Angelo Cenedese

In this paper, we consider the problem of designing and implementing 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 is required in 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. Formally based on a symmetric random gossip strategy, an open-source application designed to implement the distributed algorithm is also presented. Finally, the effectiveness of the proposed algorithm is illustrated through the application to radio-frequency localization experiments in a real world testbed, comparing its performances with those of a centralized strategy.

Tissue engineering of autologous lung tissue aims to become a therapeutic alternative to transplantation. Efforts published so far in creating scaffolds have used harsh decellularization techniques that damage the extracellular matrix (ECM), deplete its components and take up to 5 weeks to perform. The aim of this study was to create a lung natural acellular scaffold using a method that will reduce the time of production and better preserve scaffold architecture and ECM components.  Decellularization of rat lungs via the intratracheal route removed most of the nuclear material when compared to the other entry points. An intermittent inflation approach that mimics lung respiration yielded an acellular scaffold in a shorter time with an improved preservation of pulmonary micro-architecture. Electron microscopy demonstrated the maintenance of an intact alveolar network, with no evidence of collapse or tearing. Pulsatile dye injection via the vasculature indicated an intact capillary network in the scaffold. Morphometry analysis demonstrated a significant increase in alveolar fractional volume, with alveolar size analysis confirming that alveolar dimensions were maintained. Biomechanical testing of the scaffolds indicated an increase in resistance and elastance when compared to fresh lungs. Staining and quantification for ECM components showed a presence of collagen, elastin, GAG and laminin. The intratracheal intermittent decellularization methodology could be translated to sheep lungs, demonstrating a preservation of ECM components, alveolar and vascular architecture. Decellularization treatment and methodology preserves lung architecture and ECM whilst reducing the production time to 3 hours. Cell seeding and in vivo experiments are necessary to proceed towards clinical translation.

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.

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.

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

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.

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.

In this work the design and implementation of an application to track multiple agents in a indoor Wireless Sensor Actor Network (WSAN) is proposed. We developed a tracking algorithm that falls into the category of the radio frequency localization/tracking methods, that exploit the strength of the wireless communications among fixed and mobile agents to establish the position of the mobile ones. The algorithm resorts to an Extended Kalman Filter to process the agents measurements and reach a desired level of tracking performance. The tracking application, namely Teseo, is composed by a low-level NesC management software for the agents side and a Java graphical interface provided to users connected to mobile agents. A detailed description of the operations performed by Teseo is given, accompanied both by simulations to validate the tracking algorithm and experiments on a real testbed to test Teseo.

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.

Mutations in the survival of motor neuron gene (SMN1) are responsible for spinal muscular atrophy (SMA), a fatal neuromuscular disorder. Mice carrying a homozygous deletion of Smn exon 7 directed to skeletal muscle (HSA-Cre, Smn^F7/F7 mice) present clinical features of human muscular dystrophies for which new therapeutic approaches are highly warranted. Herein we demonstrate that tail vein transplantation of mouse amniotic fluid stem (AFS) cells enhances the muscle strength and improves the survival rate of the affected animals. Secondly, after cardiotoxin injury of the Tibialis Anterior, only AFS-transplanted mice efficiently regenerate. Most importantly, secondary transplants of satellite cells (SC) derived from treated mice show that AFS cells integrate into the muscle stem cell compartment, and have long term muscle regeneration capacity indistinguishable from that of wild type-derived SC. This is the first study demonstrating the functional and stable integration of AFS cells into the skeletal muscle, highlighting their value as cell source for the treatment of muscular dystrophies.

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.

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.

A new boundary reconstruction procedure is presented and validated against ITER nominal equilibria. An approximation 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.

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.

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.

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.

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.

Simulating the turbulence effect on ground telescope observations is of fundamental importance for the design and test of suitable control algorithms for adaptive optics systems. In this paper we propose a multiscale approach for efficiently synthesizing turbulent phases at very high resolution. First, the turbulence is simulated at low resolution, taking advantage of a previously developed method for generating phase screens. Then, high-resolution phase screens are obtained as the output of a multiscale linear stochastic system. The multiscale approach significantly improves the computational efficiency of turbulence simulation with respect to recently developed methods. Furthermore, the proposed procedure ensures good accuracy in reproducing the statistical characteristics of the turbulent phase.

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.