Repositório RCAAP

Industry 4.0 brings in a whole set of new requirements to engineering industrial systems, with notorious impact at the networking layer. A key challenge posed by Industry 4.0 is the operational flexibility needed to support on-the-fly reconfiguration of production cells, stations, and machines. At the networking layer, this flexibility implies dynamic packet handling, scheduling, and dispatching. SoftwareDefined Networking (SDN) provides this level of flexibility in the general Local Area Network (LAN) domain. However, its application in the industry has been hindered by a lack of support for real-time services. This paper addresses this limitation, proposing an extended SDN OpenFlow framework that includes realtime services, leveraging existing real-time data plane Ethernet technologies. We show the OpenFlow enhancements, a real-time SDN controller, and experimental validation and performance assessment. Using a proof-of-concept prototype with 3 switches and cycles of 250µs, we could achieve 1µs jitter on timetriggered traffic and a reconfiguration time between operational modes below 10ms

The increasing use of mobile cooperative robots in a variety of applications also implies an increasing research effort on cooperative strategies solutions, typically involving communications and control. For such research, simulation is a powerful tool to quickly test algorithms, allowing to do more exhaustive tests before implementation in a real application. However, the transition from an initial simulation environment to a real application may imply substantial rework if early implementation results do not match the ones obtained by simulation, meaning the simulation was not accurate enough. One way to improve accuracy is to incorporate network and control strategies in the same simulation and to use a systematic procedure to assess how different techniques perform. In this paper, we propose a set of procedures called Integrated Robotic and Network Simulation Method (IRoNS Method), which guide developers in building a simulation study for cooperative robots and communication networks applications. We exemplify the use of the improved methodology in a case-study of cooperative control comparison with and without message losses. This case is simulated with the OMNET++/INET framework, using a group of robots in a rendezvous task with topology control. The methodology led to more realistic simulations while improving the results presentation and analysis.

One thing can be said to be common to all forms of debit card fraud –authentication bypass. This implies that a secure debit card transaction system can only be guaranteed by a safe and reliable authentication system. Many approaches have been adopted to ensure a secure authentication system, but often, these approaches are either focused on the Automated Teller Machines (ATM)/Point of Sales (POS) terminals or Online/e-commerce transactions, thus not providing full security on both fronts. In this work, we address this problem by adopting a multi-factor debit card system that uses a combination of the traditional Personal Identification Number code (PIN) and the mobile-phone delivered One-Time Password (OTP) with a biometric authentication option(fingerprint). We demonstrate that this approach ensures the security of both online and terminal transactions. The fingerprint option makes it easy to use by people who find memorizing PINs difficult.

Recent advancements in the IoT domain have been pushing for stronger demands of Qualityof-Service (QoS) and in particular for improved determinism for time-critical wireless communications under power constraints. The IEEE 802.15.4e standard protocol introduced several new MAC behaviors that provide enhanced time-critical and reliable communications. The Deterministic Synchronous Multichannel Extension (DSME) is one of its prominent MAC behaviors that combines contention-based and contentionfree communication, guaranteeing bounded delays and improved reliability and scalability by leveraging multi-channel access and CAP reduction. However, DSME has a multi-superframe structure, which is statically defined at the beginning of the network. As the network evolves dynamically by changing its traffic characteristics, these static settings can affect the overall throughput and increase the network delay because of improper allocation of bandwidth. In this paper, we address this problem, and we present a dynamic multi-superframe tuning technique that dynamically adapts the multi-superframe structure based on the size of the network. This technique improves the QoS by providing 15-30% increase in throughput and 15-35% decrease in delay when compared to static DSME networks

Unmanned Aerial Vehicles (UAVs) with Microwave Power Transfer (MPT) capability provide a practical means to deploy a large number of wireless powered sensing devices into areas with no access to persistent power supplies. The UAV can charge the sensing devices remotely and harvest their data. A key challenge is online MPT and data collection in the presence of on-board control of a UAV (e.g., patrolling velocity) for preventing battery drainage and data queue overflow of the devices, while up-to-date knowledge on battery level and data queue of the devices is not available at the UAV. In this paper, an on-board deep Q-network is developed to minimize the overall data packet loss of the sensing devices, by optimally deciding the device to be charged and interrogated for data collection, and the instantaneous patrolling velocity of the UAV. Specifically, we formulate a Markov Decision Process (MDP) with the states of battery level and data queue length of devices, channel conditions, and waypoints given the trajectory of the UAV; and solve it optimally with Q-learning. Furthermore, we propose the on-board deep Q-network that enlarges the state space of the MDP, and a deep reinforcement learning based scheduling algorithm that asymptotically derives the optimal solution online, even when the UAV has only outdated knowledge on the MDP states. Numerical results demonstrate that our deep reinforcement learning algorithm reduces the packet loss by at least 69.2%, as compared to existing non-learning greedy algorithms.

Unmanned Aerial Vehicles are increasingly being used in surveillance and traffic monitoring thanks to their high mobility and ability to cover areas at different altitudes and locations. One of the major challenges is to use aerial images to accurately detect cars and count-them in real-time for traffic monitoring purposes. Several deep learning techniques were recently proposed based on convolution neural network (CNN) for real-time classification and recognition in computer vision. However, their performance depends on the scenarios where they are used. In this paper, we investigate the performance of two state-of-the art CNN algorithms, namely Faster R-CNN and YOLOv3, in the context of car detection from aerial images. We trained and tested these two models on a large car dataset taken from UAVs. We demonstrated in this paper that YOLOv3 outperforms Faster R-CNN in sensitivity and processing time, although they are comparable in the precision metric.

The following topics are dealt with: mobile robots; multi-robot systems; path planning; robot vision; service robots; collision avoidance; learning (artificial intelligence); legged locomotion; control engineering computing; production engineering computing.

Building and maintaining complex systems requires good software engineering practices, including code modularity and reuse. The same applies in the context of coordination of complex component-based systems. This paper investigates how to verify properties of complex coordination patterns built hierarchically, i.e., built from composing blocks that are in turn built from smaller blocks. Most existing approaches to verify properties flatten these hierarchical models before the verification process, losing the hierarchical structure. We propose an approach to verify hierarchical models using containers as actions; more concretely, containers interacting with their neighbours. We present a dynamic modal logic tailored for hierarchical connectors, using Reo and Petri Nets to illustrate our approach. We realise our approach via a prototype implementation available online to verify hierarchical Reo connectors, encoding connectors and formulas into mCRL2 specifications and formulas

The commodity Single Board Computers (SBCs) are increasingly becoming powerful and can execute standard operating systems and mainstream workloads. In the context of cloud-based smart city applications, SBCs can be utilized as Edge computing devices reducing the network communication. In this paper, we investigate the design and implementation of a SBC based edge cluster (SBC-EC) framework for a smart parking application. Since SBCs are resource constrained devices, we devise a container based framework for a lighter foot-print. Kubernetes was used as an orchestration tool to orchestrate various containers in the framework. To validate our approach, we implemented a proof-of-concept of the SBC based Edge cluster for a smart parking application, as a possible ioT use-case.Our implementation shows that, the use of SBC devices at the edge of a cloud based smart parking application is a cost effective and low energy, green computing solution. The proposed framework can be extended to similar cloud based applications in the context of a smart city.

Most recurrent real-time applications can be modeled as a set of sequential code segments (or blocks) that must be (repeatedly) executed in a specific order. This paper provides a schedulability analysis for such systems modeled as a set of parallel DAG tasks executed under any limited-preemptive global job-level fixed priority scheduling policy. More precisely, we derive response-time bounds for a set of jobs subject to precedence constraints, release jitter, and execution-time uncertainty, which enables support for a wide variety of parallel, limited-preemptive execution models (e.g., periodic DAG tasks, transactional tasks, generalized multi-frame tasks, etc.). Our analysis explores the space of all possible schedules using a powerful new state abstraction and state-pruning technique. An empirical evaluation shows the analysis to identify between 10 to 90 percentage points more schedulable task sets than the state-of-the-art schedulability test for limited-preemptive sporadic DAG tasks. It scales to systems of up to 64 cores with 20 DAG tasks. Moreover, while our analysis is almost as accurate as the state-of-the-art exact schedulability test based on model checking (for sequential non-preemptive tasks), it is three orders of magnitude faster and hence capable of analyzing task sets with more than 60 tasks on 8 cores in a few seconds.

This work presents different approaches to calculate CPRO for set-associative caches. The PCB-ECB approach uses PCBs of the task under analysis and ECBs of all other tasks in the system to provide sound estimates of CPRO for set-associative caches. The resilienceP analysis then removes some of the pessimism in the PCB-ECB approach by considering the resilience of PCBs during CPRO calculations. We show that using the state-of-the-art (SoA) resilience analysis to calculate resilience of PCBs may result in underestimating the CPRO tasks may suffer. Finally, we have also presented a multi-set alike resilienceP analysis that highlights the pessimism in the resilienceP analysis and provides some insights on how it can be removed.

The advent of multi-core platforms in critical realtime domains such as the avionics, automotive and railways to achieve higher and higher computing performances has turned the view on thermal concerns of the underlying chip die while it is still mandatory to meet all the temporal constraints. As a matter of fact, high chip temperature may not only degrade system performance and reliability, but it may also damage the chip permanently. We propose a methodology to address this problem, based on fixed task-to-core mapping and per-core analysis to derive a sound system model without feedback loops. To this end, it is important to have a better and deeper understanding of the existing thermal models in the literature. This is the main contribution of this research.

In order to communicate, cores of a multi-core platform traditionally relied on shared busses. However, with the increasing number of computation nodes integrated in multi- and many-core platforms, Network-on-Chips (NoCs) emerged as a new alternative communication medium in Systems-on-Chips (SoCs). Hoplite-RT is a new NoC design that was recently proposed. Hoplite-RT is a compact design easy to analyze and with a low-cost implementation that was specifically tailored for FPGA. In this work, we introduce priority-based routing to Hoplite-RT and change the network topology so as to improve its timing behavior, i.e., its Worst-Case Traversal Time (WCTT).

Timing analysis of safety-critical real-time embedded systems should be free of both optimistic and pessimistic aspects. The multiframe model was devised to eliminate the pessimism in the schedulability analysis of systems with tasks whose worst-case execution times vary from job to job, according to known patterns. However, this model is optimistic and unsafe for multicores with shared memory controllers, since it ignores memory contention, and existing approaches to stall analysis based on memory regulation are very pessimistic if straightforwardly applied. This paper remedies this by adapting existing stall analyses for memory-regulated systems of conventional Liu-and-Layland tasks to the multiframe model. Experimental evaluations with synthetic task sets (and different task and memory budget assignment heuristics) show up to 85% higher scheduling success ratio for our analysis, compared to the frameagnostic analysis, enabling higher platform utilisation without compromising safety. We also explore implementation aspects, such as how to speed up the analysis and how to trade off accuracy with tractability.

This work explores the impact of antenna heights and polarization on overwater links during the cycle of tidal variations. We focus our attention on links of short-to-medium-range distances with antenna heights near-to-the-water-surface. The typical use-case for such a scenario is an overwater, water quality monitoring wireless sensor network. The radio propagation is simulated using a featured two-ray model that considers the relative permittivity of the water surface and the antenna polarization. The results show that the performance of overwater links may be better with lower antennas than higher antennas as well as with one polarization or the other, intuitively, during part of the tidal cycle.

This book describes the Optical Immersion Clearing method and its application to acquire information with importance for clinical practice and various fields of biomedical engineering. The method has proved to be a reliable means of increasing tissue transparency, allowing the investigator or surgeon to reach deeper tissue layers for improved imaging and laser surgery. This result is obtained by partial replacement of tissue water with an active optical clearing agent (OCA) that has a higher refractive index and is a better match for the refractive index of other tissue components. Natural tissue scattering is thereby reduced. An exponential increase in research using this method has occurred in recent years, and new applications have emerged, both in clinical practice and in some areas of biomedical engineering. Recent research has revealed that treating ex vivo tissues with solutions containing active OCAs in different concentrations produces experimental data to characterize drug delivery or to discriminate between normal and pathological tissues. The obtained drug diffusion properties are of interest for the pharmaceutical and organ preservation industry. Similar data can be estimated with particular interest for food preservation. The free water content evaluation is also of great interest since it facilitates the characterization of tissues to discriminate pathologies. An interesting new application that is presented in the book regards the creation of two optical windows in the ultraviolet spectral range through the application of the immersion method. These induced transparency windows open the possibility to diagnose and treat pathologies with ultraviolet light. This book presents photographs from the tissues we have studied and figures that represent the experimental setups used. Graphs and tables are also included to show the numerical results obtained in the sequential calculations performed.

This paper presents the analysis of the statistics of latency and information theoretic capacity of an adaptive link with retransmission-spatial diversity in a scenario with co-channel interference. The paper focuses specifically on the delay of the wireless transmission component, measured from the instant a packet at the head of the queue is first transmitted until it is correctly received by the destination (considering retransmissions). The objective is to evaluate the ability of temporal and spatial diversity tools to achieve ultra-low values of latency as desired in future 5G and machine-to-machine (M2M) networks with real-time requirements. It is assumed that the source transmits information towards the destination in a Rayleigh fading spatially correlated channel. In case the instantaneous signal-to-interference-plus-noise (SINR) ratio has not surpassed a predetermined reception threshold, then the source engages in a persistent retransmission protocol. All the copies of the original transmission and subsequent retransmissions are stored in memory and processed at the destination using maximum ratio combining (MRC) to obtain a more reliable copy of the signal (a scheme also called retransmission diversity). The retransmission scheme stops once the instantaneous post-processing SINR achieves the desired target threshold. This persistent retransmission scheme can also be regarded as a security mechanism against interference jamming attacks. Since retransmissions are assumed to take place in a short time interval in order to achieve very low values of latency, they are modelled with statistical temporal correlation, which is explicitly introduced in the embedded Gaussian channel distribution model. Results suggest that retransmission diversity can provide good latency results in moderate to high values of SINR. However, at low SINR, a combination with other diversity sources will be necessary to achieve the desired target value.

This paper presents the derivation new expressions for the statistics of a Chi-square distribution with $n$ degrees of freedom and where n is an even number. The complex Gaussian components of the chi-square distribution are modelled with a linear correlated model using different statistics (multi-rate) for each component. We focus on the specific expressions for the probability density function (PDF) and complementary cumulative density function (CCDF). Unlike previous approaches, we use a frequency domain interpretation that allows us to derive a closed form expression for the characteristic function (CF) as an inverse polynomial equation. Using the roots of this polynomial equation, it is possible to decompose the CF as a partial fraction expansion (PFE). This allows us to obtain a simple expression for both the PDF and CCDF by simply using the inverse Fourier transform of PFE decomposition of the CF. The statistics derived here have a much lower complexity than the expressions obtained from conventional non-frequency domain methods at the expense of the complexity of the polynomial root solution scheme. In scenarios where the average statistics of the components do not change over some periods of time, the proposed expressions provide the lowest possible complexity, as the polynomial rooting process needs to be conducted only once and potentially offline.

The very particular characteristics of electricity markets, require deep studies of the interactions between the involved players. MASCEM is a market simulator developed to allow studying electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is implemented as a multiagent system, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. This paper also presents a methodology to define players’ models based on the historic of their past actions, interpreting how their choices are affected by past experience, and competition.

O mundo do trabalho é cada vez mais imprevisível, complexo e instável, o que coloca crescentes desafios às organizações e às pessoas que nelas trabalham. Às organizações exige-se o aumento da sua competitividade e da sua agilidade para responder às constantes mudanças. Simultaneamente, aos colaboradores exigem-se cada vez mais competências de modo a contribuírem para o sucesso organizacional. No equilíbrio entre os interesses das organizações e o bem-estar e produtividade dos colaboradores, situamos a inteligência emocional e o engagement no trabalho. Se por um lado a inteligência emocional tem sido apontada como preditora do engagement no trabalho, é sabido que este está associado tanto ao bem-estar do indivíduo como ao sucesso organizacional. O objetivo principal da presente investigação é analisar a relação entre a inteligência emocional e o engagement no trabalho. Pretende-se também analisar a associação entre variáveis sociodemográficas e profissionais com a inteligência emocional e com o engagement no trabalho. Participaram no estudo 161 colaboradores de três empresas de diferentes sectores de atividade que responderam à escala de avaliação das emoções (EAE; Schutte et al., 1998) e ao questionário de engagement no trabalho (UWES; Schaufeli et Bakker, 2004). O principal resultado encontrado permite-nos concluir que existe uma relação positiva entre a inteligência emocional e o engagement no trabalho, e que as habilitações literárias se relacionam positivamente com a inteligência emocional. Constata-se ainda que a inteligência emocional foi capaz de predizer 29% da variância do engagement no trabalho. Estes resultados apontam para importantes implicações para a prática, pois contribui para dotar as organizações de conhecimento orientador do desenvolvimento de políticas de recursos humanos que conjugam os interesses da organização com o bem-estar dos seus colaboradores.