Solar Intensity Forecasting using Artificial Neural Networks and Support Vector Machines

This paper presents several forecasting methodologies based on the application of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), directed to the prediction of the solar radiance intensity. The methodologies differ from each other by using different information in the training of the methods, i.e, different environmental complementary fields such as the wind speed, temperature, and humidity. Additionally, different ways of considering the data series information have been considered. Sensitivity testing has been performed on all methodologies in order to achieve the best parameterizations for the proposed approaches. Results show that the SVM approach using the exponential Radial Basis Function (eRBF) is capable of achieving the best forecasting results, and in half execution time of the ANN based approaches.

Distributed Forest Fire Monitoring Using Wireless Sensor Networks

Disaster management is one of the most relevant application fields of wireless sensor networks. In this application, the role of the sensor network usually consists of obtaining a representation or a model of a physical phenomenon spreading through the affected area. In this work we focus on forest firefighting operations, proposing three fully distributed ways for approximating the actual shape of the fire. In the simplest approach, a circular burnt area is assumed around each node that has detected the fire and the union of these circles gives the overall fire’s shape. However, as this approach makes an intensive use of the wireless sensor network resources, we have proposed to incorporate two in-network aggregation techniques, which do not require considering the complete set of fire detections. The first technique models the fire by means of a complex shape composed of multiple convex hulls representing different burning areas, while the second technique uses a set of arbitrary polygons. Performance evaluation of realistic fire models on computer simulations reveals that the method based on arbitrary polygons obtains an improvement of 20% in terms of accuracy of the fire shape approximation, reducing the overhead in-network resources to 10% in the best case.

Sensor Integration for Smart Cities Using Multi-Hop Networks

Smart Cities are designed to be living systems and turn urban dwellers life more comfortable and interactive by keeping them aware of what surrounds them, while leaving a greener footprint. The Future Cities Project [1] aims to create infrastructures for research in smart cities including a vehicular network, the BusNet, and an environmental sensor platform, the Urban Sense. Vehicles within the BusNet are equipped with On Board Units (OBUs) that offer free Wi-Fi to passengers and devices near the street. The Urban Sense platform is composed by a set of Data Collection Units (DCUs) that include a set of sensors measuring environmental parameters such as air pollution, meteorology and noise. The Urban Sense platform is expanding and receptive to add new sensors to the platform. The parnership with companies like TNL were made and the need to monitor garbage street containers emerged as air pollution prevention. If refuse collection companies know prior to the refuse collection which route is the best to collect the maximum amount of garbage with the shortest path, they can reduce costs and pollution levels are lower, leaving behind a greener footprint. This dissertation work arises in the need to monitor the garbage street containers and integrate these sensors into an Urban Sense DCU. Due to the remote locations of the garbage street containers, a network extension to the vehicular network had to be created. This dissertation work also focus on the Multi-hop network designed to extend the vehicular network coverage area to the remote garbage street containers. In locations where garbage street containers have access to the vehicular network, Roadside Units (RSUs) or Access Points (APs), the Multi-hop network serves has a redundant path to send the data collected from DCUs to the Urban Sense cloud database. To plan this highly dynamic network, the Wi-Fi Planner Tool was developed. This tool allowed taking measurements on the field that led to an optimized location of the Multi-hop network nodes with the use of radio propagation models. This tool also allowed rendering a temperature-map style overlay for Google Earth [2] application. For the DCU for garbage street containers the parner company provided the access to a HUB (device that communicates with the sensor inside the garbage containers). The Future Cities use the Raspberry pi as a platform for the DCUs. To collect the data from the HUB a RS485 to RS232 converter was used at the physical level and the Modbus protocol at the application level. To determine the location and status of the vehicles whinin the vehicular network a TCP Server was developed. This application was developed for the OBUs providing the vehicle Global Positioning System (GPS) location as well as information of when the vehicle is stopped, moving, on idle or even its slope. To implement the Multi-hop network on the field some scripts were developed such as pingLED and “shark”. These scripts helped upon node deployment on the field as well as to perform all the tests on the network. Two setups were implemented on the field, an urban setup was implemented for a Multi-hop network coverage survey and a sub-urban setup was implemented to test the Multi-hop network routing protocols, Optimized Link State Routing Protocol (OLSR) and Babel.

Implementation of an IoT-based Sensor Network Integrating IoT-based Sensor Networks with Large-Scale Message Distribution

As technology advances not only do new standards and programming styles appear but also some of the previously established ones gain relevance. In a new Internet paradigm where interconnection between small devices is key to the development of new businesses and scientific advancement there is the need to find simple solutions that anyone can implement in order to allow ideas to become more than that, ideas. Open-source software is still alive and well, especially in the area of the Internet of Things. This opens windows for many low capital entrepreneurs to experiment with their ideas and actually develop prototypes, which can help identify problems with a project or shine light on possible new features and interactions. As programming becomes more and more popular between people of fields not related to software there is the need for guidance in developing something other than basic algorithms, which is where this thesis comes in: A comprehensive document explaining the challenges and available choices of developing a sensor data and message delivery system, which scales well and implements the delivery of critical messages. Modularity and extensibility were also given much importance, making this an affordable tool for anyone that wants to build a sensor network of the kind.

Learning networks and moodle use in online courses: a social network analysis study

Dissertação para obtenção do Grau de Doutor em Ciências da Educação Especialidade em Tecnologias, Redes e Multimédia na Educação e Formação

ISEGI-NOVA AR Project - Augmented Reality Aplied to the Universidade Nova de Lisboa

Project work presented as a partial requirement to obtain a Master Degree in Information Management

Corporate networks in Poland: Interlocking directorates and business systems

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Social networks as glocal products: The case of Facebook

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Comparing the efficiency of advergames vs. social networks when communicating with children

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

TV advertisement and social networks: an empirical study with young adults

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Old cellulose for new multifunctional networks

Dissertação para obtenção do Grau de Doutor em Ciência e Engenharia de Materiais

Multipacket reception in LTE femtocell networks

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

Migrant networks and political participation in Mozambique

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

A systematic methodology to analyse the performance and design configurations of business interoperability in cooperative industrial networks

This thesis proposes a methodology for modelling business interoperability in a context of cooperative industrial networks. The purpose is to develop a methodology that enables the design of cooperative industrial network platforms that are able to deliver business interoperability and the analysis of its impact on the performance of these platforms. To achieve the proposed objective, two modelling tools have been employed: the Axiomatic Design Theory for the design of interoperable platforms; and Agent-Based Simulation for the analysis of the impact of business interoperability. The sequence of the application of the two modelling tools depends on the scenario under analysis, i.e. whether the cooperative industrial network platform exists or not. If the cooperative industrial network platform does not exist, the methodology suggests first the application of the Axiomatic Design Theory to design different configurations of interoperable cooperative industrial network platforms, and then the use of Agent-Based Simulation to analyse or predict the business interoperability and operational performance of the designed configurations. Otherwise, one should start by analysing the performance of the existing platform and based on the achieved results, decide whether it is necessary to redesign it or not. If the redesign is needed, simulation is once again used to predict the performance of the redesigned platform. To explain how those two modelling tools can be applied in practice, a theoretical modelling framework, a theoretical Axiomatic Design model and a theoretical Agent-Based Simulation model are proposed. To demonstrate the applicability of the proposed methodology and/or to validate the proposed theoretical models, a case study regarding a Portuguese Reverse Logistics cooperative network (Valorpneu network) and a case study regarding a Portuguese construction project (Dam Baixo Sabor network) are presented. The findings of the application of the proposed methodology to these two case studies suggest that indeed the Axiomatic Design Theory can effectively contribute in the design of interoperable cooperative industrial network platforms and that Agent-Based Simulation provides an effective set of tools for analysing the impact of business interoperability on the performance of those platforms. However, these conclusions cannot be generalised as only two case studies have been carried out. In terms of relevance to theory, this is the first time that the network effect is addressed in the analysis of the impact of business interoperability on the performance of networked companies and also the first time that a holistic approach is proposed to design interoperable cooperative industrial network platforms. Regarding the practical implications, the proposed methodology is intended to provide industrial managers a management tool that can guide them easily, and in practical and systematic way, in the design of configurations of interoperable cooperative industrial network platforms and/or in the analysis of the impact of business interoperability on the performance of their companies and the networks where their companies operate.

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.