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The work considers the design of an indirect adaptive controller for a satellite equipped with a robotic arm manipulating an object. Uncertainty on the manipulated object can considerably impact the overall behavior of the system. In addition, the dynamics of the actuators of the base satellite are non-linear and can be affected by malfunctioning. Neglecting these two phenomena may lead to excessive control effort or degrade performance. An indirect adaptive control approach is pursued, which allows consideration of relevant features of the actuators dynamics, such as loss of effectiveness. Furthermore, an adaptive law that preserves the physical consistency of the inertial parameters of the various rigid bodies comprising the system is employed. The performance and robustness of the controller are first analyzed and then validated in simulation.

Characterized by a cross-disciplinary nature, the bearing-based target localization task involves estimating the position of an entity of interest by a group of agents capable of collecting noisy bearing measurements. In this work, this problem is tackled by resting both on the weighted least square estimation approach and on the active-sensing control paradigm. Indeed, we propose an iterative algorithm that provides an estimate of the target position under the assumption of Gaussian noise distribution, which can be considered valid when more specific information is missing. Then, we present a seeker agents control law that aims at minimizing the localization uncertainty by optimizing the covariance matrix associated with the estimated target position. The validity of the designed bearing-based target localization solution is confirmed by the results of an extensive Monte Carlo simulation campaign.

Tilted hexarotors embody a technology that remains partially unexploited in terms of its potential, especially concerning precise and concurrent position and attitude control. Focusing on these aerial platforms, we propose two control architectures that can tackle the trajectory tracking task, ensuring also the attitude regulation: one is designed resting on the differential flatness property of the system, which is investigated in the paper, and the other is a hierarchical nonlinear controller. We comparatively discuss the performance of the two control schemes, in terms of the accuracy of both the tracking control action and the attitude regulation, the input effort, and the robustness in the presence of disturbances. Numerical results reveal both the robustness of the hierarchical approach in the case of external disturbance and the accuracy of the differential flatness-based controller in unwindy conditions.

The diffusion of the Industry 4.0 paradigm lead to the creation and collection of huge manufacturing datasets; such datasets contain for example measurements coming from physical sensors located in different equipment or even in different productive manufacturing organizations. Such large and heterogeneous datasets represent a challenge when aiming for developing data-driven approaches like Anomaly Detection or Predictive Maintenance. In this work we present a new approach for performing Anomaly Detection that is able to handle heterogeneous data coming from different equipment, work centers or production sites.

Recent results show that features of adversarially trained networks for classification, in addition to being robust, enable desirable properties such as invertibility. The latter property may seem counter-intuitive as it is widely accepted by the community that classification models should only capture the minimal information (features) required for the task. Motivated by this discrepancy, we investigate the dual relationship between Adversarial Training and Information Theory. We show that the Adversarial Training can improve linear transferability to new tasks, from which arises a new trade-off between transferability of representations and accuracy on the source task. We validate our results employing robust networks trained on CIFAR-10, CIFAR-100 and ImageNet on several datasets. Moreover, we show that Adversarial Training reduces Fisher information of representations about the input and of the weights about the task, and we provide a theoretical argument which explains the invertibility of deterministic networks without violating the principle of minimality. Finally, we leverage our theoretical insights to remarkably improve the quality of reconstructed images through inversion.

The potentialities of In-Orbit Servicing (IOS) to extend the operational life of satellites and the need to implement Active Debris Removal (ADR) to effectively tackle the space debris problem are well known among the space community. Research on technical solutions to enable this class of missions is thriving, also pushed by the development of new generation sensors and control systems. Among private companies, space agencies and universities, the European Space Agency (ESA) has been developing technologies in this field for decades. Several solutions have been proposed over the years to safely capture orbital objects, the majority relying on robotic systems. A promising option is the employment of an autonomous spacecraft (chaser) equipped with a highly dexterous robotic arm able to perform the berthing with a resident space object. This operation poses complex technical challenges both during the approach phase and after contact. In this respect, the design of an effective, reliable, and robust Guidance, Navigation and Control (GNC) system, for which several algorithmic architectures and hardware configurations are possible, plays a key role to ensure safe mission execution. This work presents the outcomes of a research activity performed by a consortium of universities under contract with ESA with the goal to develop the navigation and control subsystems of a GNC system for controlling a chaser equipped with a redundant manipulator. Both the final approach until capture and the target stabilization phase after capture are considered in the study. The proposed solution aims at the implementation of a combined control strategy. Robust control methods are adopted to design control laws for the uncertain, nonlinear dynamics of the chaser and of the complete chaser–target stack after capture. Visual–based solutions, i.e., relying on active/passive electro– optical sensors, are selected for relative navigation. A complete sensor suite for relative and absolute navigation is part of the GNC system, including transducers for robot joint measurements. To properly validate the proposed solutions, a complete numerical simulator has been developed. This software tool allows to thoroughly assess the system performance, accounting for all the relevant external disturbances and error sources. A realistic synthetic image generator is also used for relative navigation performance assessment. This paper presents the design solutions and the results of preliminary numerical testing, considering three mission scenarios to prove the flexibility of the solution and its applicability to a wide range of operational cases.

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.

Particle accelerators are complex systems composed of multiple subsystems that must work together to produce high quality beams employed for physics experiments. A fault or an anomalous behaviour in one of such subsystems can lead to expensive downtime for the whole facility. Thus, it is of paramount importance to be able to promptly detect anomalies.Given the vast amount of streaming data generated by accelerator field sensors, Machine Learning (ML)-based tools are promising candidates for efficient monitoring of such systems: an approach based on unsupervised ML techniques exploiting the data from a Radio Frequency tuning system is here proposed. Feature importance is exploited to guide the definition of the optimal windowing for feature extraction. The proposed approach is here validated on real-world data related to the ALPI accelerator at Legnaro National Laboratories in Italy.

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.

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.

One of the main challenges in developing Machine Learning-based solutions for Semiconductor Manu-facturing is the high number of machines in the production and their differences, even when consideringchambers of the same machine; this poses a challenge in the scalability of Machine Learning-based so-lutions in this context, since the development of chamber-specific models for all equipment in the fab isunsustainable. In this work, we present a domain adaptation approach for Virtual Metrology (VM), one ofthe most successful Machine Learning-based technology in this context. The approach provides a commonVM model for two identical-in-design chambers whose data follow different distributions. The approach isbased on Domain-Adversarial Neural Networks and it has the merit of exploiting raw trace data, avoidingthe loss of information that typically affects VM modules based on features. The effectiveness of theapproach is demonstrated on real-world Etching.

Machine Learning-based Anomaly Detection (AD) approaches are efficient tools to monitor complexprocesses. One of the advantages of such approaches is that they provide a unique anomaly indicator,a quantitative index that captures the degree of ’outlierness’ of the process at hand considering possiblyhundreds or more variables at the same time, the typical scenario in semiconductor manufacturing. Oneof the drawback of such approaches is that Root Cause Analysis is not guided by the system itself. Inthis work, we show the effectiveness of a method, called DIFFI, to equip Isolation Forest, one of themost popular AD algorithms, with interpretability traits that can help corrective actions and knowledgeunderstanding. Such approach is validated on real world semiconductor manufacturing data related to aChemical Vapor Deposition process.

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.

In modern manufacturing scenarios, detecting anomalies in production systems is pivotal to keep high-quality standards and reduce costs. Even in the Industry 4.0 context, realworld monitoring systems are often simple and based on the use of multiple univariate control charts. Data-driven technologies offer a whole range of tools to perform multivariate data analysis that allow to implement more effective monitoring procedures. However, when dealing with complex data, common data-driven methods cannot be directly used, and a feature extraction phase must be employed. Feature extraction is a particularly critical operation, especially in anomaly detection tasks, and it is generally associated with information loss and low scalability. In this paper we consider the task of Anomaly Detection with twodimensional, image-like input data, by adopting a Deep Learningbased monitoring procedure, that makes use of convolutional autoencoders. The procedure is tested on real Optical Emission Spectroscopy data, typical of semiconductor manufacturing. The results show that the proposed approach outperforms classical feature extraction procedures.

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.

Two methods based on Regression are presented to determine the moisture content of items, e.g. clothes and the like, which are introduced in a household laundry dryer appliance. The aim of this work is to develop Soft Sensors (SS) for a household Heat Pump Washer-Dryer (WD-HP) to provide an estimation of the desired signal (the laundry moisture during drying) avoiding the use of additional physical sensors with the goal of improving the current performance in terms of precision and energy consumption of the automatic drying cycle end and using the machine equipment already available. On an algorithmic point of view, the SS developed in this work exploits regularization methods and Genetic Programming for Symbolic Regression in order to find suitable models for the purpose at hand. Proposed approaches have been tested on real data provided by an industrial partner.

In the past recent years, Machine Learning methodologies have been applied in countless application areas. In particular, they play a key role in enabling Industry4.0. However, one of the main obstacles to the diffusion of Machine Learning-based applications is related to the lack of interpretability of most of these methods. In this work, we propose an approach for defining a ‘feature importance’ in Anomaly Detection problems. Anomaly Detection is an important Machine Learning task that has an enormous applicability in industrial scenarios. Indeed, it is extremely relevant for the purpose of quality monitoring. Moreover, it is often the first step towards the design of a Machine Learning based smart monitoring solutions because Anomaly Detection can be implemented without the need of labelled data. The proposed feature importance evaluation approach is designed for Isolation Forest, one of the most commonly used algorithm for Anomaly Detection. The efficacy of the proposed method is tested on synthetic and real industrial datasets.

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.

We present an algorithm for minimizing the sum of a strongly convex time-varying function with a time-invariant, convex, and nonsmooth function.
The proposed algorithm employs the prediction-correction scheme alongside the forward-backward envelope, and we are able to prove the convergence of the solutions to a neighborhood of the optimizer that depends on the sampling time.
Numerical simulations for a time-varying regression problem with elastic net regularization highlight the effectiveness of the algorithm.

We address the solution of time-varying optimization problems characterized by the sum of a time-varying strongly convex function and a time-invariant nonsmooth convex function.
We design an algorithmic framework based on a prediction-correction scheme, which employs splitting methods to solve the sampled instances of the time-varying problem.
We describe the prediction-correction scheme and two splitting methods, the forward-backward and the Douglas-Rachford. Then by using a novel result for generalized equations, we prove convergence of the generated sequence of approximate optimizers to a neighborhood of the optimal solution trajectory. Simulation results for a leader following formation in robotics assess the performance of the proposed algorithm.

In this paper we present a novel algorithm to solve the robot kinematic structure identification problem. Given a time series of data, typically obtained processing a set of visual observations, the proposed approach identifies the ordered sequence of links associated to the kinematic chain, the joint type interconnecting each couple of consecutive links, and the input signal influencing the relative motion. Compared to the state of the art, the proposed algorithm has reduced computational costs, and is able to identify also the joints' type sequence. 

Emotion Classification (EC) aims at assigning an emotion label to a textual document with two inputs – a set of emotion labels (e.g. anger, joy, sadness) and a document collection. The best performing approaches for EC are dictionary-based and suffer from two main limitations: (i) the out-of-vocabulary (OOV) keywords problem and (ii) they cannot be used across heterogeneous domains. In this work, we propose a way to overcome these limitations with a supervised approach based on TF-IDF indexing and Multinomial Linear Regression with Elastic-Net regularization to extract an emotion lexicon and classify short documents from diversified domains. We compare the proposed approach to state-of-the-art methods for document representation and classification by running an extensive experimental study on two shared and heterogeneous data sets.

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.

On-line and phone banking frauds are responsible for millions of dollars loss every year. In this work, we propose a Machine Learning-based Decision Support System to automatically associate a risk factor to each transaction performed through an on-line/mobile banking system. The proposed approach has a hierarchical architecture: First, an unsupervised Machine Learning module is used to detect abnormal patterns or wrongly labeled transactions; then, a supervised module provides a risk factor for the transactions that were not marked as anomalies in the previous step. Our solution exploits personal and historical information about the user, statistics that describe online traffic generated on the online/mobile banking system, and features extracted from motives of the transactions. The proposed approach deals with dataset unbalancing effectively. Moreover, it has been validated on a large database of transactions and on-line traffic provided by an industrial partner.

In this paper we propose a distributed implementation
of the relaxed Alternating Direction Method of Multipliers algorithm
(R-ADMM) for optimization of a separable convex cost
function, whose terms are stored by a set of interacting agents,
one for each agent. Specifically  the local cost stored by each node is in
general a function of both the state of the node and the states of its
neighbors, a framework that we refer to as `partition-based' optimization.
This framework presents a great flexibility and can be adapted to a large
number of different applications.
By recasting the problem into an operator theoretical framework, the proposed
algorithm is shown to be provably robust against random packet losses that
might occur in the communication between
neighboring nodes. Finally, the effectiveness of the proposed algorithm is
confirmed by  a set of compelling numerical simulations run over random
geometric graphs subject to i.i.d. random packet losses.

Fabric care home appliances are pervasive inhouses worldwide and manufactures are constantly working for improving product performance, efficiency, and usability. From a manufacturing perspective, increase of performancehas to be attained while minimizing the increase of production costs. In this context, a Soft Sensor for estimating the clothes load weight in a horizontal axis household washing machines ishere presented. The proposed Soft Sensor is based on Machine Learning approaches. Several methodologies, both time-seriesand feature-based, are employed and compared. The approach has been tested on real world data on commercial household washing machines.

In this work we address the problem of distributed optimization of the sum of convex cost functions in the context of multi-agent systems over lossy communication networks. Building upon operator theory, first, we derive an ADMM-like algorithm, referred to as relaxed ADMM (R-ADMM) via a generalized Peaceman-Rachford Splitting operator on the Lagrange dual formulation of the original optimization problem. This algorithm depends on two parameters, namely the averaging coefficient $\alpha$ and the augmented Lagrangian coefficient $\rho$ and we show that by setting $\alpha=1/2$ we recover the standard ADMM algorithm as a special case. Moreover, first, we reformulate our R-ADMM algorithm into an implementation that presents reduced complexity in terms of memory, communication and computational requirements. Second, we propose a further reformulation which let us provide the first ADMM-like algorithm with guaranteed convergence properties even in the presence of lossy communication. Finally, this work is complemented with a set of compelling numerical simulations of the proposed algorithms over random geometric graphs subject to i.i.d. random packet losses.

Subjects affected by Type I Diabetes (T1D) are constantly confronted with the complicated problem of administering themselves an adequate amount of insulin, so as to keep their blood-glucose concentration in a nearly physiological range. Recently, powerful technological tools have been developed to better face this challenge, in particular the so-called Artificial Pancreas (AP). Unluckily, the AP actuator, an insulin pump, is subject to faults, with potential serious consequences for subjects' safety. This calls for the development of advanced fault detection (FD) methods, leveraging the unprecedented data availability in this application. In this paper we tackle the problem of detecting insulin pump malfunctioning using a model-free approach, so that the complex sub-task of identifying a model of patients physiology is avoided. Moreover, we employed unsupervised methods since labeled data are hardly available in practice. The adopted data-driven Anomaly Detection (AD) methods are Local Outlier Factor and Connectivity-based Outlier Factor. The methods are applied on a feature set able to account for the physiological dynamics of T1D patients. The proposed algorithms are tested on a synthetic dataset, generated using the “UVA/Padova Type 1 Diabetic Simulator”, an accurate nonlinear computer simulator of the T1D subject physiology. Both methods show precision ~75% and recall ~60%• The described approach is suitable both for embedding in medical devices, such as the AP, and implementation in cloud-based remote monitoring systems.

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 laundry treatment appliances, the weight ofthe laundry loaded by the user inside the drum dramaticallyaffects the operating behavior. Therefore, it is important toobtain a good estimate of the said quantity in order tocorrectly configure the machine before the washing/dryingstarts. In Vertical Axis Washing Machinesthe laundry weightis computed by exploiting the quantity of water absorbed bythe clothes. However, such approach does not grant accurateresults because the water absorption depends on the clothesfabric. For this reason, we propose a Soft Sensing approachfor weight estimation that exploits the information obtainedfrom physical sensors available on board without added costs.Data-driven Soft Sensors are developed, where, using MachineLearning techniques, a statistical model of the phenomenon ofinterest is created from a set of sample data.

Wafer metrology is an expensive and time consuming activity in semiconductor manufacturing, but is essential to support advanced process control, predictive maintenance and other quality assurance functions. Keeping metrology to a minimum is therefore desirable. In the context of spatial sampling of wafers this has motivated the development of a number of data driven methodologies for optimizing wafer sampling plans. Two such methodologies are considered in this paper. The first combines Principal Component Analysis and Minimum Variance Estimation (PCA-MVE) to determine an optimum subset of sites from historical metrology data from a larger candidate set, while the second employs Forward Selection Component Analysis (FSCA), an unsupervised variable selection technique, to achieve the same result. We investigate the relationship between these two approaches and show that under specific conditions a regularized extension of FSCA, denoted FSCA-R, and PCA-MVE are equivalent. Numerical studies using simulated data verify the equivalence conditions. Results for simulated and industrial case studies show that the improvement in wafer profile reconstruction accuracy with regularization is not statistically significant for the case studies considered, and that when PCA-MVE is implemented with a denoising step as originally proposed, it is outperformed by FSCA. Therefore, FSCA is the preferred methodology.

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 laundry weight of the loaded in the drum of a laundry treatment machine is an important piece of information; laundry weight can be used to set various washing/drying cycle parameters and to optimize performances and efficiency. Unfortunately, dedicated weight sensors cannot be included in consumer laundry equipment given the related costs. For this reason, we present in this work a soft sensor approach for estimating laundry weight based on sensors already in place in a laundry treatment equipment; in particular, we consider here a heat pump tumble dryer as case study. The proposed soft sensor is based on regularization, a popular approach in Machine Learning to provide models without overfitting the training data. Different studies are provided in this work, by considering different constrains on timing and complexity of the Soft Sensor solution. The developed Soft Sensors have been tested on laboratory data provided by an industrial partner.

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).

Advanced Monitoring Systems are fundamental in advanced manufacturing for control, quality and maintenance purposes. Nowadays, with the increasing availability of data in production and equipment, the need for high-dimensional Anomaly Detection techniques is thriving; anomalies are data patterns that have different data characteristics from normal production instances and that may be associated with faults or drifts in production. Tools for dealing with high-dimensional monitoring problems are provided by Machine Learning: in this paper, we test the performance of a state-of-the-art anomaly detection technique, called Isolation Forest, on a real industrial dataset related to Etching, one of the most important semiconductor manufacturing process. The monitoring has been performed exploiting Optical Spectroscopy Data.

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.

Standard hexarotors are often mistakenly considered ‘by definition’ fail-safe multi-rotor platforms because of the two additional propellers when compared to quadrotors. However this is not true, in fact, a standard hexarotor cannot statically hover with ‘only’ five propellers. In this paper we provide a set of new general algebraic conditions to ensure static hover for any multi-rotor platform with any number of generically oriented rotors. These are elegantly formulated as the full-rankness of the control moment input matrix, and the non-orthogonality between its null-space and the row space of the control force input matrix. Input saturations and safety margins are also taken into account with an additional condition on the null-space of control moment input matrix. A deep analysis on the hoverability properties is then carried out focusing on the propeller loss in a hexarotor platform. Leveraging our general results we explain why a standard hexarotor is not robust and how it can be made robust thanks to a particular tilt of the rotors. We finally propose a novel cascaded controller based on a preferential direction in the null-space of the control moment input matrix for the large class of statically hoverable multi-rotors, which goes far beyond standard platforms, and we apply this controller to the case of failed tilted hexarotor.

Virtual Metrology is one of the most prominentAdvanced Process Control applications in SemiconductorManufacturing. The goal of Virtual Metrology is to provideestimations of quantities that are important for production andto assess process quality, but are costly or impossible to bemeasured. Virtual Metrology solutions are based on MachineLearning approaches. The bottleneck of developing VirtualMetrology solutions is generally the feature extraction phase thatcan be time-consuming, and can deeply affect the estimationperformance. In particular, in presence of data with additionaldimensions, such as time, feature extraction is typicallyperformed by means of heuristic approaches that may pickfeatures with poor predictive capabilities. In this work, wepropose the usage of modern Deep Learning approaches tobypass manual feature extraction and to provide highperformanceautomatic Virtual Metrology modules. Theproposed methodology is tested on a real industrial datasetrelated to Etching. The dataset at hand contains OpticalEmission Spectroscopy data and it is paradigmatic of the featureextraction problem under examination.

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.

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.

The Multirate Support feature has been introduced by the IEEE 802.11 standard to improve system performance. It has been widely exploited within general purpose Wireless LANs by means of Rate Adaptation (RA) strategies, that unfortunately revealed ineffective for the case of real-time industrial communications. This paper presents the innovative Rate Selection for Industrial Networks (RSIN) algorithm, specifically conceived for the real-time industrial scenario with the goal of minimizing the transmission error probability, while taking into account the deadlines imposed to packet delivery.

In this paper an approach to deal with Predictive Maintenance (PdM) problems with time-series data is discussed. PdM is a important approach to tackle maintenance and it is gaining an increasing attention in advanced manufacturing to minimize scrap materials, downtime, and associated costs. PdM approaches are generally based on Machine Learning tools that require the availability of historical process and maintenance data. Given the exponential growth in data logging in modern equipment, time series dataset are increasingly available in PdM applications. To exploit time series data for PdM, a functional learning methodology, namely Supervised Aggregative Feature Extraction (SAFE), is here employed on a semiconductor manufacturing maintenance problem.

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 analyze the performances of some popular algorithms used to solve distributed optimization problems involving quadratic cost functions in a multi agent system. Namely, we study the performances of standard consensus, accelerated consensus and ADMM.  We analyze the scalar quadratic function case, under different scenarios and with structured graphs. We find that accelerated consensus is the algorithm with the best performance in all the cases analyzed. On the other hand, ADMM has performance comparable to the accelerated consensus when the graph is scarcely connected, while for dense graphs its performance deteriorates and becomes worse than the one of standard consensus. The results therefore suggest that the choice of the algorithm to solve the problem we analyze strongly depends on the graph, and that accelerated consensus should always be preferred.

Recently, several working implementations of inband full-duplex wireless systems have been presented, where the same node can transmit and receive simultaneously in the same frequency band. The introduction of such a possibility at the physical layer could lead to improved performance but also poses several challenges at the MAC layer. In this paper, an innovative mechanism of channel contention in full-duplex OFDM wireless networks is proposed. This strategy is able to ensure efficient transmission scheduling with the result of avoiding collisions and effectively exploiting full-duplex opportunities. As a consequence, considerable performance improvements are observed with respect to standard and state-of-the-art MAC protocols for wireless networks, as highlighted by extensive simulations performed in ad hoc wireless networks with varying number of nodes.

In this paper, statistical Quality of Service provisioning in next generation heterogeneous mobile cellular networks is investigated. To this aim, any active entity of the cellular network is regarded as a queuing system, whose statistical QoS requirements depend on the specific application. In this context, by quantifying the performance in terms of effective capacity, we introduce a lower bound for the system performance that facilitates an efficient analysis. We exploit this analytical framework to give insights about the possible improvement of the statistical QoS experienced by the users if the current heterogeneous cellular network architecture migrates from a Half Duplex to a Full Duplex mode of operation. Numerical results and analysis are provided, where the network is modeled as a Mate?rn point processes with a hard core distance. The results demonstrate the accuracy and computational efficiency of the proposed scheme, especially in large scale wireless systems.

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.

IEEE 802.11 systems are drawing an ever increasing interest for wireless industrial communication, also thanks to the interesting features provided by the most recent and advanced amendments to this standard, such as IEEE 802.11n. Due to the intrinsic unreliability of the wireless medium, the current research efforts aim at improving both timeliness and reliability of such a protocol in view of its adoption for real-time applications. A significant issue in this context is represented by the reduction of the randomness that affects packet delivery times. An important benefit in this direction can be obtained by the deactivation of the standard legacy carrier sensing and backoff procedures. In this paper we show, through a simulative assessment, that a fine control of such features leads to improved real-time performance.

A new Bayesian approach to linear system identification
has been proposed in a series of recent papers. The
main idea is to frame linear system identification as predictor
estimation in an infinite dimensional space, with the aid of
regularization/Bayesian techniques. This approach guarantees
the identification of stable predictors based on the prediction
error minimization. Unluckily, the stability of the predictors
does not guarantee the stability of the impulse response of
the system. In this paper we propose and compare various
techniques to guarantee that the final model identified following
this Bayesian approach is stable. First, we consider the socalled
“LMI - constraint” approach and adapt it to constrain
the eigenvalues of the estimated model within the unit circle.
A second possibility which is being considered is to add to the
“classic” Stable-Spline algorithm a penalty term, depending on
the maximum absolute value of the eigenvalue of the system.
This last technique has the advantage of being integrated
directly inside the pre-existing optimization problem and not to
simply post-process the estimated model to guarantee stability.
Finally, we considered a Monte Carlo Markov Chain approach
sampling in both the space of hyper-parameters and of impulse
responses. Simulations results comparing these techniques will
be provided.

The IEEE 802.11 standard, since its earliest versions, provides the multi-rate support feature typically exploited by Rate Adaptation (RA) techniques to dynamically select the most suitable transmission rate, based on an estimation of the channel status. With the release of the IEEE 802.11n amendment, several enhancements have been introduced to the standard, notably the support for MIMO architectures, whose benefits can be effectively combined with multi-rate support. In an industrial communication scenario, the RA algorithms commonly available for general purpose applications revealed ineffective. This led to the definition of purposely designed algorithms, with the aim of improving the real-time behavior of IEEE 802.11 networks. In this paper we take into consideration these techniques, as well as some general purpose RA strategies, and analyze their implementation on an IEEE 802.11n communication system deployed in an industrial scenario. Furthermore, we propose an effective parameters tuning for the considered RA algorithms, as well as some enhancements conceived to enforce their timeliness. An exhaustive assessment, carried out via numerical simulations, shows that the improved techniques allow to achieve excellent performance.

Slow release drugs must be manufactured to meettarget speci?cations with respect to dissolution curve pro?les.In this paper we consider the problem of identifying thedrivers of dissolution curve variability of a drug from historicalmanufacturing data. Several data sources are considered: rawmaterial parameters, coating data, loss on drying and pellet sizestatistics. The methodology employed is to develop predictivemodels using LASSO, a powerful machine learning algorithmfor regression with high-dimensional datasets. LASSO providessparse solutions facilitating the identi?cation of the most importantcauses of variability in the drug fabrication process.The proposed methodology is illustrated using manufacturingdata for a slow release drug.

In the last years, IEEE 802.11 Wireless LANs (WLANs) have proved their effectiveness for a wide range of real-time industrial communication applications. Nonetheless, the enhancements at the PHY and MAC layers introduced by the IEEE 802.11n amendment have not yet been adequately addressed in the context of industrial communication. In this paper we investigate the impact of some IEEE 802.11n new features on some important performance figures for industrial applications, such as timeliness and reliability.

Faulty operations of Heating, Ventilation and Air Conditioning (HVAC) chiller systems can lead to discomfort for the occupants, energy wastage, unreliability and shorter equipment life. Such faults need to be detected early to prevent further escalation and energy losses. Commonly, data regarding unforeseen phenomena and abnormalities are rare or are not available at the moment of HVAC systems installation: for this reason in this paper an unsupervised One-Class SVM classifier employed as a novelty detection system to identify unknown status and possible faults is presented. The approach, that exploits Principal Component Analysis to accent novelties w.r.t. normal operations variability, has been tested on a HVAC literature dataset.

In recent years, Visual Sensor Networks (VSNs) have emerged as an interesting category of distributed sensor- actor systems to retrieve data from the observed scene and produce information. Indeed, the request for accurate 3D scene reconstruction in several applications is leading to the development of very large systems and more specifically to large scale motion capture systems. When dealing with such huge amount of data from a large number of cameras it becomes very hard to make real time reconstruction on a single machine.
Within this context, a distributed approach for reconstruc- tion on large scale camera networks is proposed. The approach is based on geometric triangulation performed in a distributed fashion on the computational grid formed by the camera net- work organized into a tree structure. Since the computational performance of the algorithm strongly depends on the order in which cameras are paired, to optimize the efficiency of the reconstruction a pairing strategy is designed that relies on an affinity score among cameras. This score is computed from a probabilistic perspective by studying the variance of the 3D target reconstruction error and resorting to a normalized cut graph partitioning.
The scaling laws and the results obtained in simulation suggest that the proposed optimization strategy allows to obtain a significant reduction of the computational time.

Process monitoring and Predictive Maintenance (PdM) are gaining increasing attention in most manufacturing environments as a means of reducing maintenance related costs and downtime. This is especially true in industries that are data intensive such as semiconductor manufacturing. In this paper an adaptive PdM based flexible maintenance scheduling decision support system, which pays particular attention to associated opportunity and risk costs, is presented. The proposed system, which employs Machine Learning and regularized regression methods, exploits new information as it becomes available from newly processed components to refine remaining useful life estimates and associated costs and risks. The system has been validated on a real industrial dataset related to an Ion Beam Etching process for semiconductor manufacturing.

We consider the problem of building a transitional model of an initially uncalibrated camera network. More specifically, we discuss an Hidden Markov Model (HMM) based strategy in which the model’s statespace is defined in terms of a partition of the physical network coverage. Transitions between any two such states are modeled by the distribution of the underlying Markov Process. Extending previous work in (Cenedese et al., 2010), we show how it is possible to infer the model structure and parameters from coordinate free observations and introduce a novel performance index that is used for model validation. We moreover show the predictive power of this HMM approach in simulated and real settings that comprise Pan-Tilt- Zoom (PTZ) cameras.

In this paper, we consider the problem of designing a distributed strategy to estimate the channel parameters for a generic Wireless Sensor-Actor Network. To this aim, we present a distributed least-square algorithm that complies with the constraint of transmitting only integer data through the wireless communication, which often characterizes Wireless Sensor-Actor Network embedded architectures. In this respect, we propose a quantized consensus strategy that mitigates the effects of the rounding operations applied to the wireless exchanged floating data. Moreover, the approach is based on a symmetric random gossip strategy, making it suitable for the actual deployment in multiagent networks. Finally, the effectiveness of the proposed algorithm and of its implementation as an open-source application is assessed and the employment of the procedure is illustrated through the application to radio-frequency localization experiments in a real world testbed.

Technological advances and the ever-growing human quest for improving the resolution of telescope observations are motivating the design of larger and larger ground telescopes: indeed, the larger is the telescope lens diameter, the better is the diffraction limited resolution of the telescope. Unfortunately, the terrestrial atmospheric turbulence, if not properly compensated, negatively affects the telescope observations, limiting its real resolution. Adaptive Optics (AO) systems are used in large ground telescopes in order to compensate the effect of the atmosphere, and hence to make the real telescope resolution be determined by the diffraction properties of the lens.

Technological advances and the ever-growing human quest for improving the resolution of telescope observations are motivating the design of larger and larger ground telescopes: indeed, the larger is the telescope lens diameter, the better is the diffraction limited resolution of the telescope. Unfortunately, the terrestrial atmospheric turbulence, if not properly compensated, negatively affects the telescope observations, limiting its real resolution. Adaptive Optics (AO) systems are used in large ground telescopes in order to compensate the effect of the atmosphere, and hence to make the real telescope resolution be determined by the diffraction properties of the lens. AO systems exploit the measurements of wavefront sensors to estimate the current values of the atmospheric turbulence, and compensate its effect by properly adapting the shape of a set of deformable mirrors. As the size of the telescope lenses is increasing, then the size of the AO system (e.g. the number of deformable mirror actuators and the size of the wavefront sensor) is increasing as well. This causes the increase of the computational burden needed to compute a proper compensation of the effect of the atmosphere. Consequently, as the potential telescope resolution increases, the task of the AO systems becomes more challenging. Motivated by the need of providing AO solutions useful for the next generations of ground telescopes, then a number of efficient algorithms have been recently considered in the literature to solve the problems related to the AO system. This paper considers the combination of a recently proposed very efficient phase reconstruction method, namely the CuRe, with a properly defined Kalman filter in order to obtain a dynamic compensation of the atmospheric turbulence. The performance of the proposed approach is investigated in some simulations.

In the semiconductor manufacturing environment it is very important to understand which factors have the most impact on process outcomes and to control them accordingly. This is usually achieved through design of experiments at process start-up and long term observation of production. As such it relies heavily on the expertise of the process engineer. In this work, we present an automatic approach to extracting useful insights about production processes and equipment based on state-of-the-art Machine Learning techniques. The main goal of this activity is to provide tools to process engineers to accelerate the learning-by-observation phase of process analysis. Using a Metal Deposition process as an example, we highlight various ways in which the extracted information can be employed.

This paper investigates an original system for wireless control and monitoring of an agricultural machine. The system is implemented by means of an IEEE 802.11-based soft realtime communication architecture which enables the connection of the machine with off-the-shelf mobile devices, like widespread tablet PCs, that could hence replace traditional ad-hoc developed operator panels. The harsh surrounding environment, however, introduces severe requirements. Hence, focusing on the wireless communication behavior, the paper yields a thorough performance analysis derived by extensive experimental campaigns. By investigating the outcomes of these measurement sessions, the paper assesses some causes of performance degradation, and provides viable and easy to implement solutions to improve the overall system behavior.

Classification methods with embedded feature selection capability are very appealing for the analysis of complex processes since they allow the analysis of root causes even when the number of input variables is high. In this work, we investigate the performance of three techniques for classification within a Monte Carlo strategy with the aim of root cause analysis. We consider the naive Bayes classifier and the logistic regression model with two different implementations for controlling model complexity, namely, a LASSO-like implementation with a l1 norm regularization and a fully Bayesian implementation of the logistic model, the so called relevance vector machine. Several challenges can arise when estimating such models mainly linked to the characteristics of the data: a large number of input variables, high correlation among subsets of variables, the situation where the number of variables is higher than the number of available data points and the case of unbalanced datasets. Using an ecological and a semiconductor manufacturing dataset, we show advantages and drawbacks of each method, highlighting the superior performance in term of classification accuracy for the relevance vector machine with respect to the other classifiers. Moreover, we show how the combination of the proposed techniques and the Monte Carlo approach can be used to get more robust insights into the problem under analysis when faced with challenging modelling conditions.

The diffusion of visual sensor networks, and in particular of smart camera networks, is motivating an increasing interest on the research of distributed solutions for several vision problems. Specifically, in this paper we propose a distributed solution to the problem of reconstructing target positions in large Motion Capture (MoCap) systems. Real time reconstruction by means of centralized procedures is practically unfeasible for very large systems, while the use of distributed computation allows to significantly reduce the computational time required for reconstruction, thus allowing the development of real time solutions.
Then the proposed distributed reconstruction procedure is optimized by exploiting information about the structure of the system: the visibility matrix states which objects in the scene are somehow measurable by a sensor (sensor-object matrix). Often, the typical localization of data from real application scenarios induces an underlying structure on the visibility matrix, that can be exploited to improve the performance of the system in understanding the surrounding environment. Unfortunately, usually these data are not properly organized in the visibility matrix: for instance, listing the sensors in a pseudo-random order can hide the underlying structure of the matrix. This paper considers the problem of recovering such underlying structure directly from the visibility matrix and designs an algorithm to perform this task.
Our simulations show that the distributed reconstruction algorithm optimized by means of the estimation of the structure of the visibility matrix allows a particularly relevant computational time reduction with respect to the standard (centralized) reconstruction algorithm.

In semiconductor fabrication processes, effectivemanagement of maintenance operations is fundamental todecrease costs associated with failures and downtime. PredictiveMaintenance (PdM) approaches, based on statistical methodsand historical data, are becoming popular for their predictivecapabilities and low (potentially zero) added costs. We presenthere a PdM module based on Support Vector Machines forprediction of integral type faults, that is, the kind of failuresthat happen due to machine usage and stress of equipmentparts. The proposed module may also be employed as a healthfactor indicator. The module has been applied to a frequentmaintenance problem in semiconductor manufacturing industry,namely the breaking of the filament in the ion-source ofion-implantation tools. The PdM has been tested on a realproduction dataset.

We describe tradeoffs between the performance in visual decision problems and the control authority that the agent can exercise on the sensing process. We focus on problems of “coverage” (ensuring that all regions in the scene are seen) and “change estimation” (finding and learning an unknown object in an otherwise known and static scene), propose a measure of control authority and empirically relate it to the expected risk and its proxy (conditional entropy of the posterior density). We then show that a “passive” agent can provide no guarantees on performance beyond what is afforded by the priors, and that an “omnipotent” agent, capable of infinite control authority, can achieve arbitrarily good performance (asymptotically).

In past years the Reversed Field Pinch RFX-mod has also been operated as a low current Tokamak to perform experiments of active control of MHD modes particularly harmful to a prospective reactor. The stabilization of m=2, n=1 mode has been achieved for 150 kA plasma currents in circular shape discharges at q(a)<2. In order to test the system capability of stabilizing such modes in improved confinement regimes, the possibility of producing D- shaped plasma discharges has been explored. Preliminary experiments were carried out in open loop in 2011. In the meantime a completely new plasma position and shape control system was designed and its performances simulated with the finite element 2D MHD equilibrium code MAXFEA. According to the simulation results, feedback control of the D- shape configuration was capable of meeting the design requirements. As a first step, the recent experimental campaign in Tokamak configuration was partially dedicated to demonstrate the possibility of a stable feedback controlled operation with an elongated plasma. In the paper the identification of the transfer function between a dedicated Field Shaping (FS) coil current distribution and the plasma elongation, the design of the control system, its implementation and successful testing are described.

Camera positioning units are widely used in surveillance and they are sometimes mounted on floating supports, e.g.
on patrolling ships or buoys. The support motion, in turn, induces an apparent motion in the image plane, which can create troubles to the image processing, especially when a specific feature must be tracked (e.g. a distant ship, getting close to a forbidden area). Low cost devices are often characterized by low frame rate and low image resolution, for which traditional image stabilization techniques usually results to be rather ineffective. Additionally, low-end camera units are usually driven by hybrid stepper motors and, being conceived to work in an harsh environment, they do not mount any optical image stabilization (OIS) system, either in the camera lenses or in the image sensor. In this paper, the image acquired by a pan–tilt camera positing unit mounted on a moving support is stabilized by exploiting the camera attitude information provided by a MEMS-based IMU with an embedded magnetometer. In particular, two independent integral control loops are designed for the pan and tilt motors in order to compensate for the yaw and pitch motions of the support. As for the roll motion, since it relates to an unavailable degree of freedom in the positioning unit, it can be compensated only on the captured image. The proposed solution is experimentally tested on a real device mounted on a moving table actuated by a 6 degrees–of–freedom pneumatic hexapod. Realistic motions are recreated by using the data recordings taken aboard of a patrolling ship and a costal buoy. Experimental results show that the proposed solution is capable of keeping the camera pointing at a fixed target with a good accuracy, thus making higher-level image processing easier and more effective.

Camera positioning units for surveillance applica- tions are often mounted on mobile supports or vehicles. In such circumstances, the motion of the supporting base affects the camera field of view, thus making the task of pointing and tracking a specific target problematic, especially when using low cost devices that are usually not equipped with rapid actuators and fast video processing units. Visual tracking capabilities can be improved if the camera field of view is preliminarily stabilized against the movements of the base. Although some cameras available on the market are already equipped with an optical image stabilization (OIS) system, implemented either in the camera lenses or in the image sensor, these are usually too expensive to be installed on low–end positioning devices.
A cheaper approach to image stabilization consists of stabilizing the camera motion using the motors of the positioning unit and the inertial measurements provided by a low–cost MEMS Inertial Measurement Unit (IMU). This paper explores the feasibility of applying such image stabilization system to a low cost pan–tilt– zoom (PTZ) camera positioning unit driven by hybrid stepper motors (HSMs), in order to aid the task of pointing and tracking of a specific target on the camera image plane. In the proposed solution, a two–level cascaded control structure, consisting of inner inertial stabilizing control loop and an outer visual servoing control loop, is used to control the PTZ unit. Several tests are carried out on a real device mounted on a moving table actuated by a 6 degrees–of–freedom pneumatic hexapod. Realistic motions are recreated by using the data recordings taken aboard of a patrolling ship.

In this paper, we study modeling and identification of stochastic systems by Generalized Factor Analysis models. Although this class of models was originally introduced for econometric purposes, we present some possible  applications of engineering interest. We show that there is a natural connection between Generalized Factor Analysis models and multi-agents systems. The common factor component of the model has an interpretation as a flocking component of the system behaviour.

Models typically used to simulate the influence of atmospheric turbulence on ground telescope observations are usually based on the frozen flow hypothesis. However, the frozen flow model of the atmosphere is valid at time scales of the order of tens/hundreds of milliseconds. This paper generalizes a previous model for turbulence simulation to ensure reliable tests of AO system performance in realistic working conditions. The proposed method relies on the use of two simulation models: First, the part of turbulence that shows a coherent flow at short time scales is simulated by means of a multiscale autoregressive-moving average model, which allows to efficiently simulate (with computational complexity O(n)) the coherent evolution of the turbulence. Secondly, an approach similar to that considered for dynamic textures, is used to simulate aberrations caused by processes that evolve on much longer time scales. The proposed procedure is tested on simulations.

Motivated by the increasing importance of Adap- tive Optics (AO) systems for improving the real resolution of large ground telescopes, and by the need of testing the AO system performance in realistic working conditions, in this paper we address the problem of simulating the turbulence effect on ground telescope observations at high resolution. The multiscale approach presented here generalizes that in [3]: First, a relevant computational time reduction is obtained by exploiting a local spatial principal component analysis (PCA) representation of the turbulence. Furthermore, differently from [3], the turbulence at low resolution is modeled as a moving average (MA) process. While in [3] the wind velocity was restricted to be directed along one of the two spatial axes, the approach proposed here allows to evolve the turbulence indifferently in all the directions. In our simulations the pro- posed procedure reproduces with good accuracy the theoretical statistical characteristics of the turbulent phase.

In large ground telescopes the Adaptive Optics (AO) system aims at compensating the atmosphere effect on telescope measurements, and, the use of optimal filtering is fundamental for such task. This work is motivated by two important characteristics of new AO systems: on one hand, because of the request of very high measurement resolutions, the size of new telescopes, and of their sensors, is quickly increasing in the last decades, thus imposing to the AO systems the analysis of larger amount of data. On the other hand, the optimal filter has to be periodically updated according to temporal changes in atmosphere characteristics. Hence, it is of fundamental importance the use of computationally efficient algorithms for the update of the optimal filter gain.
This paper proposes some changes to a recently presented method for the efficient computation, in the frequency domain, of the Kalman gain for large AO systems [15]. The proposed changes, which mainly aim at correcting some issues due to the conversion spatial–frequency domain, and viceversa, allow to compute a better approximation of the optimal Kalman gain, and, consequently, significantly improve the performance of the AO system.

In current and next generation of ground tele- scopes, Adaptive Optics (AO) are employed to overcome the detrimental effects induced by the presence of atmospheric turbulence, that strongly affects the quality of data transmission and limits the actual resolution of the overall system. The analysis as well as the prediction of the turbulent phase affecting the light wavefront is therefore of paramount importance to guarantee the effective performance of the AO solution.
In this work, a layered model of turbulence is proposed, based on the definition of a Markov-Random-Field whose pa- rameters are determined according to the turbulence statistics. The problem of turbulence estimation is formalized within the stochastic framework and conditions for the identifiability of the turbulence structure (numbers of layers, energies and velocities) are stated. Finally, an algorithm to allow the layer detection and characterization from measurements is designed. Numerical simulations are used to assess the proposed procedure and validate the results, confirming the validity of the approach and the accuracy of the detection.

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the predictionof ion-source tungsten filament breaks. The PdM has been tested on a real production dataset.

We consider the problem of controlling two agents with competitive objectives. Agents are modelled as linear discrete time systems, and collect each other’s state information without delays. The competitive problem is  formulated in a receding horizon framework, where each agent’s controllers are computed by minimizing a linear, quadratic cost function which depends on both agents’ states. The two agents specify their state tracking objective in a coordinated or competitive manner. We do not consider state constraints. The simplicity of our framework allows us to provide the following results analytically: 1) When agents compete, their states converge to an equilibrium trajectory where the steady state tracking error is finite. 2) Limit-cycles cannot occur. Numerical simulations and experiments done with a LEGO mindstorm multiagent platform match our analytical results.

Measurements of large ground telescopes are af- fected by the presence of the terrestrial atmospheric turbulence: local changes of the atmospheric refraction index (e.g. due to wind and temperature variations) cause a non flat surface of the wavefront of light beams incoming on the telescope, thus degrading the quality of the observed images. Adaptive Optics (AO) systems are of fundamental importance to reduce such atmospheric influence on ground telescopes and thus to obtain high resolution observations. The goal of the AO system is that of estimating and compensating the atmospheric turbulence effect by properly commanding a set of deformable mirrors.
Because of delays in the closed loop system, the Kalman filter plays an important role in ensuring an effective control perfor- mance by providing good atmosphere predictions. However, the need of periodically updating the Kalman filter gain because of changes in the atmosphere characteristics, the increase of telescopes and sensors resolutions and the high sampling rate impose quite strict restrictions to the computational load for computing the Kalman gain.
Motivated by the above considerations, some strategies have been recently considered in the system theory and astronomical communities for the efficient computation of the Kalman gain for large AO systems. Specifically, this paper presents some changes to a recently proposed procedure: the proposed approach, which exploits some results in the control theory of distributed systems, computes an approximation of the optimal gain in the frequency domain exploiting the spatial homogeneity of the system. Then, the control strategy takes advantage of some information on the turbulent phase dynamic, that is estimated from the turbulence measurements. Performances of the proposed method are investigated in some simulations.

Semiconductor fabrication involves several sequentialprocessing steps with the result that critical productionvariables are often affected by a superposition of affects overmultiple steps. In this paper a Virtual Metrology (VM) systemfor early stage measurement of such variables is presented;the VM system seeks to express the contribution to theoutput variability that is due to a defined observable partof the production line. The outputs of the processed systemmay be used for process monitoring and control purposes. Asecond contribution of this work is the introduction of ElasticNets, a regularization and variable selection technique for themodelling of highly-correlated datasets, as a technique for thedevelopment of VM models. Elastic Nets and the proposed VMsystem are illustrated using real data from a multi-stage etchprocess used in the fabrication of disk drive read/write heads.

The request for very accurate 3D reconstruction in several applications is leading to the development of very large motion capture systems. A good synchronization of all the cameras in the system is of fundamental importance to guarantee the effectiveness of the 3D reconstruction.
In this work, first, an approximation of the reconstruction error variance taking into account of synchronization errors is derived. Then, a Kalman filter approach is considered to estimate the cameras synchronization errors. The estimated delays can be used to compensate the synchronization error effect on the reconstruction of target positions. The results obtained in some simulations suggest that the proposed strategy allows to obtain a significant reduction of the 3D reconstruction error.

Ion Implantation is one of the most sensitiveprocesses in Semiconductor Manufacturing. It consists inimpacting accelerated ions with a material substrate and isperformed by an Implanter tool. The major maintenanceissue of such tool concerns the breaking of the tungstenfilament contained within the ion source of the tool. Thiskind of fault can happen on a weekly basis, and theassociated maintenance operations can last up to 3 hours.It is important to optimize the maintenance activities bysynchronizing the Filament change operations with otherminor maintenance interventions. In this paper, a PredictiveMaintenance (PdM) system is proposed to tackle such issue;the filament lifetime is estimated on a statistical basisexploiting the knowledge of physical variables acting onthe process. Given the high-dimensionality of the data,the statistical modeling has been based on RegularizationMethods: Lasso, Ridge Regression and Elastic Nets. Thepredictive performances of the aforementioned regularizationmethods and of the proposed PdM module have beentested on actual productive semiconductor data.

Soft Sensors (SSs) are on-line estimators of “hardly to be measured” quantities of a process. The difficultyin measuring can be related to economic or temporal costs that cannot be afforded in a high-intensivemanufacturing production. In semiconductor manufacturing this technology goes with the name of Virtual Metrology(VM) systems. While a lot of efforts in research have been produced in the past years to identify the bestregression algorithms for these statistical modules, small amount of work has been done to develop algorithms fordata clustering of the entire production. This paper contains a new Information Theory-based approach to dataclustering for Virtual Metrology and Soft Sensors; the proposed algorithm allows to automatically split the datasetinto groups to be equally modeled. The proposed approach has been tested on real industrial dataset.

Virtual Metrology (VM) module have become popular in the past years and they are now widely adopted in the semiconductor plants. However, nowadays, still few works have been presented to deal with the interaction between VM and Run-to-Run (R2R), the most common control approach in the fabs. We present in this paper a new strategy to integrate VM with R2R based on Information Theory measure. The proposed control method penalizes statistical measure based on their statistical distance from the physical measure. This new approach also cope with the virtual loop control, where the R2R runs for several process iterations without in-situ measures, but based only on VM predictions. The results are compared with the actual state-of-the-art. 

In fusion devices, the accurate reconstruction of the boundary location and shape from magnetic diagnostics is of paramount importance for the efficient control of the plasma evolution and the safe running of the experiment. In addition to a good and consistent performance in the reconstruction, the task must be performed in real time as the input for the shape controller and more in general for the scenario optimization. To this aim, a statistical procedure for the evaluation of the reconstruction capability of different magnetic sensor sets is presented, which can drive the choice for an optimal set to be used for the reconstruction of plasma location and boundary shape during real time operation. In addition, an algorithm to approximately solve the free boundary problem and estimate the plasma shape starting from the magnetics is devised. Beyond representing a first step towards the definition of a boundary reconstruction code for plasma shape control, this tool is also used to cross validate and confirm the statistical analysis on the diagnostics.

We consider the distributed unconstrained minimization of separable convex costfunctions, where the global cost is given by the sum of several local and private costs, eachassociated to a specific agent of a given communication network. We specifically address anasynchronous distributed optimization technique called Newton-Raphson consensus. Besidehaving low computational complexity, low communication requirements and being interpretableas a distributed Newton-Raphson algorithm, the technique has also the beneficial properties ofrequiring very little coordination and naturally support time-varying topologies. In this workwe analytically prove that under some assumptions it shows local convergence properties, andcorroborate this result by means of numerical simulations.

Semiconductor manufacturing is one of the most technologically advanced industrial sectors. Process quality and control are critical for decreasing costs and increasing yield. The contribution of automatic control and statistical modeling in this area can drastically impact production performance. For this reason in the past decade major collaborative research projects have been undertaken between fab industries and academia in the areas of Virtual Metrology, Predictive Maintenance, Fault Detection, Run-to-Run control and modeling. In this paper we review some this research, discuss its impact on production and highlight current challenges. 

The paper considers the problem of reconstructing a probability density function from a finite set of samples independently drawn from it. We cast the problem in a Bayesian setting where the unknown density is modeled via a nonlinear transformation of a Bayesian prior placed on a Reproducing Kernel Hilbert Space. The learning of the unknown density function is then formulated as a minimum variance estimation problem. Since this requires the solution of analytically intractable integrals, we solve this problem by proposing a novel algorithm based on the Markov chain Monte Carlo framework. Simulations are used to corroborate the goodness of the new approach.

To maintain and organize distributed systems it is necessary to have a certain degree of knowledge of their status like the number of cooperating agents. The estimation of this number, usually referred as the network size, can pose challenging questions when agents' identification information cannot be disclosed, since the exchanged information cannot be associated to who originated it. In this paper we propose a totally distributed network size estimation strategy based on statistical inference concepts that can be applied under anonymity constraints. The scheme is based on the following paradigm: agents locally generate some Bernoulli trials, then distributedly compute averages of these generated data, finally locally compute the Maximum Likelihood estimate of the network size exploiting its probabilistic dependencies with the previously computed averages. In this work we study the statistical properties of this estimation strategy, and show how the probability of returning a wrong evaluation decreases exponentially in the number of locally generated trials. Finally, we discuss how practical implementation issues may affect the estimator, and show that there exists a neat phase transition between insensitivity to numerical errors and uselessness of the results.

We consider the problem of commanding the electronic power interfaces of the microgenerators in a low voltage microgrid for the task of optimal reactive power compensation. In this work, we analyze the convergence of the strategy proposed by Tenti et al. in 2012. The proof of convergence gives some additional insight on the behavior of the algorithm and allows the characterization of its rate of convergence as a function of the microgrid parameters.

We consider how a set of collaborating agents can distributedly infer some of theproperties of the communication network that they form. We specifically focus on estimatingquantities that can characterize the performance of other distributed algorithms, namely theeccentricities of the nodes, and the radius and diameter of the network. We propose a strategythat can be implemented in any network, even under anonymity constraints, and has thedesirable properties of being fully distributed, parallel and scalable. We analytically characterizethe statistics of the estimation error, and highlight how the performance of the algorithmdepends on a parameter tuning the communication complexity.

We consider the problem of estimating the size of dynamic anonymous networks, motivated by network maintenance. The proposed algorithm is based on max-consensus information exchange protocols, and extends a previous algorithm for static anonymous networks. A regularization term is accounting for a-priori assumptions on the smoothness of the estimate, and we specifically consider quadratic regularization terms since they lead to closed-form solutions and intuitive design laws. We derive an explicit estimation scheme for a particular peer-to-peer service network, starting from its statistical model. To validate the accuracy of the algorithm, we perform numerical experiments and show how the algorithm can be implemented using finite precision arithmetics as well as small communication burdens.

A new boundary reconstruction procedure is presented and validated against ITER nominal equilibria. An approxima- tion of the plasma with an equivalent filamentary current model is employed, which is computed iteratively and allows to describe a wide variety of plasma current distributions (from the peaked ones, to the pedestal current ones). One of the specific features of the procedure is how the filaments are switched on and how the total current is distributed over the entire set, being the filaments independently considered: this allows more degrees of freedom to the model to adapt to particular current distributions, yielding better performances with a negligible additional computational burden. The code also implements a special points search making it well suited for both diverted (be they top or bottom x-point) and limiter configurations. In addition also the reconstruction in presence of noise has been explored.

Many modeling problems require to estimate ascalar output from one or more time series. Such problemsare usually tackled by extracting a fixed number of featuresfrom the time series (like their statistical moments), with aconsequent loss in information that leads to suboptimal predictivemodels. Moreover, feature extraction techniques usuallymake assumptions that are not met by real world settings (e.g.uniformly sampled time series of constant length), and failto deliver a thorough methodology to deal with noisy data.In this paper a methodology based on functional learningis proposed to overcome the aforementioned problems; theproposed Supervised Aggregative Feature Extraction (SAFE)approach allows to derive continuous, smooth estimates oftime series data (yielding aggregate local information), whilesimultaneously estimating a continuous shape function yieldingoptimal predictions. The SAFE paradigm enjoys severalproperties like closed form solution, incorporation of first andsecond order derivative information into the regressor matrix,interpretability of the generated functional predictor and thepossibility to exploit Reproducing Kernel Hilbert Spaces settingto yield nonlinear predictive models. Simulation studies areprovided to highlight the strengths of the new methodology withrespect to standard unsupervised feature selection approaches.

In semiconductor manufacturing plants, monitoringphysical properties of all wafers is fundamental in order tomaintain good yield and high quality standards. However, suchan approach is too costly and in practice only few wafers in a lotare actually monitored. Virtual Metrology (VM) systems allowto partly overcome the lack of physical metrology. In a VMscheme, tool data are used to predict, for every wafer, metrologymeasurements. In this paper, we present a VM system for aChemical Vapor Deposition (CVD) process. On the basis ofthe available metrology results and of the knowledge, for everywafer, of equipment variables, it is possible to predict CVDthickness. In this work we propose a VM module based onLARS to overcome the problem of high dimensionality andmodel interpretability. The proposed VM models have beentested on industrial production data sets.

In this paper we study the problem of real-time optimal distributed partitioning for perimeter patrolling in the context of multi-camera networks for surveillance. The objective is to partition a given segment into non-overlapping sub-segments, each assigned to a different camera to patrol. Each camera has both physical mobility range and limited speed, and it must patrol its assigned sub-segment by sweeping it back and forth at maximum speed. Here we first review the solution for the centralized optimal partitioning. Then we propose two different distributed control strategies to determine the extremes of the optimal patrolling areas of each camera. Both these strategies require only local communication with the neighboring cameras but adopt different communication schemes, respectively, symmetric gossip and asynchronous asymmetric broadcast. The first scheme is shown to be provably convergent to the optimal solution. Some theoretical insights are provided also for the second scheme whose effectiveness is validated through numerical simulations.

In this work the design and implementation of an application to track multiple agents in a indoor Wireless Sensor Actor Network (WSAN) is proposed. The adopted embedded hardware for the network nodes is theTmote Sky, an ultra low power IEEE 802.15.4 compliant wireless device, which has become a reference in the academia for the early development of algorithms and applications for Wireless Sensor Actor Networks (WSANs). These devices are based on the TinyOS operative system and are programmed in NesC a C-derived language specifically developed for embedded systems. NesC has become indispensable for low-level management ofindividual agents while Java was chosen to provide the user with a simple and intuitive graphical interface with whom showing and coordinating the tracking.

In this work an algorithm to track multiple agents in an indoor Wireless Sensor Actor Network (WSAN) is proposed. The algorithm falls into the category of the radio frequency localization methods, since it exploits the strength of the wireless communications among nodes to establish the position of a set of mobile nodes within a network of fixed nodes placed in known locations. In this sense, a radio channel model is introduced that allows to estimate the distances among nodes to attain localization and tracking (range-based approach). Moreover, to compensate for the scant robustness of power measurements, the loss effects induced by wireless communication,the intrinsic uncertainty of unstructured environments, the algorithm resorts to an Extended Kalman Filter to process the node measurements and reach a desired level of localization performance. Finally, the design phase is validated through the implementation and the experiments on a real testbed.

In this work we consider a multidimensional distributed optimization technique that is suitable for multiagents systems subject to limited communication connectivity. In particular, we consider a convex unconstrained additive problem, i.e. a case where the global convex unconstrained multidimensional cost function is given by the sum of local cost functions available only to the specific owning agents. We show how, by exploiting the separation of time-scales principle,the multidimensional consensus-based strategy approximates a Newton-Raphson descent algorithm. We propose two alternative optimization strategies corresponding to approximations of the main procedure. These approximations introduce tradeoffs between the required communication bandwidth and the convergence speed/accuracy of the results. We provide analytical proofs of convergence and numerical simulations supporting the intuitions developed through the paper.

In semiconductor manufacturing, state of the art for wafer quality control relies on product monitoring and feedback control loops; the involved metrology operations, performed by means of scanning electron microscopes, are particularly cost-intensive and time-consuming. For this reason, it is not possible to evaluate every wafer: in common practice, a small subset of a productive lot is measured at the metrology station and devoted to represent the whole lot. Virtual Metrology (VM) methodologies are able to obtain reliable predictions of metrology results at process time, without actually performing physical measurements; this goal is usually achieved by means of statistical models, linking process data and context information to target measurements. Since semiconductor manufacturing processes involve a high number of sequential operations, it is reasonable to assume that the quality features of a certain wafer (such as layer thickness, critical dimensions, electrical test results) depend on the whole processing and not only on the last step before measurement. In this paper, we investigate the possibilities to improve the Virtual Metrology quality relying on knowledge collected from previous process steps. We will present two different scheme of multistep VM, along with dataset preparation indications; special consideration will be reserved to regression techniques capable of handling high dimensional input spaces. The proposed multistep approaches will be tested against actual data from semiconductor manufacturing industry. 

This paper considers the problem of simulating the turbulence effect on ground telescope observations. The approach presented here is an evolution of a recently proposed approach [3]. The main contributions with respect to [3] are: First, the Haar transform at the basis of the multiscale model in [3] is shown to be equivalent to a local PCA representation. This equivalence allows to reduce the computational complexity of the simulation algorithm by neglecting the components in the signal with lower energy. Furthermore, the simulation of nonstationary turbulence is obtained by properly changing the values of the multiscale model: Such change is eased by the invariance of the PCA spatial basis with respect to the change of turbulence statistical characteristics. The proposed approach is validated by means of some simulations.

This paper reviews a number of recent results dealing with the zeros of rational  transfer functions, and including transfer functions of blocked discrete-time systems having more than one underlying sampling period.  A non-square system which is finite-dimensional, linear and time-invariant generically has  no zeros; in contrast, for a square system, generically all zeros will be distinct, finite and simple.  For  systems subjected to loss of some output values, so that with inputs  every T seconds,  some outputs available every T seconds and others only available every NT seconds, a blocked transfer function can be constructed. Similar conclusions hold, save that special attention nis needed for zeros at 0 and infinity.  The multi-frequency results are potentially very important in econometric modelling, where different time-series are available at different frequencies, e.g. monthly and quarterly, and offer the possibility of great simplification in the associated identification problem.

Geometric triangulation is at the basis of the estimation of the 3D position of a target from a set of camera measurements. The problem of optimal estimation (minimizing the L2 norm) of the target position from multi-view perspective projective measurements is typically a hard problem to solve. In literature there are different types of algorithms for this purpose, based for example on the exhaustive check of all the local minima of a proper eigenvalue problem [2], or branch- and-bound techniques [3]. However, such methods typically become unfeasible for real time applications when the number of cameras and targets become large, calling for the definition of approximate procedures to solve the reconstruction problem.
In the first part of this paper, linear (fast) algorithms, computing an approximate solution to such problems, are described and compared in simulation. Then, in the second part, a Gaussian approximation to the measurement error is used to express the reconstruction error’s standard deviation as a function of the position of the reconstructed point. An upper bound, valid over all the target domain, to this expression is obtained for a case of interest. Such upper bound allows to compute a number of cameras sufficient to obtain a user defined level of position estimation accuracy.

Epitaxy is a process strongly dependent onwafer temperature. Unfortunately, the performance ofthe pyrometers in charge of sensing wafer temperaturedeteriorate with the usage. This represents the majormaintenance issue for epitaxy process engineers who haveto frequently calibrate pyrometers emissivity coefficient. Atthe present state the change of the emissivity coefficient isheuristically based on fab tradition and process engineersexperience. We present a statistical tool to map therelationship between change in the temperature readingsand emissivity adjustments. The module has been testedon real industrial dataset.

This paper proposes a real-time distributed algorithm for a team of smart camerasto self-organize and perform video surveillance of an open boundary. In particular, our algorithmsimultaneously partitions the boundary among the cameras, and synchronizes the motion of thecameras to optimize the surveillance performance. We focus on the detection of smart intruders,who are aware of the cameras configuration at each time instant, and who schedule their motionto avoid detection for as long as possible. We consider both the worst-case and the averagedetection times of smart intruders. Our algorithm achieves minimum worst-case detection time,and, under some reasonable assumptions, constant-factor optimal average detection time.

We consider the convergence rates of two peculiar2 convex optimization strategies in the context of multi agent3 systems, namely the Newton-Raphson consensus optimization4 and a distributed Gradient-Descent opportunely derived from5 the first. To allow analytical derivations, the convergence6 analyses are performed under the simplificative assumption of7 quadratic local cost functions. In this framework we derive8 sufficient conditions which guarantee the convergence of the9 algorithms. From these conditions we then obtain closed form10 expressions that can be used to tune the parameters for11 maximizing the rate of convergence. Despite these formulae12 have been derived under quadratic local cost functions13 assumptions, they can be used as rules-of-thumb for tuning14 the parameters of the algorithms in general situations.

Nowadays, the adaptive optics (AO) system is of fundamental importance to reduce the effect of atmospheric turbulence on the images formed on large ground telescopes. In this paper the AO system takes advantage of the knowledge of the current turbulence characteristics, that are estimated by data, to properly control the deformable mirrors. The turbulence model considered in this paper is based on two assumptions: considering the turbulence as formed by a discrete set of layers moving over the telescope lens, and each layer is modeled as a Markov-Random-Field. The proposed Markov-Random-Field approach is exploited for estimating the layers’ characteristics. Then, a linear predictor of the turbulent phase, based on the computed information on the turbulence layers, is constructed. Since scalability and low computational complexity of the control algorithms are important requirements for real AO systems, the computational complexity properties of the proposed model are investigated. Interestingly, the proposed model shows a good scalability and an almost linear computational complexity thanks to its block diagonal structure. Performances of the proposed method are investigated by means of some simulations.

We considered the problem of minimizing reactive power flows in a smart microgrid. First we modeled this problem as a linearly constrained quadratic optimization, in which the decision variables are the amount of reactive power that compensators inject into the network. Then, we designed a distributed algorithm in which agents are clustered into overlapping subsets, according to a given communication graph that allows them to coordinate and to exchange information. At each time, one subset is triggered, and agents belonging to it update their states in order to minimize the reactive power flows on the grid. We showed that, by sensing the network at their points of connection, agents can perform this minimization with just the data that they can gather from the other agents belonging to the subset. We characterized convergence of this algorithm in term of conditions on the subsets and on the randomized triggering sequence. Moreover, we studied the rate of convergence, obtaining also a convenient upper bound. We finally analyzed the rate of convergence for some specic topologies of the grid and for some choices of the agents communication topologies.

We consider the scenario of a low voltage microgrid populated by a number of distributed microgenerators. We focus on the problem of obtaining a dynamic model that describes the input-output relation between complex power commands sent to the microgenerator inverters and the voltage measurements across the network. Such a model is intended as a necessary tool in the design of distributed and centralized control algorithms for the provision of ancillary services in the power distribution grid. Because this model is to be used for the design of such algorithms, we look for an analytical derivation instead of a simulative tool. The proposed model is linear and explicitly contains the network parameters and topology. Simulation shows how the proposed model approximates well the behavior of the original nonlinear system.

Silicon Epitaxial Deposition is a process strongly influenced by wafer temperature behavior, that has to be constantly monitored to avoid the production of defective wafers. A Predictive Maintenance (PdM) System is here proposed with the aim of predicting process behavior and scheduling control actions in advance. Two different prediction techniques have been employed and compared: the Kalman predictor and the Particle Filter with Gaussian Kernel Density Estimator. The accuracy of the PdM module has been tested on real fab data. The proposed approach is flexible and can handle the presence of different recipes on the same equipment.

In semiconductor manufacturing plants, monitoring of all wafers is fundamental in order to maintain good yield and high quality standards. However, this is a costly approach and in practice only few wafers in a lot are actually monitored. With a Virtual Metrology (VM) system it is possible to partly overcome the lack of physical metrology. In a VM scheme, tool data are used to predict, for every wafer, metrology measurements. In this paper, we present a VM system for a Chemical Vapor Deposition (CVD) process. Various data mining techniques are proposed. Due to the huge fragmentation of data derived from CVD's mixed production, several kind of data clustering have been adopted. The proposed models have been tested on real productive industrial data sets.

We consider the problem of optimal reactive power compensation for the minimization of power distribution losses in a smart microgrid. We first propose an approximate model for the power distribution network, which allows us to cast the problem into the class of convex quadratic, linearly constrained, optimization problems.

We also show how this model provides the tools for a distributed approach, in which agents have a partial knowledge of the problem parameters and state, and can only perform local measurements.

Then, we design a randomized, gossip-like optimization algorithm, providing conditions for convergence together with an analytical characterization of the convergence speed. The analysis shows that the best performance can be achieved when we command cooperation among agents that are neighbors in the smart microgrid topology. Numerical simulations are included to validate the proposed model and to confirm the analytical results about the performance of the proposed algorithm.

In this work we study the problem of unconstrained distributed optimization in the context of multi-agents systems subject to limited communication connectivity. In particular we focus on the minimization of a sum of convex cost functions, where each component of the global function is available only to a specific agent and can thus be seen as a private local cost. The agents need to cooperate to compute the minimizer of the sum of all costs. We propose a consensus-like strategy to estimate a Newton-Raphson descending update for the local estimates of the global minimizer at each agent. In particular, the algorithm is based on the separation of time-scales principle and it is proved to converge to the global minimizer if a specific parameter that tunes the rate of convergence is chosen sufficiently small. We also provide numerical simulations and compare them with alternative distributed optimization strategies like the Alternating Direction Method of Multipliers and the Distributed Subgradient Method.

We analyze the problem of data sets reduction

for support vector classification. The work is also motivated

by distributed problems, where sensors collect binary mea-

surements at different locations moving inside an environment

that needs to be divided into a collection of regions labeled in

two different ways. The scope is to let each agent retain and

exchange only those measurements that are mostly informative

for the collective reconstruction of the decision boundary. For

the case of separable classes, we provide the exact conditions

and an efficient algorithm to determine if an element in the

training set can become a support vector when new data arrive.

The analysis is then extended to the non-separable case deriving

a sufficient discardability condition and a general data selection

scheme for classification. Numerical experiments relative to the

distributed problem show that the proposed procedure allows

the agents to exchange a small amount of the collected data to

obtain a highly predictive decision boundary.

We consider a distributed system of N agents, on which we define a quadratic optimization problem subject to a linear equality constraint. We assume that the nodes can estimate the gradient of the cost function by measuring the steady state response of the system. Even if the cost function cannot be decoupled into individual terms for the agents, and the linear constraint involves the whole system state, we are able to design a distributed, gradient-driven, algorithm, for the solution of the optimization problem. This algorithm belongs to the class of quasi-Newton methods and requires minimal knowledge of the system to behave fairly well. We proved finite time convergence of the algorithm in its centralized version, and we designed its distributed implementation in the case in which a communication graph is given. In this latter case, the tool of average consensus results to be fundamental for the distribution of the algorithm. As a testbed for the proposed method, we consider the problem of optimal distributed reactive power compensation in smart microgrids.

The distributed estimation of the number of active sensors in a network can be important for estimation and organization purposes. We propose a design methodology based on the following paradigm: some locally randomly generated values are exchanged among the various sensors and thus modified by known consensus-based strategies. Statistical analysis of the a-consensus values allows estimation of the number of participant sensors. The main features of this approach are: algorithms are completely distributed, since they do not require leader election steps; sensors are not requested to transmit authenticative information (for example identificative numbers or similar data), and thus the strategy can be implemented whenever privacy problems arise. After a rigorous formulation of the paradigma we analyze some practical examples, fully characterize them from a statistical point of view, and finally we provide some general theoretical results and asymptotic analyses.

We study feedback-controlled, discrete-time quantum Markovian dynamics focusing on pure-state stabilization problem. Assuming that the system is unitarily controllable, and accessible via a given quantum measurement, we explicitly construct a choice of control actions conditioned on the measurement outcome that globally stabilizes the target state for the averaged dynamics. A key step in deriving this result is the definition of a canonical QR decomposition for complex matrices.

Wind energy is a random, uncontrollable, and highly variable source of energy, which will lead to serious challenges to grid integration at deep penetration levels. It is clear that the current extra-market approach to supporting wind integration (ex: California's Participating Intermittent Renewable Program (PIRP)) will not scale with an increase in installed wind capacity. At these deep penetration levels, forecasting, storage, demand response, and novel market mechanisms all become necessary ingredients to realize the promise of wind energy. In this paper, we examine the interplay between energy storage and market strategies to address the difficulties of wind integration. We attempt to answer the basic question: when faced with uncertainty in future market prices and wind power, how should a scheduling coordinator for a wind power plant exploit the ability to store energy so as to maximize revenue when scheduling wind power in a sequence of nested markets (day-ahead, hour-ahead, and real-time)? The problem is framed as a nonstandard chance-constrained model predictive control (MPC) problem. In formalizing the problem, we develop idealized models for electricity markets, wind energy revenue, energy storage, and the physical wind process. As chance constraints are, in general, nonlinear and difficult to handle in optimization problems, we suggest several stochastic models for wind that lead to tractable computations. This approach provides a contrast to scenario-based techniques for dealing with uncertainty in stochastic optimization problems.

Global asymptotic stabilization of quantum pure states is relevant to chemical process control, quantum cooling, state purification, and is crucial to the initialization of quantum information processing algorithms. We provide a linear-algebraic characterization of discrete-time Markovian dynamics leading to invariance and attractivity of a given quantum state. Assuming that the system is unitarily controllable, and accessible via a given quantum measurement, we provide a condition that that characterize the stabilizable target states. We also argue that if the control problem is feasible, then an effective control choice can be explicitly constructed. The result strongly relies on some remarlable properties of a canonical QR decomposition for complex matrices.

In this paper a distributed clock synchronization algorithm is proposed. The algorithm requires gossip asynchronous communication between the nodes of the network, and because of its proportional-integral (PI) structure it is able to compensate both initial offsets and different clock speeds. Convergence of the algorithm is proved and analysed with respect to the controller parameter, while scalability issues are addressed by simulations.

In this paper we analyze the problem of estimating a function from different noisy data sets collected by spatially distributed sensors and subject to unknown temporal shifts. We propose a novel approach based on non-parametric Gaussian regression and reproducing kernel Hilbert space theory that exploit compact and accurate representations of function estimates. As a first result, suitable minimization of inner products in reproducing kernel Hilbert spaces is used to obtain a novel time delay estimation technique when attention is restricted only to two signals. Then, we derive both a centralized and a distributed estimator to simultaneously identify the unknown function and delays for a generic number of networked sensors subject to a restricted communication graph. Numerical simulations are used to test the effectiveness of the proposed approaches.

The employment of Real-Time Ethernet networks in factory automation systems is rapidly increasing and several commercial products, with different characteristics, are already available from various manufacturers. Most of these networks have been included in both the IEC 61158 and IEC 61784 International Standards that, in addition, define a set of Performance Indicators. In this paper we focus on two popular Real Time Ethernet networks, namely Ethernet POWERLINK and EtherCAT, and we evaluate their performance for a typically deployed factory automation configuration. Specifically, we compute the most relevant performance indicators introduced by IEC 61784 standard and two (purposely defined) additional ones, namely minimum cycle time and jitter, which are suitable for the two networks considered.

In the last years the use of Ethernet as the communication technology for automation systems has been sensibly increasing. In particular, Real-Time Ethernet networks have become widely adopted. In this paper we consider one of the most popular Real-Time Ethernet networks, namely EtherCAT. We firstly provide a short outline of the protocol, along with a description of a simulation model we have developed. Subsequently, we focus on some typical network configurations and provide some insights on their performance by means of both theoretical analysis and numerical simulations.

We study a problem of output feedback stabi-lization of complex-valued reaction-advection-diffusion systems with parametric uncertainties (these systems can also be viewedas coupled parabolic PDEs). Both sensing and actuation are performed at the boundary of the PDE domain and the unknown parameters are allowed to be spatially varying. First, we transform the original system into the form where unknown functional parameters multiply the output, which can be viewed as a PDE analog of observer canonical form. Input and output filters are then introduced to convert a dynamic parametrization of the problem into a static parametrization where a gradient estimation algorithm is used. The control gain is obtained by solving a simple complex-valued integral equation online. The solution of the closed-loop system is shown to be bounded and asymptotically stable around the zero equilibrium. The results are illustrated by simulations.

The paper deals with the Model Predictive Control(MPC) algorithm applied to control a permanent magnet synchronous motor, which is, at present, among the motors with the highest power efficiency and then very attractive for energy-saving applications. Absolute novelty of the proposed MPC is its feature of inherently managing the flux weakening control above base speed.Speed and current controller are combined together, including all the state variables of the system, instead of keeping the conventional cascade structure. This way it is possible to enforce in the controller the current and voltage limits. Simulation and experimental results point out the validity of the design procedure and the powerful capabilities of the MPC in the electrical drives field.

In this paper we study the problem of estimatingthe channel parameters for a generic wireless sensor network(WSN) in a completely distributed manner, using consensusalgorithms. Specifically, we first propose a distributed strategyto minimize the effects of unknown constant offsets in thereading of the Radio Strength Signal Indicator (RSSI) due touncalibrated sensors. Then we show how the computation of theoptimal wireless channels parameters, which are the solutionof a global least-square optimization problem, can be obtainedwith a consensus-based algorithm. The proposed algorithmsare general algorithms for sensor calibration and distributedleast-square parameter identification, and do not require anyknowledge on the global topology of the network nor thetotal number of nodes. Finally, we apply these algorithms toexperimental data collected from an indoor WSN.

Recently, two new types of communication networks have become available at the low levels of factory automation systems. Besides fieldbuses, which have been traditionally used since long time, both Real–Time Ethernet (RTE) and wireless networks may now be employed. Consequently, it is envisaged that hybrid configurations using all types of the available communication systems will be even more deployed in the future. In this paper we focus, specifically, on hybrid systems which realize wireless extensions of wired networks. In detail, we address the extension of Ethernet Powerlink, a popular RTE network, by means of the IEEE 802.11 WLAN. We consider two types of extensions based on both bridge and gateway devices. After a discussion concerning the actual implementation of the extensions, we provide some performance figures obtained from both a theoretical analysis and numerical simulations.