Angelo Cenedese

We address modeling and control of a gate access automation system. A model of the mechatronic system is derived and identified. Then, an approximate explicit feedback linearization scheme is proposed, which ensures almost linear response between the electronic driver duty cycle input and the delivered torque. A nonlinear optimization problem is solved offline to generate a feasible trajectory associated with a feedforward action, and a low-level feedback controller is designed to track it. The feedback gains can be conveniently tuned by solving a set of convex linear matrix inequalities, performing a multiobjective tradeoff between disturbance attenuation and transient response. The proposed control strategy is tested on an industrial device. The experiments show that it can effectively meet the requirements in terms of robustness, load disturbance rejection, and tracking performance.

Visual sensor networks (VSNs) constitute a fundamental class of distributed sensing systems, with unique complexity and appealing performance features, which correspondingly bring in quite active lines of research. An important research direction consists in the identification and estimation of the VSN sensing features: these are practically useful when scaling with the number of cameras or with the observed scene complexity. With this context in mind, this paper introduces for the first time the idea of Stimulation Model (SM), as a mathematical relation between the set of detectable events and the corresponding stimulated cameras observing those events. The formulation of the related SM identification problem is proposed, along with a proper network observations model, and a solution approach based on deep embedded features and soft clustering. In detail: first, the Gaussian Mixture Modeling is employed to provide a suitable description for data distribution, while an autoencoder is used to reduce undesired effects due to the so-called curse of dimensionality emerging in case of large scale networks. Then, it is shown that a SM can be learnt by solving Maximum A-Posteriori estimation on the encoded features belonging to a space with lower dimensionality. Numerical results on synthetic scenarios are reported to validate the devised estimation algorithm.

The recent trend toward the development of IoT architectures has entailed the transformation of the standard camera networks into smart multi-device systems, capable of acquiring, elaborating, exchanging data and, often, dynamically adapting to the environment. Along this line, this work proposes a novel distributed solution that guarantees the real-time monitoring of 3D indoor structured areas and also the tracking of multiple targets, by employing an heterogeneous visual sensor network composed of both fixed and Pan-Tilt-Zoom (PTZ) cameras. Specifically, the fulfilment of the twofold mentioned goal is ensured through the implementation of a suitable optimization procedure regarding the PTZ devices controllable parameters, inspired by game theory. Numerical simulations in realistic scenarios confirm the capability of the outlined strategy of securing the simultaneous tracking of several targets, maintaining the total coverage of the surveilled area. In particular, the proposed solution results to be effective in dealing with conflicting goals like achieving a good tracking precision while obtaining high resolution frames of the tracked subjects.

This work presents a novel general regularized distributed solution for the state estimation problem in networked systems. Resting on the graph-based representation of sensor networks and adopting a multivariate least-squares approach, the designed solution exploits the set of the available inter-sensor relative measurements and leverages a general regularization framework, whose parameter selection is shown to control the estimation procedure convergence performance. As confirmed by the numerical results, this new estimation scheme allows (i) the extension of other approaches investigated in the literature and (ii) the convergence optimization in correspondence to any (undirected) graph modeling the given sensor network.

This work focuses on bearing rigidity theory, namely the branch of knowledge investigating the structural properties necessary for multi-element systems to preserve the inter-unit bearings under deformations. The contributions of this work are twofold. The first one consists in the development of a general framework for the statement of the principal definitions and properties of bearing rigidity. We show that this approach encompasses results existing in the literature, and also provides a systematic approach for studying bearing rigidity on any differential manifold in SE(3)^n, where n is the number of agents.The second contribution is the derivation of a general form of the rigidity matrix, a central construct in the study of rigidity theory. We provide a necessary and sufficient condition for the infinitesimal rigidity of a bearing framework as a property of the rank of the rigidity matrix. Finally, we present two examples of multi-agent systems not encountered in the literature and we study their rigidity properties using the developed methods

This work proposes a formation control law for multi-agent systems whose components are heterogeneous in terms of actuation capabilities, but at the same time are all able to retrieve bearing information w.r.t. some neighbors in the group. The designed controller exploits the results of the bearing rigidity theory deriving from the modeling of heterogeneous formations as generalized frameworks. The outlined solution is compared with a leader-follower combination of existing rigidity based homogeneous formation controllers in order to highlight the easy tuning, the flexibility w.r.t. the formation composition, and the increased efficiency of the new proposed control approach. A sufficient condition ensuring the convergence of the designed controller is also given.

Visual inspection has recently gained increasing importance in the manufacturing industry and is often addressed by means of learning methodologies applied to data obtained from specific lighting and camera system setups. The industrial scenario becomes particularly challenging when the inspection regards reflective objects, which may affect both the data acquisition and the classification decision process, thus limiting the overall performance. In this context, we observe that the dynamics of the reflected light is the key aspect to characterize these surfaces and needs to be accurately exploited to improve the performances of the learning algorithms. To this aim, we propose a combined model-based and data-driven approach designed to detect defects on the reflective surfaces of industrial products, captured as video sequences under coaxial structured illumination. Specifically, a tunable spatial-temporal descriptor of the evolution of the reflected light (Dynamic Evolution of the Light, DEL) is designed and employed within a Hybrid Learning (HL) framework, where the learning process of a Convolutional Neural Network (CNN) is driven by the model-based descriptor. This approach is also extended by adopting the similar in nature descriptor Dynamic Image. The proposed HL solutions are validated against a whole spectrum of state-of-the-art learning procedures and different descriptors. Experiments run on a dataset coming from an actual industrial scenario confirm the ability of DEL to accurately characterize reflective surfaces and the validity of the HL method, which shows remarkably better performance in fault detection even with respect to modern 3D- CNNs with comparable computational effort.

The transportation of large payloads can be made possible with Multi-Robot Systems(MRS) implementing cooperative strategies. In this work, we focus on the coordinated MRS trajectory planning task exploiting a Model Predictive Control (MPC) framework addressing both the actingrobots and the transported load. In this context, the main challenge is the possible occurrence of a temporary mismatch among agents’ actions with consequent formation errors that can cause severe damage to the carried load. To mitigate this risk, the coordination scheme may leverage a leader–follower approach, in which a hierarchical strategy is in place to trade-off between the task accomplishment and the dynamics and environment constraints. Nonetheless, particularly in narrow spaces or cluttered environments, the leader’s optimal choice may lead to trajectories that are infeasible for the follower and the load. To this aim, we propose a feasibility-aware leader–follower strategy, where the leader computes a reference trajectory, and the follower accounts for its own and the load constraints; moreover, the follower is able to communicate the trajectory infeasibility to the leader, which reacts by temporarily switching to a conservative policy. The consistent MRS co-design is allowed by the MPC formulation, for both the leader and the follower: here, the prediction capability of MPC is key to guarantee a correct and efficient execution of the leader–follower coordinated action. The approach is formally stated and discussed, and a numerical campaign is conducted to validate and assess the proposed scheme, with respect to different scenarios with growing complexity.

The functional architecture of the resting brain, as measured with the blood oxygenation level-dependent functional connectivity (BOLD-FC), is slightly modified during task performance. In previous work, we reported behaviorally relevant BOLD-FC modulations between visual and dorsal attention regions when subjects performed a visuospatial attention task as compared to central fixation (Spadone et al., 2015).

Here we use magnetoencephalography (MEG) in the same group of subjects to identify the electrophysiological correlates of the BOLD-FC modulation found in our previous work. While BOLD-FC topography, separately at rest and during visual attention, corresponded to neuromagnetic Band-Limited Power (BLP) correlation in the alpha and beta bands (8-30 Hz), BOLD-FC modulations evoked by performing the visual attention task (Spadone et al. 2015) did not match any specific oscillatory band BLP modulation. Conversely, following the application of an orthogonal spatial decomposition that identifies common inter-subject co-variations, we found that attention–rest BOLD-FC modulations were recapitulated by multi-spectral BLP-FC components. Notably, individual variability of alpha connectivity between Frontal Eye Fields and visual occipital regions, jointly with decreased interaction in the Visual network, correlated with visual discrimination accuracy. In summary, task-rest BOLD connectivity modulations match multi-spectral MEG BLP connectivity.

Research on connected vehicles represents a continuously evolving technological domain, fostered by the emerging Internet of Things (IoT) paradigm and the recent advances in intelligent transportation systems. Nowadays, vehicles are platforms capable of generating, receiving and automatically act based on large amount of data. In the context of assisted driving, connected vehicle technology provides real-time information about the surrounding traffic conditions. Such information is expected to improve drivers' quality of life, for example, by adopting decision making strategies according to the current parking availability status. In this context, we propose an online and adaptive scheme for parking availability mapping. Specifically, we adopt an information-seeking active sensing approach to select the incoming data, thus preserving the onboard storage and processing resources; then, we estimate the parking availability through Gaussian Process Regression. We compare the proposed algorithm with several baselines, which attain inferior performance in terms of mapping convergence speed and adaptivity capabilities; moreover, the proposed approach comes at the cost of a very small computational demand.

Active Search and Tracking for search and rescue missions or collaborative mobile robotics relies on the actuation of a sensing platform to detect and localize a target. In this paper we focus on visually detecting a radio-emitting target with an aerial robot equipped with a radio receiver and a camera. Visual-based tracking provides high accuracy, but the directionality of the sensing domain often requires long search times before detecting the target. Conversely,radio signals have larger coverage, but lower tracking accuracy. Thus, we design a Recursive Bayesian Estimation scheme that uses camera observations to refine radio measurements. To regulate the camera pose, we design an optimal controller whose cost function is built upon a probabilistic map. Theoretical results support the proposed algorithm, while numerical analyses show higher robustness and efficiency with respect to visual and radio-only baselines.

Research on wireless sensors represents a continuously evolving technological domain thanks to their high flexibility and scalability, fast and economical deployment, pervasiveness in industrial, civil and domestic contexts. However, the maintenance costs and the sensors reliability are strongly affected by the battery lifetime, which may limit their use. In this paper we consider a wireless smart camera, equipped with a low-energy radio receiver, and used to visually detect a moving radio-emitting target. To preserve the camera lifetime without sacrificing the detection capabilities, we design a probabilistic energy-aware controller to switch on/off the camera. The radio signal strength is used to predict the target detectability, via self-supervised Gaussian Process Regression combined with Recursive Bayesian Estimation. The automatic training process minimizes the human intervention, while the controller guarantees high detection accuracy and low energy consumption, as numerical and experimental results show.

Multi-modal Probabilistic Active Sensing (MMPAS) uses sensor fusion and probabilistic models to control the perception process of robotic sensing platforms. MMPAS is successfully employed in environmental exploration, collaborative mobile robotics, and target tracking, being fostered by the high performance guarantees on autonomous perception. In this context, we propose a bi-Radio-Visual PAS scheme to solve the transmitter discovery problem. Specifically, we firstly exploit the correlation between radio and visual measurements to learn a target detection model in a self-supervised manner. Then, the model is combined with antenna radiation anisotropies into a Bayesian Optimization framework that controls the platform. We show that the proposed algorithm attains an accuracy of 92%, overcoming two other probabilistic active sensing baselines.

In the world of autonomous driving high resolution maps play a fundamental role. Such maps are highly accurate representations of the environment and are essential for all the algorithms of strategy and path planning operations.

Unfortunately it is not always possible to guarantee the total reliability of these maps and therefore it is necessary to introduce a system for its validation. In this paper we introduce a framework for validating map data at run-time based on probabilistic graphical models. Results from simulations show the capabilities of the proposed approach and highlight the need to find an appropriate balance between model accuracy and complexity.

The Industrial IoT (IIoT) paradigm represents an attractive opportunity for new generation measurement applications, which are increasingly based on efficient and reliable communication systems to allow the extensive availability of continuous data from instruments and/or sensors, thus enabling real-time measurement analysis. Nevertheless, different communication systems and heterogeneous sensors and acquisition systems may be found in an IIoT-enabled measurement application, so that solutions need to be defined to tackle the issue of seamless, effective, and low-latency interoperability. A significant and appropriate solution is the Open Platform Communications (OPC) Unified Architecture (UA) protocol, thanks to its object–oriented structure that allows a complete contextualization of the information. The intrinsic complexity of OPC UA, however, imposes a meaningful performance assessment to evaluate its suitability in the aforementioned context. To this aim, this paper presents the design of a general yet accurate and reproducible measurement setup that will be exploited to assess the performance of the main open source implementations of OPC UA. The final goal of this work is to provide a characterization of the impact of this protocol stack in an IIoT-enabled Measurement System, in particular in terms of both the latency introduced in the measurement process and the power consumption.

Cooperative robotics is a trending topic nowadays as it makes possible a number of tasks that cannot be performed by individual robots, such as heavy payload transportation and agile manipulation. In this work, we address the problem of cooperative transportation by heterogeneous, manipulatorendowed robots. Specifically, we consider a generic number of robotic agents simultaneously grasping an object, which is to be transported to a prescribed set point while avoiding obstacles. The procedure is based on a decentralized leader-follower Model Predictive Control scheme, where a designated leader agent is responsible for generating a trajectory compatible with its dynamics, and the followers must compute a trajectory for their own manipulators that aims at minimizing the internal forces and torques that might be applied to the object by the different grippers. The Model Predictive Control approach appears to be well suited to solve such a problem, because it provides both a control law and a technique to generate trajectories, which can be shared among the agents. The proposed algorithm is implemented using a system comprised of a ground and an aerial robot, both in the robotic Gazebo simulator as well as in experiments with real robots, where the methodological approach is assessed and the controller design is shown to be effective for the cooperative transportation task.

In aerial robotics, path following constitutes a popular
      task requiring a vehicle to pursue a given trajectory.
      Resting upon the fulfillment of a desired time law,
      trajectory tracking techniques often turn out to be
      ineffective in presence of external disturbances, favoring
      the adoption of maneuver regulation strategies wherein the
      desired trajectory is parameterized in terms of the
      path-variable. In this scenario, this work proposes a new
      delay-compensating maneuver regulation controller for fully
      actuated aerial vehicles, whose aim is to guarantee the
      perfect tracking of a given path in the shortest time
      interval. The innovative aspect of such a solution relies
      on the introduction of a recovery term that compensates for
      possible delays in
      the task execution. In addition, the dual-quaternion
      formalism is adopted to model the dynamics of the aerial
      platforms allowing feedback linearize the whole system,
      including both position and attitude, with a single
      controller. The tests conducted in Gazebo physic simulator
      show that the proposed controller outperforms the popular
      trajectory tracking PID regulators.

This paper presents an innovative mechatronic design of a high-accuracy pointing mechanism for orbital laser communication terminals. The system is based on a dual-stage architecture and is miniaturized to fit nanosatellite-class spacecraft, aiming to enable optical communication on small-size space platforms. The focus is on control design aspects and on the performance assessment of an experimental prototype under emulated external environmental disturbances.

Solutions for the quality control of metallic surfaces are proposed. Specifically, we study a deflectometric apparatus based on coaxial structured light and the related algorithmic procedure, which is able to detect the faulty surface of a sample captured by a video sequence. First, by considering the metallic surface a dynamic scene illuminated under different light conditions, we develop the descriptor residuals of linear evolution of light (RLEL) that extracts the defectiveness information starting from the movement of the object without explicitly considering the physical characteristics of the light structure. Then, leveraging on RLEL, we present a hybrid learning (HL) technique capable of overcoming the data-driven approach used in classic deep learning (DL). By exploiting a multisteps training process, we combine the model-based descriptor RLEL and a classical data-driven convolutional neural network (CNN) to obtain an unconventional gray-box CNN, which exceeds the performance of popular DL solutions such as 3-D-inception and 3-D-residual DL networks. Remarkably, HL also shows its validity in comparing the performance of the same network structures trained not in a hybrid way, namely without the injection of the model-based information given by RLEL.

The number of applications involving unmanned aerial vehicles (UAVs) grew dramatically during the last decade. Despite such incredible success, the use of drones is still quite limited in GNSS denied environment: indeed, the availability of a reliable GNSS estimates of the drone position is still fundamental in order to enable most of the UAV applications. Given such motivations, in this paper an alternative positioning system for UAVs, based on low cost ultra-wideband band (UWB) is considered. More specifically, this work aims at assessing the positioning accuracy of UWB-based positioning thanks to the comparison with positions provided by a motion capture (MoCap) system. Since the MoCap accuracy is much higher than that of the UWB system, it can be safely used as a reference trajectory for the validation of UWB estimates. In the considered experiment the UWB system allowed to obtain a root mean square error of 39.4?cm in 3D positioning based on the use of an adaptive extended Kalman filter, where the measurement noise covariance was adaptively estimated.

We propose a non-linear model predictive scheme for planning fuel efficient maneuvers of small spacecrafts that shall rendezvous space debris. The paper addresses the specific issues of potential limited on-board computational capabilities and low-thrust actuators in the chasing spacecraft, and solves them by using a novel MatLab-based toolbox for real-time non-linear model predictive control (MPC) called MATMPC. This tool computes the MPC rendezvous maneuvering solution in a numerically efficient way, and this allows to greatly extend the prediction horizon length. This implies that the overall MPC scheme can compute solutions that account for the long time-scales that usually characterize the low-thrust rendezvous maneuvers. The so-developed controller is then tested in a realistic scenario that includes all the near-Earth environmental disturbances. We thus show, through numerical simulations, that this MPC method can successfully be used to perform a fuel-efficient rendezvous maneuver with an uncontrolled object, plus evaluate performance indexes such as mission duration, fuel consumption, and robustness against sensor and process noises.

Gesture Recognition has a prominent importance in smart environment and home automation. Thanks to the availability of Machine Learning approaches it is possible for users to define gestures that can be associated with commands for the smart environment. In this paper we propose a Random Forest-based approach for Gesture Recognition of hand movements starting from wireless wearable motion capture data. In the presented approach, we evaluate different feature extraction procedures to handle gestures and data with different duration. To enhance reproducibility of our results and to foster research in the Gesture Recognition area, we share the dataset that we have collected and exploited for the present work.

With the rise of Industry 4.0 and of the Industrial Internet, the computing and communication infrastructures achieved an essential role within process and factory automation, and cyberphysical systems in general. In this scenario, the OPC UA standard is currently becoming a widespread opportunity to enable interoperability among heterogeneous industrial systems. Nonetheless, OPC UA is characterized by a complex protocol architecture, that may impair the scalability of applications and may represent a bottleneck for its effective implementation in resource-constrained devices, such as low-cost industrial embedded systems. Several different OPC UA implementations are available, which in some significant cases are released under an open source license. In this context, the aim of this paper is to provide an assessment of the performance provided by some of these different OPC UA implementations, focusing specifically on potential development and resource bottlenecks. The analysis is carried out through an extensive experimental campaign explicitly targeting general purpose low-cost embedded systems. The final goal is to provide a comprehensive performance comparisons to allow devising some useful practical guidelines.

The use of unmanned vehicles to perform tiring, hazardous, repetitive tasks, is becoming a reality out of the academy laboratories, getting more and more interest for several application fields from the industrial, to the civil, to the military contexts. In particular, these technologies appear quite promising when they employ several low-cost resource-constrained vehicles leveraging their coordination to perform complex tasks with efficiency, flexibility, and adaptation that are superior to those of a single agent (even if more instrumented). In this work, we study one of said applications, namely the visual tracking of an evader (target) by means of a fleet of autonomous aerial vehicles, with the specific aim of focusing on the target so as to perform an accurate position estimation while concurrently allowing a wide coverage over the monitored area so as to limit the probability of losing the target itself. These clearly conflicting objectives call for an optimization approach that is here developed: by considering both aforementioned aspects and the cooperative capabilities of the fleet, the designed algorithm allows controling in real time the single fields of view so as to counteract evasion maneuvers and maximize an overall performance index. The proposed strategy is discussed and finally assessed through the realistic Gazebo-ROS simulation framework.

In an automated demand feeder system, underlining the parameters that contribute to fish hunger is crucial in order to facilitate an optimised food allocation to the fish. The present investigation is carried out to classify the hunger state of Lates calcarifer. A video surveillance technique is employed for data collection. The video was taken throughout the daytime, and the fish were fed through an automated feeding system. It was demonstrated through this investigation that the use of such automated system does contribute towards a higher specific growth rate percentage of body weight as well as the total length by approximately 26.00% and 15.00%, respectively against the conventional time-based method. Sixteen features were feature engineered from the raw dataset into window sizes ranging from 0.5?min, 1.0?min, 1.5?min and 2.0?min, respectively coupled with the mean, maximum, minimum and variance for each of the distinctive temporal window sizes. In addition, the extracted features were analysed through Principal Component Analysis (PCA) for dimensionality reduction as well as PCA with varimax rotation. The data were then classified using a Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Random Forest Tree models. It was demonstrated that the varimax based PCA yielded the highest classification accuracy with eight identified features. The prediction results based of the developed k-NN model on the selected features on the test data exhibited a classification rate of 96.5% was achieved suggesting that the features examined are non-trivial in classifying the fish hunger behaviour.

With this work we report on the design, development and testing of near UV LED-based systems for oxygen gas sensing. The design and developed system is an optoelectronic setup based on 405 nm LEDs which excites and measures the photoluminescence emitted from a porphyrin based luminophor. By means of an accurate optical and optoelectronic setup, the system is able to operate without the need of avalanche photodiodes, thus resulting in a compact and low energy structure. The optical setup is specifically designed to maximize both the LED light exciting the luminophor and converted light acquired from the sensor.

Wireless networks are ever more deployed in industrial automation systems in various types of applications. A significant example in this context is represented by the transmission of safety data that, traditionally, was accomplished by wired systems. In this paper we propose an implementation of the Fail Safe over EtherCAT (FSoE) protocol on the top of IEEE 802.11 WLAN. The paper, after a general introduction of FSoE, focuses on the implementation of such protocol on commercial devices running UDP at the transport layer and connected via the IEEE 802.11 Wireless LAN. Then the paper presents some experimental setups and the tests that have been carried out on them. The obtained results are encouraging, since they show that good safety performance can be achieved even in the presence of wireless transmission media.

Over the past few years, a huge number of distributed camera calibration strategies have been proposed for video surveillance and monitoring systems involving mobile terminals. Many of the proposed solutions rely on consensus-based algorithms, which aim at estimating the configuration of the network via a message passing protocol. In this paper we propose an improved consensus-based distributed camera calibration strategy that exploits a robust initialization, together with a pruning protocol to remove faulty links which could propagate excessively-noisy information through the network reducing the convergence time. The proposed solution seems to improve the state-of-the-art strategies in terms of accuracy, convergence speed, and computational complexity.

Over the last decades, small satellites have become very appealing among the space community for their low complexity and high flexibility. Many proposed mission concepts foresee the employment of miniature spacecraft for a variety of applications, many of which are economically unfeasible with traditional vehicles. This is due to the fact that the development of miniaturized and standardized space vehicles may considerably reduce the design, manufacturing and lunch costs involved. Furthermore, the reduced unitary mass of small satellites allows launches of multiple vehicles equipped with independent functionalities, thus achieving increased flexibility and redundancy. In the future, one additional opportunity could be given by the capability to assemble spacecraft in orbit to form reconfigurable structures. This would further boost the number of possibilities in terms of applications and operations. Nevertheless, the novelty of such concept and the intrinsic complexity of its practical realization still require a considerable research effort. In fact, only few navigation and docking technologies for nano- and micro satellites have been designed and proved in relevant environment. In this framework, the authors focus on the development and validation of critical technologies for close navigation, rendezvous and docking suitable for nanosatellites.

This paper presents a laboratory experiment for the validation on a complete rendezvous, navigation and docking package compatible with the common CubeSat standard. The experiment is conducted on a low friction table, with one free moving vehicle (chaser) that approaches and docks to a fixed target interface. The test facility allows three degrees of freedom to the nanosat mock-up. The vehicle is equipped with an autonomous package that features (1) a camera-based vision system for relative navigation, (2) a set of independent electro-magnetic coils for final alignment and soft docking, (3) a single-actuator hard docking system for structural connection between the chaser and the target, (4) a dedicated electronics package for motion control and system status monitoring. The mobile platform is also equipped with a set of flat air bearing with a dedicated high-pressure pneumatic circuit for frictionless in-plane motion.

This paper describes the docking package, the CubeSat mock-up and the test facility in details, with reference to the main design considerations. Numerical simulations have been conducted to foresee the dynamical behaviour of the system and to select the appropriate control algorithm. An intensive experimental campaign aims at the validation of numerical results and at the functional verification and performance estimation for each subsystem and for the system as a whole. The numerical and experimental results are presented and compared, allowing to draw useful conclusions for the future development steps. 

In the last years, international space-related companies and agencies are manifesting great interest in on-orbit servicing. Innovative solutions to perform on-orbit operations such as refuelling, payload updating and maintenance, subsystems repairing and inspection are under study and all the new ideas and technologies under development are perceived as extremely functional and cost-effective, capable of increasing the operational lifetime of a satellite and decreasing the costs related to its complete replacement.

For these reasons, the development of an automatic, standard and reliable docking system would simplify the accomplishment of on-orbit servicing procedures. Presently, there has been an increasing interest in developing different technologies for proximity navigation and rendezvous manoeuvres but no competitive or commercial technologies are currently available to perform autonomous rendezvous and docking between small-satellites. One promising solution is represented by relative magnetic navigation, where the chaser relative position and attitude can be controlled thanks to magnetic interactions with the target vehicle.

This paper presents an overview of the PACMAN experiment: PACMAN is a technology demonstrator developed by a team of university and PhD students in the framework of ESA Education Fly Your Thesis! 2017 programme and supported by the University of Padova. The experiment has been selected for the 68^th ESA Parabolic Flight Campaign that took place in December 2017. The main goal of the project was to develop and validate in low-gravity conditions an integrated system for proximity navigation and soft-docking based on magnetic interactions, suitable for small-scale spacecraft. This has been accomplished by launching a miniature spacecraft mock-up (1U CubeSat) towards a free-floating target that generates a static magnetic field; a set of actively-controlled magnetic coils on-board the spacecraft mock-up, assisted by dedicated localization sensors, have been used to control its attitude and position relative to the target. This experimental setup allowed to study the behaviour of a miniature spacecraft subjected to controlled magnetic interactions in low-gravity conditions and to validate the theoretical/numerical models that describe such interactions.

The paper describes the experiment design, realization and execution, from the initial concept to the Parabolic Flight Campaign tests. The experiment working principle is illustrated with particular attention towards the navigation and soft-docking subsystems, and the analysis of retrieved scientific results is finally presented.

Presently, no competitive or commercial solution is currently available to perform autonomous rendezvous and docking between small-satellites. Therefore, in the last years there has been an increasing interest in developing different technologies for proximity navigation and rendezvous manoeuvres, addressing the main issues of fuel consumption and the strong impact of close range navigation subsystems on satellites mass budget and complexity. One promising solution is represented by relative magnetic navigation, where the chaser relative position and attitude can be controlled thanks to magnetic interactions with the target vehicle.

PACMAN experiment is a technology demonstrator that has been developed by a team of university and PhDs students in the framework of ESA Education Fly Your Thesis! 2017 programme and supported by the University of Padova. The experiment has been selected to fly during the 68^th ESA Parabolic Flight Campaign, currently scheduled to take place this December. The main goal of the project is to develop and validate in low-gravity conditions an integrated system for proximity navigation and soft-docking based on magnetic interactions, suitable for small-scale spacecraft. This will be accomplished by launching a miniature spacecraft mock-up towards a free-floating target that generates a static magnetic field; a set of actively-controlled magnetic coils on-board the spacecraft mock-up, assisted by dedicated localization sensors, will be used to control its attitude and position relative to the target.

The realization of PACMAN experiment will allow to study the behaviour of a miniature spacecraft subjected to controlled magnetic interactions in low-gravity conditions and to validate the theoretical/numerical models that describe such interactions.

This paper presents an overview from the concept and design of the experiment to the Parabolic Flight Campaign tests. The experiment working principle will be illustrated, along with the design and assembly phases. Particular attention will be made towards the problem solving approach. Alternatives and backup solutions are introduced as part of the lessons learned during the entire programme. Finally, the analysis of retrieved scientific results will be showed.

The relationship between fMRI and MEG signals between different cortical regions (functional connectivity, FC) has been extensively analyzed in the resting state (De Pasquale et al 2010; Brookes et al 2011; Hipp et al 2012). Much less is known about FC modulations from rest to task states, and how they appear respectively in these two imaging modalities. Previously we have shown task-specific alterations of FC in fMRI during a visuospatial attention task (Spadone et al., PNAS, 2015). Specifically, decrements of resting correlation in visual areas were coupled with increments of correlation between visual and dorsal attention regions. Here, we compared fMRI with band-limited power (BLP) correlation obtained with MEG on the same group of subjects. Aim (i) is to measure frequency specific task-related FC modulations in MEG. Aim (ii) is to compare fMRI- and MEG-FC modulations (task-rest).

A purely geometric approach has been investigated to reconstruct the Demonstration Fusion Power Reactor (DEMO) plasma boundary for control purposes. The whole plasma boundary is reconstructed by using a deformable template method based on B-splines. The final curve shape is achieved by minimizing the distance between a limited number of estimated and measured (at present provided by an equilibrium code) plasma boundary points along the reflectometer lines of sight. The resulting unconstrained optimization problem is solved by a simulated annealing algorithm. The method is complemented by including the available plasma and poloidal field coil current measurements to refine the boundary reconstruction in the X-point region. The robustness with respect to random measurement random errors and to a reduction in the number of measurements is discussed. The main equilibrium and shape geometric quantities (such as plasma cross-sectional area, plasma center position, elongation, and triangularity) were computed and compared to the corresponding quantities of a DEMO reference equilibrium.

We consider the problem of controlling a smart lighting system of multiple luminaires with collocated occupancy and light sensors. The objective is to attain illumination levels higher than specified values (possibly changing over time) at the workplace by adapting dimming levels using sensor information, while minimizing energy consumption. We propose to estimate the daylight illuminance levels at the workplace based on the daylight illuminance measurements at the ceiling. More specifically, this daylight estimator is based on a model built from data collected by light sensors placed at workplace reference points and at the luminaires in a training phase. Three estimation methods are considered: Regularized least squares, locally weighted regularized least squares, and cluster-based regularized least squares. This model is then used in the operational phase by the lighting controller to compute dimming levels by solving a linear programming problem, in which power consumption is minimized under the constraint that the estimated illuminance is higher than a specified target value. The performance of the proposed approach with the three estimation methods is evaluated using an open-office lighting model with different daylight conditions. We show that the proposed approach offers reduced under-illumination and energy consumption in comparison to existing alternative approaches.

Recently, different communities in computer science, telecommunication and control systems have devoted a huge effort towards the design of energy efficient solutions for data transmission and network management. This paper collocates along this research line and presents a novel energy efficient strategy conceived for Ethernet networks. The proposed strategy combines the statistical properties of the network traffic with the opportunities offered by the IEEE 802.3az amendment to the Ethernet standard, called Energy Efficient Ethernet (EEE). This strategy exploits the possibility of predicting the incoming traffic from the analysis of the current data flow, which typically presents a self-similar behavior. Based on the prediction, Ethernet links can then be put in a low power consumption state for the intervals of time in which traffic is expected to be of low intensity. Theoretical bounds are derived that detail how the performance figures depend on the parameters of the designed strategy and scale with respect to the traffic load. Furthermore, simulations results, based on both real and synthetic traffic traces, are presented to prove the effectiveness of the strategy, which leads to considerable energy savings at the cost of only a limited bounded delay in data delivery.

In this paper, we study the problem of real-time optimal distributed partitioning for perimeter patrolling in the context of multicamera networks for surveillance, where each camera has limited mobility range and speed, and the communication is unreliable. The objective is to coordinate the cameras in order to minimize the time elapsed between two different visits of each point of the perimeter. We address this problem by casting it into a convex problem in which the perimeter is partitioned into nonoverlapping segments, each patrolled by a camera that sweeps back and forth at the maximum speed. We then propose an asynchronous distributed algorithm that guarantees that these segments cover the whole patrolling perimeter at any time and asymptotically converge to the optimal centralized solution under reliable communication. We finally modify the proposed algorithm in order to attain the same convergence and covering properties even in the more challenging scenario, where communication is lossy and there is no channel feedback, i.e., the transmitting camera is not aware whether a packet has been received or not by its neighbors.

In this paper we study cluster synchronization in a network of Kuramoto oscillators, where groups of oscillators evolve cohesively and at different frequencies from the neighboring oscillators. Synchronization is critical in a variety of systems, where it enables complex functionalities and behaviors. Synchronization over networks depends on the oscillators’ dynamics, the interaction topology, and coupling strengths, and the relationship between these different factors can be quite intricate. In this work we formally show that three network properties enable the emergence of cluster synchronization. Specifically, weak inter-cluster connections, strong intra-cluster connections, and sufficiently diverse natural frequencies among oscillators belonging to different groups. Our approach relies on system-theoretic tools, and is validated with numerical studies.

Wireless sensor networks (WSNs) can provide numerous benefits in industrial automation. By removing the cable infrastructure, the wireless architecture enables the possibility for nodes in a network to dynamically and autonomously group into clusters according to the communication features and the data they collect. This capability allows to leverage the flexibility and robustness of industrial WSNs in supervisory intelligent systems for high-level tasks, such as, for example, environmental sensing, condition monitoring, and process automation. In this paper, a clustering strategy is studied that partitions a sensor network into a nonfixed number of nonoverlapping clusters according to the communication network topology and measurements distribution: To this aim, both a centralized and a distributed algorithm are designed that do not require a cluster-head structure or other network assumptions. As a validation, these strategies are tested on a real dataset coming from a structured environment and the effectiveness of the clustering procedure is also investigated to perform anomalies detection in an industrial production process.

This paper presents a robust technique, based on Krylov-subspace method, for the reduction of large-scale state-space models arising in many electromagnetic applications in fusion machines. The proposed approach, built on the Arnoldi algorithm, aims at reducing the number of states of the system and lowering the computational effort, with a negligible loss of accuracy in the numerical solution. A detailed performance study is presented on an ITER-like machine, addressing both 2-D and 3-D problems.

The 2014–2016 JET results are reviewed in the light of their significance for optimising the ITER research plan for the active and non-active operation. More than 60?h of plasma operation with ITER first wall materials successfully took place since its installation in 2011. New multi-machine scaling of the type I-ELM divertor energy flux density to ITER is supported by first principle modelling. ITER relevant disruption experiments and first principle modelling are reported with a set of three disruption mitigation valves mimicking the ITER setup. Insights of the L–H power threshold in Deuterium and Hydrogen are given, stressing the importance of the magnetic configurations and the recent measurements of fine-scale structures in the edge radial electric. Dimensionless scans of the core and pedestal confinement provide new information to elucidate the importance of the first wall material on the fusion performance. H-mode plasmas at ITER triangularity (H??=??1 at ? N ~ 1.8 and n/n GW ~ 0.6) have been sustained at 2 MA during 5?s. The ITER neutronics codes have been validated on high performance experiments. Prospects for the coming D–T campaign and 14 MeV neutron calibration strategy are reviewed.

The control and optimization of oxygen content in wine matrices is becoming more and more important in wine production in order to guarantee their best quality. It is well known that in a first phase O2 is necessary to facilitate the development and activity of yeasts, to favor the combination of anthocyanins and color stabilization, and to help reducing the astringency of red wines. In a second phase, however, during the maturation, O2 can severely deteriorate the organoleptic characteristics of the wine. The main oenological practices commonly performed in wine cellars causes remarkable amounts of oxygen to dissolve in wine. For this reason, an instrumentation able to be used in cellars is needed to detect the amount of dissolved oxygen in the wine; as well, a plant technology is needed which is capable of eliminating excess oxygen. In this work, a new economic prototype of optical sensor for oxygen measurements in oenological matrices is developed. It is based on the sampling of the light emission of a polysulfone polymer membrane containing 5,10,15,20-Tetraphenyl-21H,23H-porphyrin platinum (II) (PtTPP). The experimental parameter used for calibration of the sensor is the life time of the PtTPP, obtained from the fitting of the emission decay profile produced by stimulation of the membrane with short pulses emitted by a 390 nm excitation LED. The membrane guarantees a linear behavior of the Stern-Volmer equation and long-lasting signal stability [1,2]. Studies on the behavior of the sensor have been performed in different environments such as air, water, synthetic wine, and also in real red and white wine samples at different temperatures from 5 ° to 20 ° C. The sensor has been suitably designed to work in food matrices and to optimize the noise signal ratio, while keeping the price of the components as low as possible. The sensor was tested for a month within a barrel of 10 m containing wine in the first fermentation phase. In particular, two equal sensors were placed at two levels of depth compared to the wine infeed level: at 0.5 and 2.5 m, respectively. The oxygen content measured during this period was always constant and equal to 0.2%. We thank Smart Future S.r.l. and the project "WOW: DEPLOYMENT OF WSAN TECHNOLOGY FOR MONITORING OXYGEN IN WINE PRODUCTS" financed by the Veneto Region ex LR 5/2001 - ex LR 9/2007.

The diffusion in Power Systems of distributed renewable energy resources, electric vehicles and controllable loads has made advanced monitoring systems fundamental to cope with the consequent disturbances in power flows; advanced monitoring systems can be employed for Anomaly Detection, Root Cause Analysis and Control purposes.

Several Machine Learning-based approaches have been developed in the past recent years to detect if a power system is running under anomalous conditions and, eventually, to classify such situation with respect to known problems.

One of the aspects that makes Power Systems challenging to be tackled, is that the monitoring has to be performed on streams of data that have a time series evolution; this issue is generally tackled by performing a features extraction procedure before the classication phase. The features extraction phase consists of translating the informative content of time series data into scalar quantities: such procedure may be a time-consuming step that requires the involvement of process experts to avoid loss of information in the making; moreover, extracted features designed to capture certain behaviors of the system, may not be informative under unseen conditions leading to poor monitoring performances.

A different type of data-driven approaches, that will be reviewed in this chapter, allow to perform classication directly on the raw time series data, avoiding the features extraction phase: among these approaches, Dynamic Time Warping and Symbolic-based methodologies have been widely applied in many application areas.

In the following, pros and cons of each approach will be discussed and practical implementation guidelines will be provided.

In the context of activity recognition, wearable devices arenowadays the preferable hardware thanks to their usability, user expe-rience and performances; at the same time, these devices present limi-tations in terms of computational capability and memory, which forcethe algorithm design to be at the same time ecient and simple. Inthis work, we adopt Symbolic Aggregate Approximation (SAX), a sym-bolic approach for information retrieval in time series data that allowsdimensionality and numerosity reduction; SAX is employed here, in com-bination with 1-Nearest Neighbor classier, to identify activity phases incontinuous repetitive activities from inertial time-series data. The pro-posed approach is validated on a public activity recognition dataset.

The problem of daylight estimation in a smart lighting system is considered. The smart lighting system consists of multiple luminaires with collocated occupancy and light sensors. Using sensor information, the objective is to attain illumination levels higher than specified values at the workspaces. We consider a training phase wherein light sensors are used at the workspaces in addition. Data from the light sensors at the ceiling and workspaces is used to estimate the mapping across the sensors. In the operational phase, the estimated mapping is used at the lighting controller to obtain an estimate of the illuminance value at the workspaces. Under the constraint that the estimated illuminance is higher than a specified target value, the controller optimizes the dimming levels of the luminaires to minimize power consumption. We evaluate the performance of the proposed approach in an open-office lighting model by considering different daylight conditions.

We address the problem of distributed unconstrained convex optimization under separability assumptions, i.e., the framework where a network of agents, each endowed with local private multidimensional convex cost and subject to communication constraints, wants to collaborate to compute the minimizer of the sum of the local costs. We propose a design methodology that combines average consensus algorithms and separation of time-scales ideas. This strategy is proven, under suitable hypotheses, to be globally convergent to the true minimizer. Intuitively, the procedure lets the agents distributedly compute and sequentially update an approximated Newton-Raphson direction by means of suitable average consensus ratios. We show with numerical simulations that the speed of convergence of this strategy is comparable with alternative optimization strategies such as the Alternating Direction Method of Multipliers. Finally, we propose some alternative strategies which trade-off communication and computational requirements with convergence speed.

The problem of emergent synchronization pat-terns in a complex network of coupled oscillators has caughtscientists’ interest in a lot of different disciplines. In particular,from a biological point of view, considerable attention has beenrecently devoted to the study of the human brain as a networkof different cortical regions that show coherent activity duringresting-state. In literature, there can be found different large-scale models of resting-state dynamics in health and disease.In this context, the Kuramoto model, a classical model apt todescribe oscillators’ dynamics, has been extended to capture thespatial displacement and the communication conditions in suchbrain network. Starting from a previous work in this ?eld ,we analyze this modi?ed model and compare it with otherexisting large-scale models. In doing so, our aim is to promotea set of mathematical tools useful to better understand realexperimental data in neuroscience and estimate brain dynamics.

In  this  paper  we  address  the  average  consensus problem in a Wireless Sensor-Actor Network with the particular focus  on  autonomous  fault  detection.  To  this  aim,  we  design  a distributed  clustering  procedure  that  partitions  the  network into  clusters  according  to  both  similarity  of  measurements and communication connectivity. The exploitation of clustering techniques   in   consensus   computation   allows   to   obtain   the detection   and   isolation   of   faulty   nodes,   thus   assuring   the convergence  of  the  other  nodes  to  the  exact  consensus  value. More  interestingly,  the  algorithm  can  be  integrated  into  a Kalman filtering framework to perform distributed estimation of  a  dynamic  quantity  in  presence  of  faults.  The  proposed approach is validated through numerical simulations and tests on a real world scenario dataset.

Personal control with occupancy and daylight adaptation is considered in a lighting system with multiple luminaires. Each luminaire is equipped with a co-located occupancy sensor and light sensor that respectively provide local occupancy and illumination information to a central controller. Users may also provide control inputs to indicate a desired illuminance value. Using sensor feedback and user input, the central controller determines dimming values of the luminaires using an optimization framework. The cost function consists of a weighted sum of illumination errors at light sensors and the power consumption of the system. The optimum dimming values are determined with the constraints that the illuminance value at the light sensors are above the reference set-point at the light sensors and the dimming levels are within physical allowable limits. Different approaches to determine the set-points at light sensors associated with multiple user illumination requests are considered. The performance of the proposed constrained optimization problem is compared with a reference stand-alone controller under different simulation scenarios in an open-plan office lighting system.