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Survival analysis is a crucial tool in healthcare, allowing us to understand and predict time-to-event occurrences using statistical and machine-learning techniques. As deep learning gains traction in this domain, a specific challenge emerges: neural network-based survival models often produce discrete-time outputs, with the number of discretization points being much fewer than the unique time points in the dataset, leading to potentially inaccurate survival functions. To this end, our study explores post-processing techniques for survival functions. Specifically, interpolation and smoothing can act as effective regularization, enhancing performance metrics integrated over time, such as the Integrated Brier Score and the Cumulative Area-Under-the-Curve. We employed various regularization techniques on diverse real-world healthcare datasets to validate this claim. Empirical results suggest a significant performance improvement when using these post-processing techniques, underscoring their potential as a robust enhancement for neural network-based survival models. These findings suggest that integrating the strengths of neural networks with the non-discrete nature of survival tasks can yield more accurate and reliable survival predictions in clinical scenarios.

OBJECTIVES:Because significantly higher mortality is observed in elderly patients undergoing venoarterial extracorporeal membrane oxygenation for refractory cardiogenic shock, decision-making in this setting is challenging. We aimed to elucidate predictors of unfavorable outcomes in these elderly (>= 70 yr) patients.DESIGN:Analysis of international worldwide extracorporeal life support organization registry.SETTING:Refractory cardiogenic shock due to various etiologies (cardiac arrest excluded).PATIENTS:Elderly patients (>= 70 yr).INTERVENTIONS:Venoarterial extracorporeal membrane oxygenation.MEASUREMENTS AND MAIN RESULTS:Three age groups (70-74, 75-79, >= 80 yr) were in-depth analyzed. Uni- and multivariable analysis were performed. From January 1997 to December 2018, 2,644 patients greater than or equal to 70 years (1,395 [52.8%] 70-74 yr old, 858 [32.5%] 75-79 yr, and 391 [14.8%] >= 80 yr old) were submitted to venoarterial extracorporeal membrane oxygenation for refractory cardiogenic shock with marked increase in the most recent years. Peripheral access was applied in majority of patients. Median extracorporeal membrane oxygenation support duration was 3.5 days (interquartile range: 1.6-6.1 d), (3.9 d [3.7-4.6 d] in patients >= 80 yr) (p < 0.001). Weaning from extracorporeal membrane oxygenation was possible in 1,236 patients (46.7%). Overall in-hospital mortality was estimated at 68.3% with highest crude mortality rates observed in 75-79 years old subgroup (70.1%). Complications were mostly cardiovascular and bleeding, without apparent differences between subgroups. Airway pressures, 24-hour pH after extracorporeal membrane oxygenation start, extracorporeal membrane oxygenation duration, and renal replacement therapy were predictive of higher mortality. In-hospital mortality was lower in heart transplantation recipients, posttranscatheter aortic valve replacement, and pulmonary embolism; conversely, higher mortality followed extracorporeal me

OBJECTIVES:Because significantly higher mortality is observed in elderly patients undergoing venoarterial extracorporeal membrane oxygenation for refractory cardiogenic shock, decision-making in this setting is challenging. We aimed to elucidate predictors of unfavorable outcomes in these elderly (>= 70 yr) patients.DESIGN:Analysis of international worldwide extracorporeal life support organization registry.SETTING:Refractory cardiogenic shock due to various etiologies (cardiac arrest excluded).PATIENTS:Elderly patients (>= 70 yr).INTERVENTIONS:Venoarterial extracorporeal membrane oxygenation.MEASUREMENTS AND MAIN RESULTS:Three age groups (70-74, 75-79, >= 80 yr) were in-depth analyzed. Uni- and multivariable analysis were performed. From January 1997 to December 2018, 2,644 patients greater than or equal to 70 years (1,395 [52.8%] 70-74 yr old, 858 [32.5%] 75-79 yr, and 391 [14.8%] >= 80 yr old) were submitted to venoarterial extracorporeal membrane oxygenation for refractory cardiogenic shock with marked increase in the most recent years. Peripheral access was applied in majority of patients. Median extracorporeal membrane oxygenation support duration was 3.5 days (interquartile range: 1.6-6.1 d), (3.9 d [3.7-4.6 d] in patients >= 80 yr) (p < 0.001). Weaning from extracorporeal membrane oxygenation was possible in 1,236 patients (46.7%). Overall in-hospital mortality was estimated at 68.3% with highest crude mortality rates observed in 75-79 years old subgroup (70.1%). Complications were mostly cardiovascular and bleeding, without apparent differences between subgroups. Airway pressures, 24-hour pH after extracorporeal membrane oxygenation start, extracorporeal membrane oxygenation duration, and renal replacement therapy were predictive of higher mortality. In-hospital mortality was lower in heart transplantation recipients, posttranscatheter aortic valve replacement, and pulmonary embolism; conversely, higher mortality followed extracorporeal me

Detecting buildings in the surroundings of an urban vehicle and matching them to building models available on map services is an emerging trend in robotics localization for urban vehicles. In this paper, we present a novel technique, which improves a previous work by detecting building façade, their positions, and finding the correspondences with their 3D models, available in Open-StreetMap. The proposed technique uses segmented point clouds produced using stereo images, processed by a convolutional neural network. The point clouds of the façades are then matched against a reference point cloud, produced extruding the buildings’ outlines, which are available on OpenStreetMap (OSM). In order to produce a lane-level localization of the vehicle, the resulting information is then fed into our probabilistic framework, called Road Layout Estimation (RLE). We prove the effectiveness of this proposal, testing it on sequences from the well-known KITTI dataset and comparing the results concerning a basic RLE version without the proposed pipeline.

Background: Difficulties in handwriting, such as dysgraphia, impact several aspects of a child’s everyday life. Current methodologies for the detection of such difficulties in children have the following three main weaknesses: (1) they are prone to subjective evaluation; (2) they can be administered only when handwriting is mastered, thus delaying the diagnosis and the possible adoption of countermeasures; and (3) they are not always easily accessible to the entire community. Objective: This work aims at developing a solution able to: (1) quantitatively measure handwriting features whose alteration is typically seen in children with dysgraphia; (2) enable their study in a preliteracy population; and (3) leverage a standard consumer technology to increase the accessibility of both early screening and longitudinal monitoring of handwriting difficulties. Methods: We designed and developed a novel tablet-based app Play Draw Write to assess potential markers of dysgraphia through the quantification of the following three key handwriting laws: isochrony, homothety, and speed-accuracy tradeoff. To extend such an approach to a preliteracy age, the app includes the study of the laws in terms of both word writing and symbol drawing. The app was tested among healthy children with mastered handwriting (third graders) and those at a preliterate age (kindergartners). Results: App testing in 15 primary school children confirmed that the three laws hold on the tablet surface when both writing words and drawing symbols. We found significant speed modulation according to size (P<.001), no relevant changes to fraction time for 67 out of 70 comparisons, and significant regression between movement time and index of difficulty for 44 out of 45 comparisons (P<.05, R2>0.28, 12 degrees of freedom). Importantly, the three laws were verified on symbols among 19 kindergartners. Results from the speed-accuracy exercise showed a significant evolution with age of the global movement time

The estimation of the 3D trajectory, the class and the dimensions of a vehicle represents three relevant tasks for traffic monitoring. They are usually performed by separate sub-systems and only few existing algorithms cope with the three tasks at the same time. However, if these tasks are integrated, the trajectory estimation enforces the classification with temporal consistency, and at the same time, the estimation of the vehicle class and dimensions can be used to increase the trajectory estimate accuracy. In this work, we propose an algorithm to estimate the 3D trajectory, the class and the dimensions of vehicles simultaneously by means of a Backward-Simulation Particle Smoother whose state contains both continuous (vehicle pose and dimensions), and discrete (vehicle class) quantities. To integrate the class estimate in the Particle Smoother we model the class prediction as a Markov Chain. We performed experimental tests on both simulated and real datasets; they show that the pose and dimension estimation reaches centimeter-accuracy and the classification accuracy is higher than 95 %.

Microfluidic mixing is an important means for in-situ sample preparation and handling while Small Angle X-Ray Scattering (SAXS) is a proven tool for characterising (macro-)molecular structures. In combination those two techniques enable investigations of fast reactions with high time resolution (< 1 ms). The goal of combining a micro mixer with SAXS, however, puts constraints on the materials and production methods used in the device fabrication. The measurement channel of the mixer needs good X-ray transparency and a low scattering background. While both depend on the material used, the requirement for low scattering especially limits the techniques suitable for producing the mixer, as the fabrication process can induce molecular orientations and stresses that can adversely influence the scattering signal. Not only is it important to find a production method that results in a device with low background scattering, but it also has to be versatile enough to produce appropriate mixer designs. Here we discuss two methods – laser ablation of polycarbonate and injection moulding of Topas – which were found suitable for our needs, provided care is taken in aligning the mixing/reaction channel, where the actual measurements will be carried out. We find injection moulding to be the better of the two methods.

One of the common approaches to the automatic emotion recognition problem is based on biological signal analysis. In this context, this paper aims at identifying the biological component related to levels of arousal of the subjects, and to use such a component to automatically discriminate among these levels. We have formalized the automatic emotion recognition as a classification problem. In order to allow the system to generalize over different subjects, we addressed two crucial aspects of the procedure: normalization and cross-validation. Assuming that different subjects could react differently to the same stimuli, we defined a distance metric between their models. We performed an experiment where 14 volunteers were stimulated by means of the IAPS set of pictures divided into classes of increasing arousal intensity. Under the effect of these external visual stimuli, subjects exhibited a principal component in their autonomic space that accounts for full class separability. Moreover we observed that some of the subjects' data can be represented by the same model, while others have to be represented differently possibly due to a poor induction mechanism. This work demonstrates the possibility to build a model able to generalize over different subjects without over-fitting, but we have to guarantee that data used to build the model represent sufficiently well the measured phenomena.

The present article intends to provide an update of the article "Focus on prevention, diagnosis and treatment of hypertension in children and adolescents" published in 2013 (Spagnolo et al., Ital J Pediatr 39:20, 2013) in this journal. This revision is justified by the fact that during the last years there have been several new scientific contributions to the problem of hypertension in pediatric age and during adolescence. Nevertheless, for what regards some aspects of the previous article, the newly acquired information did not require substantial changes to what was already published, both from a cultural and from a clinical point of view. We felt, however, the necessity to rewrite and/or to extend other parts in the light of the most recent scientific publications. More specifically, we updated and extended the chapters on the diagnosis and management of hypertension in newborns and unweaned babies, on the use and interpretation of ambulatory blood pressure monitoring, and on the usefulness of and indications for physical activity. Furthermore, we added an entirely new section on the role that simple carbohydrates (fructose in particular) and uric acid may play in the pathogenesis of hypertension in pediatric age.

Within computer science, autonomous robotics takes the uneasy role of a discipline where the features of both systems (i.e., robots) and their operating environment (i.e., the physical world) conspire to make the application of the experimental scientific method most difficult. This is the reason why much experimental work in robotics is, from the methodological point of view, built on shaky grounds. In particular, scientifically sound benchmarking tools are still largely missing. This chapter starts from Rawseeds, a project focused precisely on benchmarking in robotics, to highlight the reasons for these difficulties and to propose strategies for overcoming some of them. The main result of Rawseeds is a Benchmarking Toolkit: a readily usable instrument to assess and compare algorithms for SLAM, localization, and mapping. Its most innovative aspects include a set of high-quality, validated, multi-sensor datasets, collected both in indoor and in outdoor locations and complemented by ground truth data, and the explicit definition of a set of quantitative performance metrics for the evaluation of algorithms.

Background subtraction is the classical approach to differentiate moving objects in a scene from the static background when the camera is fixed. If the fixed camera assumption does not hold, a frame registration step is followed by the background subtraction. However, this registration step cannot perfectly compensate camera motion, thus errors like translations of pixels from their true registered position occur. In this paper, we overcome these errors with a simple, but effective background subtraction algorithm that combines Temporal and Spatio-Temporal approaches. The former models the temporal intensity distribution of each individual pixel. The latter classifies foreground and background pixels, taking into account the intensity distribution of each pixels' neighborhood. The experimental results show that our algorithm outperforms the state-of-the-art systems in the presence of jitter, in spite of its simplicity. © 2013 Springer-Verlag Berlin Heidelberg.

The creation of intelligent robots has been a major goal of Artificial Intelligence since the early days and has provided many motivations to Artificial Intelligence researchers. Therefore, a large body of research has been done in this field and many relevant results have shown that integration of Artificial Intelligence and Robotics techniques is a viable approach towards this goal. This article summarizes the efforts and the achievements of several Italian research groups in the development of intelligent robotic systems characterized by a suitable integration of Artificial Intelligence and Robotic techniques. The contributions collected in this article show the long history of this research stream, the impact of the developed approaches in the scientific community, and the efforts towards actual deployment of the developed systems.

Simultaneous Localization and Mapping (SLAM) has received quite a lot of attention in the last decades because of its relevance for many applications centered on a mobile observer, such as service robotics and intelligent transportation systems. This paper focuses on the use of recursive Bayesian filtering, as implemented by the Extendend Kalman Filter (EKF), to face the Visual SLAM problem, i.e., when using data from visual sources. In Monocular SLAM, which uses a single camera as unique source of information, it is not possible to directly estimate the depth of a feature from a single image. To handle the severely non-normal distribution representing such uncertainty, inverse parametrizations were developed, capable to deal with such uncertainty and still relying on Gaussian variables. In the paper, after an introduction to EKF-SLAM, we provide a review of different inverse parametrizations, and we introduce a novel proposal, the Framed Inverse Depth (FID) parametrization, which, in terms of consistency, performs similarly to state of the art Monocular SLAM parametrizations, but at a reduced computational cost. All these parametrizations can be used in a stereo and multi camera setting too. An extensive analysis is presented for both Monocular and stereo SLAM, for a simulated environment widely used in the literature as well as on a widely used real dataset.

The European Society of Hypertension has recently published its recommendations on prevention, diagnosis and treatment of high blood pressure in children and adolescents. Taking this contribution as a starting point the Study Group of Hypertension of the Italian Society of Pediatrics together with the Italian Society of Hypertension has conducted a reappraisal of the most recent literature on this subject. The present review does not claim to be an exhaustive description of hypertension in the pediatric population but intends to provide Pediatricians with practical and updated indications in order to guide them in this often unappreciated problem.This document pays particular attention to the primary hypertension which represents a growing problem in children and adolescents. Subjects at elevated risk of hypertension are those overweight, with low birth weight and presenting a family history of hypertension. However, also children who do not present these risk factors may have elevated blood pressure levels. In pediatric age diagnosis of hypertension or high normal blood pressure is made with repeated office blood pressure measurements that show values exceeding the reference values. Blood pressure should be monitored at least once a year with adequate methods and instrumentation and the observed values have to be interpreted according to the most updated nomograms that are adjusted for children's gender, age and height. Currently other available methods such as ambulatory blood pressure monitoring and home blood pressure measurement are not yet adequately validated for use as diagnostic instruments. To diagnose primary hypertension it is necessary to exclude secondary forms. The probability of facing a secondary form of hypertension is inversely proportional to the child's age and directly proportional to blood pressure levels. Medical history, clinical data and blood tests may guide the differential diagnosis of primary versus secondary forms. The prevention of hi

In mobile robotics, localization plays a key role in every task assigned to a robot. This report describes the probabilistic module we developed to solve the localization problem in our autonomous driving vehicle. Our method uses a complete 6DoF approach within a 3D motion model, in order to correctly integrate the error caused by dead reckoning. Furthermore, the description of the environment is a 3D voxel representation. We base on particle filtering, in the localization process. An ad-hoc simulation environment has been developed, for testing the effectiveness of the motion model. Finally, we performed some field experiments, to demonstrate that our approach consistently locates the 6DoF position of the vehicle along the driven path on the test site

The Femtoprint project, a European project, aims at demonstrating the use of low-energy laser pulses (i.e. below the ablation threshold) to manufacture monolithically integrated devices including optofluidic, optomechanical and photonic devices. The longer-term objective is the implementation of a versatile table-top machining center. This paper summarizes the project progress to date and demonstrates the potential of this approach through various illustrative examples.

The Femtoprint project, a European project, aims at demonstrating the use of low-energy laser pulses (i.e. below the ablation threshold) to manufacture monolithically integrated devices including optofluidic, optomechanical and photonic devices. The longer-term objective is the implementation of a versatile table-top machining center. This paper summarizes the project progress to date and demonstrates the potential of this approach through various illustrative examples.

In the last years, the Monocular SLAM problem was widely studied, to allow the simultaneous reconstruction of the environment and the localization of the observer, by using a single camera. As for other SLAM problems, a frequently used feature for the representation of the world, is the 3D point. Differently from other SLAM problems, because of the perspective model of the camera, in Monocular SLAM, features cannot be completely perceived and initialized from a single measurement. To solve this issue, different parameterizations have been proposed in the literature, which try to solve also another problem in Monocular SLAM, i.e., the distortion of the Gaussian uncertainty in depth estimation that takes place because of the non- linear measurement model. In this paper, we start from recent results in consistency analysis for these parameterizations to propose a novel approach to improve EKF-based Monocular SLAM even further. Our claims are sustained by an extended validation on simulated and real data.

Recent works have shown that it is possible to solve the Simultaneous Localization And Mapping problem using an Extended Kalman Filter and a single perspective camera. The principal drawback of these works is an inaccurate modeling of measurement uncertainties, which therefore causes inconsistencies in the filter estimations. A possible solution to proper uncertainty modeling is the Unified Inverse Depth parametrization. In this paper we propose the Inverse Scaling parametrization that still allows an un-delayed initialization of features, while reducing the number of needed parameters and simplifying the measurement model. This novel approach allows a better uncertainty modeling of both low and high parallax features and reduces the likelihood of inconsistencies. Experiments in simulation demonstrate that the use of the Inverse Scaling solution improves the performance of the monocular EKF SLAM filter when compared with the Unified Inverse Depth approach; experiment on real data confirm the applicability of the idea

A trustable and accurate ground truth is a key requirement for benchmarking self-localization and mapping algorithms; on the other hand, collection of ground truth is a complex and daunting task, and its validation is a challenging issue. In this paper we propose two techniques for indoor ground truth collection, developed in the framework of the European project Rawseeds, which are mutually independent and also independent on the sensors onboard the robot. These techniques are based, respectively, on a network of fixed cameras, and on a network of fixed laser scanners. We show how these systems are implemented and deployed, and, most importantly, we evaluate their performance; moreover, we investigate the possible fusion of their outputs. © 2009 Springer Science+Business Media, LLC.

We present the first results of microstructure replica with re-usable masters in poly vinyl alcohol (PVA). Microneedles of heights ranging from 500 to 1100 pm were fabricated through double-exposure deep Xray lithography (DXRL) process and successfully replicated. A single PVA master was used as a template to successfully replicate up to 10 PMMA microneedle arrays, suggesting that this method might be a possible alternative to PDMS processes. A preliminary characterization of the relationship between the surface roughness of the substrates and the force required for demoulding was also performed. (C) 2009 Elsevier B.V. All rights reserved.

In this paper we face the issue of fusing 3D data from different sensors in a seamless way, using the unifying framework of uncertain projective geometry. Within this framework it is possible to describe, combine, and estimate various types of geometric elements (2D and 3D points, 2D and 3D lines, and 3D planes) taking their uncertainty into account. Because of the size of the data involved in this process, the integration process and thus the SLAM algorithm turns out to be very slow. For this reason, in this work, we propose the use of an R*-Tree data structure to speed up the whole process, managing in an efficent way both the estimated map and the 3D points clouds coming out from the stereo camera. The experimental section shows that the use of uncertain projective geometry and the R*-Tree data structure improves the mapping and the pose estimation

The recent literature has shown that it is possible to solve the monocular Simultaneous Localization And Mapping using both undelayed features ini- tialization and an Extedend Kalman Filter. The key concept, to achieve this result, was the introduction of a new parametrization called Unified Inverse Depth that produces measurements equations with a high degree of linear- ity and allows an efficient and accurate modeling of uncertainties. In this paper we present a monocular EKF SLAM filter based on an alternative parametrization, i.e., the Inverse Scaling Parametrization, characterized by a reduced number of parameters, a more linear measurement model, and a bet- ter modeling of features uncertainty for both low and high parallax features. Experiments in simulation demonstrate that the use of the Inverse Scaling solution improves the monocular EKF SLAM filter when compared with the Unified Inverse Depth approach, while experiments on real data show the system working in practice as well.

In this study, we assess the possibility of using ground penetrating radar (GPR) and electrical resistivity tomography (ERT) as indirect non-destructive techniques for root detection. Two experimental sites were investigated: a poplar plantation [mean height of plants 25.7 m, diameter at breast height (dbh) 33 cm] and a pinewood forest mainly composed of Pinus pinea L. and Pinus pinaster Ait. (mean height 17 m, dbh 29 cm). GPR measures were taken using antennas of 900 and 1500 MHz applied in square and circular grids. ERT was previously tested along 2-D lines, compared with GPR sections and direct observation of the roots, and then using a complete 3-D acquisition technique. Three-dimensional reconstructions using grids of electrodes centred and evenly spaced around the tree were used in all cases (poplar and pine), and repeated in different periods in the pine forest (April, June and September) to investigate the influence of water saturation on the results obtainable. The investigated roots systems were entirely excavated using AIR-SPADE Series 2000. In order to acquire morphological information on the root system, to be compared with the GPR and ERT, poplar and pine roots were scanned using a portable on ground scanning LIDAR. In test sections analysed around the poplar trees, GPR with a high frequency antenna proved to be able to detect roots with very small diameters and different angles, with the geometry of survey lines ruling the intensity of individual reflectors. The comparison between 3-D images of the extracted roots obtained with a laser scan data point cloud and the GPR profile proved the potential of high density 3-D GPR in mapping the entire system in unsaturated soil, with a preference for sandy and silty terrain, with problems arising when clay is predominant. Clutter produced by gravel and pebbles, mixed with the presence of roots, can also be sources of noise for the GPR signals. The work performed on the pine trees shows that the shape, distribu

A landslide susceptibility analysis is performed by means of Artificial Neural Network (ANN) and Cluster Analysis (CA). This kind of analysis is aimed at using ANNs to model the complex non linear relationships between mass movements and conditioning factors for susceptibility zonation, in order to identify unstable areas. The proposed method adopts CA to improve the selection of training, validation, and test records from data, managed within a Geographic Information System (GIS). In particular, we introduce a domain-specific distance measure in cluster formation. Clustering is used in data pre-processing to select non landslide records and is performed on the whole dataset, excluding the test set landslides. Susceptibility analysis is carried out by means of ANNs on the so-generated data and compared with the common strategy to select random non-landslide samples from pixels without landslides. The proposed method has been applied in the Brembilla Municipality, a landslide-prone area in the Southern Alps, Italy. The results show significant differences between the two sampling methods: the classification of the test set, previously separated and excluded from the training data, is always better when the non-landslide patterns are obtained using the proposed cluster sampling. The case study validates that, by means of a domain-specific distance measure in cluster formation, it is possible to introduce expert knowledge into the black-box modelling method, implemented by ANNs, to improve the predictive capability and the robustness of the models obtained.

A method is proposed for calibrating Catadioptric Omnidirectional Vision Systems. This method, similarly to classic camera calibration, makes use of a set of fiducial points to find the parameters of the geometric image formation model. The method makes no particular assumption regarding the shape of the mirror or its position with respect to the camera. Given the camera intrinsic parameters and the mirror profile, the mirror pose is computed using the projection of the mirror border and, eventually, the extrinsic parameters are computed by minimizing distance between fiducial points and back-projected images

Self localization and mapping with vision is still an open research field. Since redundancy in the sensing suite is too expensive for consumer-level robots, we base on vision as the main sensing system for SLAM. We approach the problem with 3D data from a trinocular vision system. Past experience shows that problems arise as a consequence of inaccurate modeling of uncertainties; interestingly enough, we found that accuracy in modeling the robot pose uncertainty is much less relevant than for the uncertainty on the sensed data. To overcome the severe limitation of linear and Gaussian approximations, we applied a particle-based description of the inherently nonnormal probability density distribution of the sensed data; the aim is to increase the success rate of data association, which we see as the most important problem. The increase in correct data associations reduces the uncertainty in the model and, consequently, in the robot pose, respectively estimated with a hierarchical map decomposition and a six degree of freedom extended Kalman filter. In this paper, we present approaches for particle-based sensor modeling and data association, with a comparative experimental evaluation on real 3D vision data.

Descrizione della piattaforma robotica mobile per le attività di raccolta dati indoor del progetto EEC RAWSEEDS. Il robot effettuava raccolte dati multi-sensoriali sincronizzate tra loro e con il sistema esterno di raccolta della Ground-Truth della robot pose. L'elenco di sensori a bordo e dei dati da questi generati, da raccogliersi senza perdite, e' ancora oggi impressionante. Si sono risolti svariati problemi, dalla meccanica, alla elettronica analogica, digitale, di software di base e di alto livello, di algoritmi di percezione robotica per la calirazione ei parametri, soprattutto geometrici. La attività è stata svolta per il supporto al benchmarking degli algoritmi di SLAM per robotica mobile.

We have investigated the electronic structure of Sr2FeMoO6. In order to probe the partial densities of states we applied soft x-ray emission spectroscopy (XES) to the Fe L, the Mo M and the O K edges. We discuss the results in the light of complementary measurements of the valence band by means of x-ray photoelectron spectroscopy (XPS) and first-principles generalized gradient approximation (GGA) and LDA + U band structure calculations [1]. [1] K. Kuepper et al., J. Phys.: Condens. Matter 17, 4309 (2005).

We have investigated the electronic structure of Sr2FeMoO6. In order to probe the partial densities of states we applied soft x-ray emission spectroscopy (XES) to the Fe L, the Mo M and the O K edges. We discuss the results in the light of complementary measurements of the valence band by means of x-ray photoelectron spectroscopy (XPS) and first-principles generalized gradient approximation (GGA) and LDA + U band structure calculations [1]. [1] K. Kuepper et al., J. Phys.: Condens. Matter 17, 4309 (2005).

In this paper we propose an approach to color classification and image segmentation in non-stationary environments. Our goal is to cope with changing illumination condition by on-line adapting both the parametric color model and its structure/complexity. Other authors used parametric statistics to model color distribution in segmentation and tracking problems, but with a fixed complexity model. Our approach is able to on-line adapt also the complexity of the model, to cope with large variations in the scene illumination and color temperature.

This paper focuses on on-line anomaly detection in video traffic surveillance systems. Markov chain (MC) have been proposed already in computer and network intrusion detection. We applied them to the traffic domain and we propose to extend the classical MC (modeling all the behaviors in the scene) with an approach that evaluates in parallel a set of behavior specific MC. Such separate MCs are more discriminatory than a single MC for all the behaviors, allowing our approach to detect anomalies resulting from joining segments of normal behaviors. The learning of such models is done by using sequences of labeled normal behaviors and discretizing the image plane by using a simple grid. The approach has been validated on traffic surveillance videos, and experimental results show good performance both in terms of precision and recall.

Objectives: Surgical ventricular reverse remodeling has been shown to possibly improve hemodynamics and symptoms, but effects on long-term mortality are not established. No consistent data are available on which patients will benefit most from this procedure. This study was designed to analyze the predictors of long-term survival after surgical ventricular reverse remodeling in patients with ischemic cardiomyopathy. Methods: Eighty-five patients who underwent surgical ventricular reverse remodeling between May 1991 and October 2003 were retrospectively analyzed. Left ventricular wall motion and left ventricular equatorial diameter were assessed by means of angioventriculography. Left ventricular ejection fraction and volumes were measured by means of echocardiography. Cox regression analysis was used in several combinations to create a final model for identifying predictors of death. Results: Actuarial survival after 1, 3, 5, and 10 years was 89%, 79%, 75%, and 75%, respectively. New York Heart Association class improved from 2.9 ± 1.0 to 1.3 ± 0.5 (P < .0001), left ventricular ejection fraction increased from 27.6% ± 6.3% to 43.0% ± 10.1% (P < .0001), and left ventricular end-systolic volume index decreased from 89.6 ± 27.6 mL/m2 to 56.5 ± 34.5 mL/m2 (P < .0001). Multivariate analysis identified left ventricular equatorial diameter of 70 mm or greater (hazard ratio, 5.28; 95% confidence interval, 1.79-11.71; P = .020) and segmental akinesia (hazard ratio, 4.46; 95% confidence interval, 1.23-17.12; P = .024) as the only independent predictors of death. Conclusions: In this analysis of a single cohort of patients, surgical ventricular reverse remodeling improves the symptoms of ischemic cardiomyopathy, as well as left ventricular function, shape, and volume, with encouraging long-term outcomes, particularly in patients with dyskinesia. A left ventricular equatorial diameter of 70 mm or greater appears to be an important independent prognostic factor, which s

Partially or completely paralyzed patients can benefit from advanced neuro-prostheses in which a continuous recording of electroencephalogram (EEG) is required, operating some processing and classification to control a computer (BCI, Brain-Computer Interfaces). Patients are so allowed to control external devices or to communicate simple messages through the computer, just concentrating their attention on codified movements or on a letter or icon on a digital keyboard. Conventional electrodes usually require skin preparation and application of electrolytic gel for high quality low amplitude biopotentials recordings and are not suitable for being easily used by patient or caregivers at home in BCI or equivalent systems. In this report we describe the fabrication and characterization of dry (gel not required), non-invasive, user-friendly biopotential electrodes. The electrodes consist of a bidimensional array of micro-needles designed to pierce the first dielectric skin layer (stratum corneum) and establishing a direct contact with the living and electrical conducting cells in the epidermis (no blood vessels and nerve terminations). The easy and immediate application of the spiked electrodes makes them also attractive for every surface long-term biosignal measurements, even at patient's home (EEG, electrocardiogram, etc). © 2006 IEEE.

In this paper we present a proposal for setting camera parameters which we claim to give results better matched to applications in color-coded environments then the camera internal algorithms. Moreover it does not require online human intervention, i.e. is automated, and is faster than a human operator. This work applies to situations where the camera is used to extract information from a color-coded world. The experimental activity presented has been performed in the framework of Robocup mid-size rules, with the hypothesis of temporal constancy of light conditions; this work is the necessary first step toward dealing with slow changes, in the time domain, of light conditions.

Takayasu's arteritis (TA) is a chronic large vessel vasculitis, first described in 1908 by the Japanese ophthalmologist Takayasu, affecting more frequently young women. The typical lesions represented by aneurysmatic dilatation or obliteration, and narrowing of the arterial lumen may pose technical difficulties as well as demanding decision making for treatment. Since Jarvell first described aortic regurgitation in TA in 1954, unresolved problems still remain for aortic valve replacement, particularly because of the young age of these patients. We report the case of a 28-year-old woman, with a known history of TA, referred to our department. © 2004 by The Society of Thoracic Surgeons.

We present Fun2maS, the Milan Robocup Team. In its implementation we have faced many aspects: hardware (electronics, mechanics), sensors (omnidirectional vision), behaviors (fuzzy behaviors management and composition), multi-agent coordination (strategy and tactics), and adaptation of the team behavior. We could fully exploit the characteristics of all the components thanks to the modular design approach we have adopted. All the modules have also been designed to be used in generic applications, and we have already adopted most of them for surveillance, mapping, guidance, and document delivery tasks. © 2002 Springer-Verlag Berlin Heidelberg.

Background. Stentless bioprostheses and homografts show better hemodynamic profiles compared with conventional stented bioprostheses and mechanical valves. Few data are available on stentless aortic valve implantation for patients with severe left ventricular dysfunction. The aim of this retrospective study was to assess the potential benefits of stentless aortic valve implantation for patients undergoing isolated aortic valve replacement with left ventricular ejection fraction ≤ 35%. Methods. From November 1988 through March 2000, 53 patients (45 men and 8 women, aged 64.2 ± 15.2 years) with a LVEF ≤ 35% (mean EF, 28.7 ± 5.4%) underwent isolated, primary aortic valve replacement for chronic aortic valve disease. Twenty patients received stentless aortic valves and 33 patients received conventional stented bioprostheses and mechanical valves. Predictive factors for LVEF recovery at echocardiographic follow-up (36.2 ± 32.1 months) were analyzed by simple and multiple regression analysis. Results. There were no significant differences between groups in early and late mortality. Stentless aortic valve implantation required a longer aortic cross-clamp time (p = 0.037). The stentless aortic valve group showed a better LVEF recovery (p = 0.016). Stentless aortic valves had a larger indexed effective orifice area compared with conventional stented bioprostheses and mechanical valves (p < 0.0001). A smaller indexed effective orifice area (p = 0.0008), chronic obstructive pulmonary disease (p = 0.015), and implantation of a conventional stented bioprosthesis or mechanical valve (p = 0.016) were related to reduced LVEF recovery by univariate analysis. A larger indexed effective orifice area (p = 0.024) was an independent predictive factor for a better LVEF recovery by multivariate analysis. Conclusions. Stentless aortic valve implantation for patients with severe left ventricular dysfunction, even if technically more demanding, is a safe procedure that warrants a larger in