Sensor inteligente para medição de cargas mecânicas

A medição precisa da força é necessária para muitas aplicações, nomeadamente, para a determinação da resistência mecânica dos materiais, controlo de qualidade durante a produção, pesagem e segurança de pessoas. Dada a grande necessidade de medição de forças, têm-se desenvolvido, ao longo do tempo, várias técnicas e instrumentos para esse fim. Entre os vários instrumentos utilizados, destacam-se os sensores de força, também designadas por células de carga, pela sua simplicidade, precisão e versatilidade. O exemplo mais comum é baseado em extensómetros elétricos do tipo resistivo, que aliados a uma estrutura formam uma célula de carga. Este tipo de sensores possui sensibilidades baixas e em repouso, presença de offset diferente de zero, o que torna complexo o seu condicionamento de sinal. Este trabalho apresenta uma solução para o condicionamento e aquisição de dados para células de carga que, tanto quanto foi investigado, é inovador. Este dispositivo permite efetuar o condicionamento de sinal, digitalização e comunicação numa estrutura atómica. A ideia vai de encontro ao paradigma dos sensores inteligentes onde um único dispositivo eletrónico, associado a uma célula de carga, executa um conjunto de operações de processamento de sinal e transmissão de dados. Em particular permite a criação de uma rede ad-hoc utilizando o protocolo de comunicação IIC. O sistema é destinado a ser introduzido numa plataforma de carga, desenvolvida na Escola Superior de Tecnologia e Gestão de Bragança, local destinado à sua implementação. Devido à sua estratégia de conceção para a leitura de forças em três eixos, contém quatro células de carga, com duas saídas cada, totalizando oito saídas. O hardware para condicionamento de sinal já existente é analógico, e necessita de uma placa de dimensões consideráveis por cada saída. Do ponto de vista funcional, apresenta vários problemas, nomeadamente o ajuste de ganho e offset ser feito manualmente, tornando-se essencial um circuito com melhor desempenho no que respeita a lidar com um array de sensores deste tipo.

Introducing mobile edge computing capabilities through distributed 5G Cloud Enabled Small Cells

Current trends in broadband mobile networks are addressed towards the placement of different capabilities at the edge of the mobile network in a centralised way. On one hand, the split of the eNB between baseband processing units and remote radio headers makes it possible to process some of the protocols in centralised premises, likely with virtualised resources. On the other hand, mobile edge computing makes use of processing and storage capabilities close to the air interface in order to deploy optimised services with minimum delay. The confluence of both trends is a hot topic in the definition of future 5G networks. The full centralisation of both technologies in cloud data centres imposes stringent requirements to the fronthaul connections in terms of throughput and latency. Therefore, all those cells with limited network access would not be able to offer these types of services. This paper proposes a solution for these cases, based on the placement of processing and storage capabilities close to the remote units, which is especially well suited for the deployment of clusters of small cells. The proposed cloud-enabled small cells include a highly efficient microserver with a limited set of virtualised resources offered to the cluster of small cells. As a result, a light data centre is created and commonly used for deploying centralised eNB and mobile edge computing functionalities. The paper covers the proposed architecture, with special focus on the integration of both aspects, and possible scenarios of application.

Genetic algorithm seeding of idiotypic networks for mobile-robot navigation

Robot-control designers have begun to exploit the properties of the human immune system in order to produce dynamic systems that can adapt to complex, varying, real-world tasks. Jerne’s idiotypic-network theory has proved the most popular artificial-immune-system (AIS) method for incorporation into behaviour-based robotics, since idiotypic selection produces highly adaptive responses. However, previous efforts have mostly focused on evolving the network connections and have often worked with a single, preengineered set of behaviours, limiting variability. This paper describes a method for encoding behaviours as a variable set of attributes, and shows that when the encoding is used with a genetic algorithm (GA), multiple sets of diverse behaviours can develop naturally and rapidly, providing much greater scope for flexible behaviour-selection. The algorithm is tested extensively with a simulated e-puck robot that navigates around a maze by tracking colour. Results show that highly successful behaviour sets can be generated within about 25 minutes, and that much greater diversity can be obtained when multiple autonomous populations are used, rather than a single one.

Modeling perspective for the relevant market of voice services: mobile to mobile

Los mercados asociados a los servicios de voz móvil a móvil, brindados por operadoras del Sistema Móvil Avanzado en Latinoamérica, han estado sujetos a procesos regulatorios motivados por la dominancia en el mercado de un operador, buscando obtener óptimas condiciones de competencia. Específicamente en Ecuador, la Superintendencia de Telecomunicaciones (Organismo Técnico de Control de Telecomunicaciones) desarrolló un modelo para identificar acciones de regulación que puedan proporcionar al mercado efectos sostenibles de competencia en el largo plazo. Este artículo trata sobre la aplicación de la ingeniería de control para desarrollar un modelo integral del mercado, empleando redes neuronales para la predicción de trarifas de cada operador y un modelo de lógica difusa para predecir la demanda. Adicionalmente, se presenta un modelo de inferencia de lógica difusa para reproducir las estrategias de mercadeo de los operadores y la influencia sobre las tarifas. Dichos modelos permitirían la toma adecuada de decisiones y fueron validados con datos reales.

Mobile marketing process of an international event – Case: Falun2015 FIS Nordic World Ski Championships

Any other technology has never affected daily life at this level and witnessed as speedy adaptation as the mobile phone. At the same time, mobile media has developed to be a serious marketing tool for all kinds of businesses, and the industry has grown explosively in recent years. The objective of this thesis is to inspect the mobile marketing process of an international event. This thesis is a qualitative case study. The chosen case for this thesis is the mobile marketing process of Falun2015 FIS Nordic World Ski Championships due to researcher’s interest on the topic and contacts to the people around the event. The empirical findings were acquired by conducting two interviews with three experts from the case organisation and its partner organisation. The interviews were performed as semi-structured interviews utilising the themes arising from the chosen theoretical framework. The framework distinguished six phases in the process: (i) campaign initiation, (ii) campaign design, (iii) campaign creation, (iv) permission management, (v) delivery, and (vi) evaluation and analysis. Phases one and five were not examined in this thesis because campaign initiation was not purely seen as part of the campaign implementation, and investigating phase five would have required a very technical viewpoint to the study. In addition to the interviews, some pre-established documents were exploited as a supporting data. The empirical findings of this thesis mainly follow the theoretical framework utilised. However, some modifications to the model could be made mainly related to the order of different phases. In the revised model, the actions are categorised depending on the time they should be conducted, i.e. before, during or after the event. Regardless of the categorisation, the phases can be in different order and overlapping. In addition, the business network was highly emphasised by the empirical findings and is thus added to the modified model. Five managerial recommendations can be concluded from the empirical findings of this thesis: (i) the importance of a business network should be highly valued in a mobile marketing process; (ii) clear goals should be defined for mobile marketing actions in order to make sure that everyone involved is aware them; (iii) interactivity should be perceived as part of a mobile marketing communication; (iv) enough time should be allowed for the development of a mobile marketing process in order to exploit all the potential it can offer; and (v) attention should be paid to measuring and analysing matters that are of relevance

DESIGN OF EFFICIENT IN-NETWORK DATA PROCESSING AND DISSEMINATION FOR VANETS

By providing vehicle-to-vehicle and vehicle-to-infrastructure wireless communications, vehicular ad hoc networks (VANETs), also known as the “networks on wheels”, can greatly enhance traffic safety, traffic efficiency and driving experience for intelligent transportation system (ITS). However, the unique features of VANETs, such as high mobility and uneven distribution of vehicular nodes, impose critical challenges of high efficiency and reliability for the implementation of VANETs. This dissertation is motivated by the great application potentials of VANETs in the design of efficient in-network data processing and dissemination. Considering the significance of message aggregation, data dissemination and data collection, this dissertation research targets at enhancing the traffic safety and traffic efficiency, as well as developing novel commercial applications, based on VANETs, following four aspects: 1) accurate and efficient message aggregation to detect on-road safety relevant events, 2) reliable data dissemination to reliably notify remote vehicles, 3) efficient and reliable spatial data collection from vehicular sensors, and 4) novel promising applications to exploit the commercial potentials of VANETs. Specifically, to enable cooperative detection of safety relevant events on the roads, the structure-less message aggregation (SLMA) scheme is proposed to improve communication efficiency and message accuracy. The scheme of relative position based message dissemination (RPB-MD) is proposed to reliably and efficiently disseminate messages to all intended vehicles in the zone-of-relevance in varying traffic density. Due to numerous vehicular sensor data available based on VANETs, the scheme of compressive sampling based data collection (CS-DC) is proposed to efficiently collect the spatial relevance data in a large scale, especially in the dense traffic. In addition, with novel and efficient solutions proposed for the application specific issues of data dissemination and data collection, several appealing value-added applications for VANETs are developed to exploit the commercial potentials of VANETs, namely general purpose automatic survey (GPAS), VANET-based ambient ad dissemination (VAAD) and VANET based vehicle performance monitoring and analysis (VehicleView). Thus, by improving the efficiency and reliability in in-network data processing and dissemination, including message aggregation, data dissemination and data collection, together with the development of novel promising applications, this dissertation will help push VANETs further to the stage of massive deployment.

Prevalence of SOS-mediated control of integron integrase expression as an adaptive trait of chromosomal and mobile integrons

BACKGROUND Integrons are found in hundreds of environmental bacterial species, but are mainly known as the agents responsible for the capture and spread of antibiotic-resistance determinants between Gram-negative pathogens. The SOS response is a regulatory network under control of the repressor protein LexA targeted at addressing DNA damage, thus promoting genetic variation in times of stress. We recently reported a direct link between the SOS response and the expression of integron integrases in Vibrio cholerae and a plasmid-borne class 1 mobile integron. SOS regulation enhances cassette swapping and capture in stressful conditions, while freezing the integron in steady environments. We conducted a systematic study of available integron integrase promoter sequences to analyze the extent of this relationship across the Bacteria domain. RESULTS Our results showed that LexA controls the expression of a large fraction of integron integrases by binding to Escherichia coli-like LexA binding sites. In addition, the results provide experimental validation of LexA control of the integrase gene for another Vibrio chromosomal integron and for a multiresistance plasmid harboring two integrons. There was a significant correlation between lack of LexA control and predicted inactivation of integrase genes, even though experimental evidence also indicates that LexA regulation may be lost to enhance expression of integron cassettes. CONCLUSIONS Ancestral-state reconstruction on an integron integrase phylogeny led us to conclude that the ancestral integron was already regulated by LexA. The data also indicated that SOS regulation has been actively preserved in mobile integrons and large chromosomal integrons, suggesting that unregulated integrase activity is selected against. Nonetheless, additional adaptations have probably arisen to cope with unregulated integrase activity. Identifying them may be fundamental in deciphering the uneven distribution of integrons in the Bacteria domain.

Measuring QoS in an aeronautical opportunistic network architecture with limited access to a satellite communications backhaul

We measure quality of service (QoS) in a wireless network architecture of transoceanic aircraft. A distinguishing characteristic of the network scheme we analyze is that it mixes the concept of Delay Tolerant Networking (DTN) through the exploitation of opportunistic contacts, together with direct satellite access in a limited number of the nodes. We provide a graph sparsification technique for deriving a network model that satisfies the key properties of a real aeronautical opportunistic network while enabling scalable simulation. This reduced model allows us to analyze the impact regarding QoS of introducing Internet-like traffic in the form of outgoing data from passengers. Promoting QoS in DTNs is usually really challenging due to their long delays and scarce resources. The availability of satellite communication links offers a chance to provide an improved degree of service regarding a pure opportunistic approach, and therefore it needs to be properly measured and quantified. Our analysis focuses on several QoS indicators such as delivery time, delivery ratio, and bandwidth allocation fairness. Obtained results show significant improvements in all metric indicators regarding QoS, not usually achievable on the field of DTNs.

Integration of process planning and scheduling using mobile-agent based approach in a networked manufacturing environment

Effective and efficient implementation of intelligent and/or recently emerged networked manufacturing systems require an enterprise level integration. The networked manufacturing offers several advantages in the current competitive atmosphere by way to reduce, by shortening manufacturing cycle time and maintaining the production flexibility thereby achieving several feasible process plans. The first step in this direction is to integrate manufacturing functions such as process planning and scheduling for multi-jobs in a network based manufacturing system. It is difficult to determine a proper plan that meets conflicting objectives simultaneously. This paper describes a mobile-agent based negotiation approach to integrate manufacturing functions in a distributed manner; and its fundamental framework and functions are presented. Moreover, ontology has been constructed by using the Protégé software which possesses the flexibility to convert knowledge into Extensible Markup Language (XML) schema of Web Ontology Language (OWL) documents. The generated XML schemas have been used to transfer information throughout the manufacturing network for the intelligent interoperable integration of product data models and manufacturing resources. To validate the feasibility of the proposed approach, an illustrative example along with varied production environments that includes production demand fluctuations is presented and compared the proposed approach performance and its effectiveness with evolutionary algorithm based Hybrid Dynamic-DNA (HD-DNA) algorithm. The results show that the proposed scheme is very effective and reasonably acceptable for integration of manufacturing functions.

Machine learning techniques for implicit interaction using mobile sensors

Interactions in mobile devices normally happen in an explicit manner, which means that they are initiated by the users. Yet, users are typically unaware that they also interact implicitly with their devices. For instance, our hand pose changes naturally when we type text messages. Whilst the touchscreen captures finger touches, hand movements during this interaction however are unused. If this implicit hand movement is observed, it can be used as additional information to support or to enhance the users’ text entry experience. This thesis investigates how implicit sensing can be used to improve existing, standard interaction technique qualities. In particular, this thesis looks into enhancing front-of-device interaction through back-of-device and hand movement implicit sensing. We propose the investigation through machine learning techniques. We look into problems on how sensor data via implicit sensing can be used to predict a certain aspect of an interaction. For instance, one of the questions that this thesis attempts to answer is whether hand movement during a touch targeting task correlates with the touch position. This is a complex relationship to understand but can be best explained through machine learning. Using machine learning as a tool, such correlation can be measured, quantified, understood and used to make predictions on future touch position. Furthermore, this thesis also evaluates the predictive power of the sensor data. We show this through a number of studies. In Chapter 5 we show that probabilistic modelling of sensor inputs and recorded touch locations can be used to predict the general area of future touches on touchscreen. In Chapter 7, using SVM classifiers, we show that data from implicit sensing from general mobile interactions is user-specific. This can be used to identify users implicitly. In Chapter 6, we also show that touch interaction errors can be detected from sensor data. In our experiment, we show that there are sufficient distinguishable patterns between normal interaction signals and signals that are strongly correlated with interaction error. In all studies, we show that performance gain can be achieved by combining sensor inputs.

The Role of B5G UAV-Aided Mobile Networks

With the advent of 5G, several novel network paradigms and technologies have been proposed to fulfil the key requirements imposed. Flexibility, efficiency and scalability, along with sustainability and convenience for expenditure have to be addressed in targeting these brand new needs. Among novel paradigms introduced in the scientific literature in recent years, a constant and increasing interest lies in the use of unmanned aerial vehicles (UAVs) as network nodes supporting the legacy terrestrial network for service provision. Their inherent features of moving nodes make them able to be deployed on-demand in real-time. Which, in practical terms, means having them acting as a base station (BS) when and where there is the highest need. This thesis investigates then the potential role of UAV-aided mobile radio networks, in order to validate the concept of adding an aerial network component and assess the system performance, from early to later stages of its deployment. This study is intended for 5G and beyond systems, to allow time for the technology to mature. Since advantages can be manyfold, the aerial network component is considered at the network layer under several aspects, from connectivity to radio resource management. A particular emphasis is given to trajectory design, because of the efficiency and flexibility it potentially adds to the infrastructure. Two different frameworks have been proposed, to take into account both a re-adaptable heuristic and an optimal solution. Moreover, diverse use cases are taken under analysis, from mobile broadband to machine and vehicular communications. The thesis aim is thus to discuss the potential and advantages of UAV-aided systems from a broad perspective. Results demonstrate that the technology has good prospects for diverse scenarios with a few arrangements.

Robust Adaptive Control with Applications to Multi-Agent Systems and Mobile Robotics

This thesis deals with robust adaptive control and its applications, and it is divided into three main parts. The first part is about the design of robust estimation algorithms based on recursive least squares. First, we present an estimator for the frequencies of biased multi-harmonic signals, and then an algorithm for distributed estimation of an unknown parameter over a network of adaptive agents. In the second part of this thesis, we consider a cooperative control problem over uncertain networks of linear systems and Kuramoto systems, in which the agents have to track the reference generated by a leader exosystem. Since the reference signal is not available to each network node, novel distributed observers are designed so as to reconstruct the reference signal locally for each agent, and therefore decentralizing the problem. In the third and final part of this thesis, we consider robust estimation tasks for mobile robotics applications. In particular, we first consider the problem of slip estimation for agricultural tracked vehicles. Then, we consider a search and rescue application in which we need to drive an unmanned aerial vehicle as close as possible to the unknown (and to be estimated) position of a victim, who is buried under the snow after an avalanche event. In this thesis, robustness is intended as an input-to-state stability property of the proposed identifiers (sometimes referred to as adaptive laws), with respect to additive disturbances, and relative to a steady-state trajectory that is associated with a correct estimation of the unknown parameter to be found.

Propagation modelling and resource allocation in mobile radio communications

Over the past years, ray tracing (RT) models popularity has been increasing. From the nineties, RT has been used for field prediction in environment such as indoor and urban environments. Nevertheless, with the advent of new technologies, the channel model has become decidedly more dynamic and to perform RT simulations at each discrete time instant become computationally expensive. In this thesis, a new dynamic ray tracing (DRT) approach is presented in which from a single ray tracing simulation at an initial time t0, through analytical formulas we are able to track the motion of the interaction points. The benefits that this approach bring are that Doppler frequencies and channel prediction can be derived at every time instant, without recurring to multiple RT runs and therefore shortening the computation time. DRT performance was studied on two case studies and the results shows the accuracy and the computational gain that derives from this approach. Another issue that has been addressed in this thesis is the licensed band exhaustion of some frequency bands. To deal with this problem, a novel unselfish spectrum leasing scheme in cognitive radio networks (CRNs) is proposed that offers an energy-efficient solution minimizing the environmental impact of the network. In addition, a network management architecture is introduced and resource allocation is proposed as a constrained sum energy efficiency maximization problem. System simulations demonstrate an increment in the energy efficiency of the primary users’ network compared with previously proposed algorithms.

Integrated communication and learning towards autonomous mobile radio networks

The integration of distributed and ubiquitous intelligence has emerged over the last years as the mainspring of transformative advancements in mobile radio networks. As we approach the era of “mobile for intelligence”, next-generation wireless networks are poised to undergo significant and profound changes. Notably, the overarching challenge that lies ahead is the development and implementation of integrated communication and learning mechanisms that will enable the realization of autonomous mobile radio networks. The ultimate pursuit of eliminating human-in-the-loop constitutes an ambitious challenge, necessitating a meticulous delineation of the fundamental characteristics that artificial intelligence (AI) should possess to effectively achieve this objective. This challenge represents a paradigm shift in the design, deployment, and operation of wireless networks, where conventional, static configurations give way to dynamic, adaptive, and AI-native systems capable of self-optimization, self-sustainment, and learning. This thesis aims to provide a comprehensive exploration of the fundamental principles and practical approaches required to create autonomous mobile radio networks that seamlessly integrate communication and learning components. The first chapter of this thesis introduces the notion of Predictive Quality of Service (PQoS) and adaptive optimization and expands upon the challenge to achieve adaptable, reliable, and robust network performance in dynamic and ever-changing environments. The subsequent chapter delves into the revolutionary role of generative AI in shaping next-generation autonomous networks. This chapter emphasizes achieving trustworthy uncertainty-aware generation processes with the use of approximate Bayesian methods and aims to show how generative AI can improve generalization while reducing data communication costs. Finally, the thesis embarks on the topic of distributed learning over wireless networks. Distributed learning and its declinations, including multi-agent reinforcement learning systems and federated learning, have the potential to meet the scalability demands of modern data-driven applications, enabling efficient and collaborative model training across dynamic scenarios while ensuring data privacy and reducing communication overhead.

Comparison between 5G private network and Wifi6 in industrial environment

Industry 4.0 refers to the 4th industrial revolution and at its bases, we can see the digitalization and the automation of the assembly line. The whole production process has improved and evolved thanks to the advances made in networking, and AI studies, which include of course machine learning, cloud computing, IoT, and other technologies that are finally being implemented into the industrial scenario. All these technologies have in common a need for faster, more secure, robust, and reliable communication. One of the many solutions for these demands is the use of mobile communication technologies in the industrial environment, but which technology is better suited for these demands? Of course, the answer isn’t as simple as it seems. The 4th industrial revolution has a never seen incomparable potential with respect to the previous ones, every factory, enterprise, or company have different network demands, and even in each of these infrastructures, the demands may diversify by sector, or by application. For example, in the health care industry, there may be e a need for increased bandwidth for the analysis of high-definition videos or, faster speeds in order to have analytics occur in real-time, and again another application might be higher security and reliability to protect patients’ data. As seen above, choosing the right technology for the right environment and application, considers many things, and the ones just stated are but a speck of dust with respect to the overall picture. In this thesis, we will investigate a comparison between the use of two of the available technologies in use for the industrial environment: Wi-Fi 6 and 5G Private Networks in the specific case of a steel factory.