrastructure created in Portugal to support research, development, and validation of marine technologies. It is a multidisciplinary open platform, capable of supporting research, development, and test of marine robotics, telecommunications, and sensing technologies for monitoring and operating in the ocean environment. Due to the installed research facilities and its privileged geographic location, it allows fast access to deep sea, and can support multidisciplinary research, enabling full validation and evaluation of technological solutions designed for the ocean environment. It is a vertically integrated infrastructure, in the sense that it possesses a set of skills and resources which range from pure conceptual research to field deployment missions, with strong industrial and logistic capacities in the middle tier of prototype production. TEC4SEA is open to the entire scientific and enterprise community, with a free access policy for researchers affiliated with the research units that ensure its maintenance and sustainability. The paper describes the infrastructure in detail, and discusses associated research programs, providing a strategic vision for deep sea research initiatives, within the context of both the Portuguese National Ocean Strategy and European Strategy frameworks.

Architecture to Support Quality of Service in Arrowhead Systems

Presented at INForum - Simpósio de Informática (INFORUM 2015). 7 to 8, Sep, 2015. Covilhã, Portugal.

Membrane structural changes support the involvement of mitochondria in the bile salt-induced apoptosis of rat hepatocytes

Clin Sci (Lond). 2002 Nov;103(5):475-85

UsaGame – A new methodology to support user- centered design of touchscreen game applications

Dissertação para Obtenção do Grau de Mestre em Engenharia e Gestão Industrial

Interactive spatial decision support for sustainable agroforestry management: a case study in East Kalimantan, Indonesia

Forest managers, stakeholders and investors want to be able to evaluate economic, environmental and social benefits in order to improve the outcomes of their decisions and enhance sustainable forest management. This research developed a spatial decision support system that provides: (1) an approach to identify the most beneficial locations for agroforestry projects based on the biophysical properties and evaluate its economic, social and environmental impact; (2) a tool to inform prospective investors and stakeholders of the potential and opportunities for integrated agroforestry management; (3) a simulation environment that enables evaluation via a dashboard with the opportunity to perform interactive sensitivity analysis for key parameters of the project; (4) a 3D interactive geographic visualization of the economic, environmental and social outcomes, which facilitate understanding and eases planning. Although the tool and methodology presented are generic, a case study was performed in East Kalimantan, Indonesia. For the whole study area, it was simulated the most suitable location for three different plantation schemes: monoculture of timber, a specific recipe (cassava, banana and sugar palm) and different recipes per geographic unit. The results indicate that a mixed cropping plantation scheme, with different recipes applied to the most suitable location returns higher economic, environmental and social benefits.