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Dissertação apresentada à Escola Superior de Tecnologia do Instituto Politécnico de Castelo Branco para obtenção do grau de Mestre em Desenvolvimento de Software e Sistemas Interativos.

Dissertação apresentada à Escola Superior de Tecnologia do Instituto Politécnico de Castelo Branco e Instituto de Telecomunicações para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Desenvolvimento de Software e Sistemas Interativos, realizada sob a orientação científica do Professor adjunto Doutor Osvaldo Santos, do Instituto Politécnico de Castelo Branco.

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With the massive deployment of broadband access to the end-users, the continuous improvement of the hardware capabilities of end devices and better video compression techniques, acceptable conditions have been met to unleash over-the-top bandwidth demanding and time-stringent P2P applications, such as multiview real-time media distribution. Such applications enable the transmission of multiple views of the same scene, providing consumers with a more immersive visual experience. This article proposes an architecture to distribute multiview real-time media content using a hybrid DVB-T2, client-server and P2P paradigms, supported by an also novel QoS solution. The approach minimizes packet delay, interar- rival jitter, inter-ISP traffic and traffic at the ISP core network, which are some of the main drawbacks of P2P networks, whilst still meeting stringent QoS demands. The proposed architecture uses DVB-T2 to distribute a self-contained and fully decodable base-layer video signal, assumed to be always available to the end-user, and an IP network to distribute in parallel - with increased delay - additional IP video streams. The result is a decoded video quality that adapts to individual end-user conditions and maxi- mizes viewing experience. To achieve its target goal this architecture: defines new services for the ISP’s services network and new roles for the ISP core, edge and border routers; makes use of pure IP mul- ticast transmission at the ISP’s core network, greatly minimizing bandwidth consumption; constructs a geographically contained P2P network that uses P2P application-level multicast trees to assist the dis- tribution of the IP video streams at the ISP access networks, greatly reducing inter-ISP traffic, and; de- scribes a novel QoS control architecture that takes advantage of the Internet resource over-provisioning techniques to meet stringent QoS demands in a scalable manner. The proposed architecture has been im- plemented in both real test bed implementation and ns-2 simulations. Results have shown a highly scal- able P2P overlay construction algorithm, with very fast computation of application-level multicast trees (in the order of milliseconds), and efficient reaction to peer-churn with no perceptually annoying impair- ments noticed. Furthermore, enormous bandwidth savings are achieved at the ISP core network, which considerable lower management and investment costs in infrastructure. The QoS based results have also shown that the proposed approach effectively deploys a fast and scalable resource and admission control mechanism, considerably lowering signalling events using a per-class over-provisioning approach thus preventing per-flow QoS reservation signalling messages. Moreover, it is aware of network link resources in real-time and supports for service differentiation and network convergence by guaranteeing that each admitted traffic flow receives the contracted QoS. Finally, the proposed architecture for Multiview Real- Time Media Distribution for Next Generation Networks, as a component for a large project demonstrator, has been evaluated by an independent panel of experts following ITU recommendations, obtaining an excellent evaluation as computed by Mean Opinion Score.

This work is a part of an ongoing study to substitute the identification of waste containers via radio-frequency identification. The purpose of this paper is to propose a method of identification based on computer vision that performs detection using images, video, or real-time video capture to identify different types of waste containers. Compared to the current method of identification, this approach is more agile and does not require as many resources. Two approaches are employed, one using feature detectors/descriptors and other using convolutional neural networks. The former used a vector of locally aggregated descriptors (VLAD); however, it failed to accomplish what was desired. The latter used you only look once (YOLO), a convolutional neural network, and reached an accuracy in the range of 90%, meaning that it correctly identified and classified 90% of the pictures used on the test set.

This paper presents a new approach to help redesigning waste management for the cities of the future. The current state of tracking waste containers is rigid, inefficient and hard to oversee. Although attempts have been made in the past using radio-frequency identification for waste container detection, it has shown problems like flexibility, cost and environmental impact. We propose and demonstrate a solution based on the use of computer vision techniques, for object detection and classification, towards the differentiation between different types of waste containers.

Este artigo apresenta o desenho e implementação de um protótipo fun- cional para avaliar e validar a utilização de técnicas de visão computacional, na identificação de contentores de resíduos no contexto de uma cidade inteligente. Este protótipo recorre à utilização de uma rede neuronal convolucional YOLO e de um microcomputador Jetson Nano da Nvidia. Comparativamente ao método atual de identificação de contentores de resíduos por radiofrequência, esta abor- dagem é mais ágil e diminui os recursos necessários para implementação, contri- buindo para poupar nos gastos logísticos e de implementação da gestão inteli- gente de resíduos.

O trabalho apresentado resulta de uma investigação preliminar que visa a utilização de técnicas de visão computacional para substituir o método atual de identificação de contentores de resíduos via identificação por radiofrequência. Comparativamente ao método atual, esta abordagem é mais ágil e diminui os recursos necessários para implementação. A abordagem aqui discutida é centrada no uso de redes neuronais convolucionais, mais especificamente a rede YOLO.

The agro-industrial sector consumes a significant amount of natural resources for farming and meat production. By 2050, population growth is expected, generating more demand and, consequently, more consumption of scarce resources. This challenging scenario is a concern of the European Commission, revealed in the Green Deal commitment and by the United Nations’ 12th goal of sustainable development. Thus, organizations must increase productivity and be more sustainable as soon as possible. Internet of Things (IoT) is introduced as a solution to facilitate agro-food companies to be more eco-efficient, mainly facing difficulties on farms, such as food loss and waste, best efficiency in management of resources, and production. The deployment of this technology depends on the stage of maturity and potential of implementation. To assess and characterize companies, with respect of IoT implementation, a survey was applied in 21 micro, small and medium agro-food companies, belonging to milk, honey, olive oil, jams, fruticulture, bakery and pastry, meat, coffee, and wine sectors, in the central region of Portugal. As results, this paper reveals the stage of maturity, level of sophistication, potential, opportunities, solutions, and barriers for implementation of IoT. Additionally, suggestions and recommendations to improve practices are discussed.

Este artigo apresenta o desenho e implementação de um protótipo para a empresa EVOX Technologies, para obtenção de informação sobre níveis de enchimento, anomalias e outros alertas relacionados com contentores de resíduos urbanos, aquando da recolha destes pelos cantoneiros. São descritas as tecnologias adotadas a nível de hardware e software para implementação do protótipo baseado num LCD que comunica com um sistema de gestão de resíduos. Constitui uma alternativa viável e vantajosa à solução tradicional baseada numa botoneira simples e estática, contribuindo para a gestão em tempo real e para a redução dos gastos logísticos e de implementação da gestão inteligente de resíduos.

Este trabalho apresenta o desenho e implementação de um protótipo funcional para avaliar e validar a utilização de técnicas de visão computacional, na identificação de contentores de resíduos no contexto de uma cidade inteli- gente. Este protótipo recorre à utilização de uma rede neuronal convolucional YOLO e de um microcomputador Jetson Nano da Nvidia. Comparativamente ao método atual de identificação de contentores de resíduos por radiofrequência, esta abordagem é mais ágil e diminui os recursos necessários para implementa- ção, contribuindo para poupar nos gastos logísticos e de implementação da gestão inteligente de resíduos.