Sviluppo di software per centralina di contollo motore tramite Simulink e Real-Time Workshop Embedded Coder

Quando si lavora con sistemi embedded real-time le tematiche da affrontare e sviscerare per ottenere dei buoni risultati sono molteplici. Il lavoro di tesi parte analizzando la fase di produzione del codice da parte del Real-Time Workshop per finire con la realizzazione di un target custom ottenuto modificando il target per Infineon C166 esistente. Nella tesi viene mostrata l’architettura software dell’ambiente di cross-sviluppo Keil e l’architettura hardware della famiglia di microcontrollori Infineon C166. Vengono poi sviluppati diversi blocchetti Simulink per ottenere il controllo delle periferiche integrate nel microcontrollore al fine di utilizzarle per realizzare il software di centralina. La tesi mostra quindi la possibilit&agrave di generare il codice di controllo della centralina interamente attraverso Simulink senza dover scrivere nessuno spezzone di codice a mano. Questo permette di simulare e modificare a piacimento il modello senza dover ricorrere ad una programmazione a basso livello per ottenere l’applicazione richiesta

RFID Gazebo-Based Simulator With RSSI and Phase Signals for UHF Tags Localization and Tracking

Radio Frequency Identification (RFID) technology is becoming very popular in the new era of Industry 4.0, especially for warehouse management, retails, and logistics. RFID systems can be used for objects identification, localization, and tracking, facilitating everyday operators' efforts. However, the deployment of RFID tags and reader antennas in real-world application scenarios is crucial and takes time. Indeed, deciding where to place tags and/or readers' requires examining many conditions. If some weaknesses appear in the design, the arrangement must be reconsidered. The proposed work presents a novel open-source RFID simulator that allows modeling environments and testing the deployment of RFID tags and antennas apriori. In such a way, validating the performance of the localization or tracking algorithms in simulation, possible weaknesses that could arise may be fixed before facilities are applied on the field. Any number of tags and antennas can be placed in any position in the created scenario, and the simulator provides the phase and the RSSI signals for each tag. Every reader antenna is parametrized so that different antennas of different vendors can be reproduced. The simulator is implemented as a plugin of Gazebo, a widely used robotic framework integrated with the Robot Operating System (ROS), to reach a broad audience. In order to validate the simulator, a warehouse scenario is modeled, and a tag localization algorithm that uses the phase unwrapping technique and hyperbolae intersection method employing a reader antenna mounted on a mobile robot is used to estimate the position of the tags deployed in the scenario. The outcomes of the experiments showed realistic results

Real-Time Numerical Simulation for Accurate Soft Tissues Modeling during Haptic Interaction

The simulation of fabrics physics and its interaction with the human body has been largely studied in recent years to provide realistic-looking garments and wears specifically in the entertainment business. When the purpose of the simulation is to obtain scientific measures and detailed mechanical properties of the interaction, the underlying physical models should be enhanced to obtain better simulation accuracy increasing the modeling complexity and relaxing the simulation timing constraints to properly solve the set of equations under analysis. However, in the specific field of haptic interaction, the desiderata are to have both physical consistency and high frame rate to display stable and coherent stimuli as feedback to the user requiring a tradeoff between accuracy and real-time interaction. This work introduces a haptic system for the evaluation of the fabric hand of specific garments either existing or yet to be produced in a virtual reality simulation. The modeling is based on the co-rotational Finite Element approach that allows for large displacements but the small deformation of the elements. The proposed system can be beneficial for the fabrics industry both in the design phase or in the presentation phase, where a virtual fabric portfolio can be shown to customers around the world. Results exhibit the feasibility of high-frequency real-time simulation for haptic interaction with virtual garments employing realistic mechanical properties of the fabric materials

Family-based treatment of pediatric obesity in low-income minority youth: Strategies, outcomes, and novel predictors of success.

Childhood obesity remains a pressing public health issue. Low-income minority children are disproportionately affected by obesity and disparities are potentially widening in these populations. Multicomponent family-based behavioral group (FBBG) treatment programs, which use comprehensive approaches to target multiple health behaviors, currently provide the strongest evidence for childhood obesity treatment. However, low-income minority children are not adequately represented in these studies. To address this gap, the primary aim of this study was to examine the effect of Healthy Hawks (HH), a standard FBBG treatment program, in low-income minority youth 2-19 years of age who are overweight or obese. Parent-child dyads were recruited from various urban clinics to participate in a standard 12-week FBBG program targeting diet, physical activity, and related weight-change skills. Child body mass index (BMI) percent above the 95th percentile (%BMIp95) was measured as the primary outcome at baseline, post-intervention (12-week), and 1-year follow-up. Findings from this primary aim suggest that HH is effective in improving %BMIp95 at post-intervention (n=201; β=-1.29 (0.37), p<0.001), but effects are not maintained at 1-year (n=115; β=-0.51 (1.06), p=0.64). The secondary aims of this study tested two novel strategies to improve child weight outcomes: 1) Technology adjuncts (physical activity app and web-based health coaching sessions) were added subsequently alongside the standard HH program in two cohorts; 2) the Healthy Hawks Primary Plus (HHP+) program was developed and implemented by recruiting participants from a single pediatric clinic and engaging primary care providers to administer visits between post-intervention and 1-year follow-up. Three cohorts have participated in HHP+. HHP+ participants (n=34) had significantly higher retention at 1-year follow-up compared to HH (HH: 38.3%, HHP+: 73.9%, χ2 =20.59, p≤0.001) and greater child %BMIp95 reductions at 1-year (β=-3.24(1.48), p=0.03). The cohort that received both technology adjuncts had significantly greater %BMIp95 reductions at post-intervention compared to HH, which received no technology (n=18, β=-2.42 (0.83), p=0.004). This research addresses several important gaps in the existing child obesity treatment literature and provides innovative targets for improving outcomes in high-risk populations. These approaches can be used to bolster future child obesity intervention efforts in populations most in need of efficacious treatment.Doctor of Philosoph

A 6-DOF haptic manipulation system to verify assembly procedures on CAD models

During the design phase of products and before going into production, it is necessary to verify the presence of mechanical plays, tolerances, and encumbrances on production mockups. This work introduces a multi-modal system that allows verifying assembly procedures of products in Virtual Reality starting directly from CAD models. Thus leveraging the costs and speeding up the assessment phase in product design. For this purpose, the design of a novel 6-DOF Haptic device is presented. The achieved performance of the system has been validated in a demonstration scenario employing state-of-the-art volumetric rendering of interaction forces together with a stereoscopic visualization setup

Making the Economic Value Proposition for Pharmacist Comprehensive Medication Management (CMM) in Primary Care: A Conceptual Framework

Introduction: Comprehensive medication management (CMM) is a patient care process provided by clinical pharmacists in primary care settings that ensures optimal use of medications with timely follow-up. Despite widespread evidence that shows CMM improves clinical and medication-related outcomes, pharmacist CMM services often fail to be adopted into U.S. primary care settings. Objective: This study presents a conceptual framework linking outcomes of pharmacist-delivered CMM services in primary care settings to financial benefits for both primary care practices investing and implementing CMM and health plans providing coverage of CMM services.Methods: A narrative literature review was performed in PubMed and the gray literature to identify financing opportunities that justify the coverage of CMM by third party health plan administrators or the implementation of CMM by primary care practices. Financing elements that could be impacted by pharmacist-led CMM outcomes, namely higher achievement of medication-related quality measures and reduction of total costs of care, were recorded and utilized to develop the conceptual framework.Results: The framework suggests that CMM may provide economic benefits to health plans and care practices by (1) increasing market competitiveness through higher quality ratings, accreditation recognition, lower premiums and plan bids, and increased patient satisfaction; and (2) increasing revenue through quality bonus payments, shared savings agreements, and fee-for-service reimbursement. Conclusions: The alignment of economic benefits that can be realized through CMM advances a strong value proposition for greater adoption of CMM in the United States’ primary care system. Future application of this study’s conceptual framework could include use as an advocacy tool amongst pharmacy associations or as a tool for individuals seeking buy-in for CMM services.Doctor of Pharmac

confined spaces industrial inspection with micro aerial vehicles and laser range finder localization

This work addresses the problem of semi-automatic inspection and navigation in confined environments. A system that overcomes many challenges at the state of the art is presented. It comprises a mu..

Smooth Coverage Path Planning for UAVs with Model Predictive Control Trajectory Tracking

Within the Industry 4.0 ecosystem, Inspection Robotics is one fundamental technology to speed up monitoring processes and obtain good accuracy and performance of the inspections while avoiding possible safety issues for human personnel. This manuscript investigates the robotics inspection of areas and surfaces employing Unmanned Aerial Vehicles (UAVs). The contribution starts by addressing the problem of coverage path planning and proposes a smoothing approach intended to reduce both flight time and memory consumption to store the target navigation path. Evaluation tests are conducted on a quadrotor equipped with a Model Predictive Control (MPC) policy and a Simultaneous Localization and Mapping (SLAM) algorithm to localize the UAV in the environment

Inter-individual cognitive variability in children with Asperger's syndrome

Multiple studies have tried to establish the distinctive profile of individuals with Asperger's syndrome (AS). However, recent reports suggest that adults with AS feature heterogeneous cognitive profiles. The present study explores inter-individual variability in children with AS through group comparison and multiple case series analysis. All participants completed an extended battery including measures of fluid and crystallized intelligence, executive functions, theory of mind, and classical neuropsychological tests. Significant group differences were found in theory of mind and other domains related to global information processing. However, the AS group showed high inter-individual variability (both sub- and supra-normal performance) on most cognitive tasks. Furthermore, high fluid intelligence correlated with less general cognitive impairment, high cognitive flexibility, and speed of motor processing. In light of these findings, we propose that children with AS are characterized by a distinct, uneven pattern of cognitive strengths and weaknesses.Fil: González Gadea, María Luz. Universidad Diego Portales; Chile. Universidad Favaloro; Argentina. Instituto de Neurología Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Tripicchio, Paula. Instituto de Neurología Cognitiva; Argentina. Universidad Favaloro; ArgentinaFil: Rattazzi del Carril, Alexia. Instituto de Neurología Cognitiva; Argentina. Universidad Favaloro; ArgentinaFil: Báez Buitrago, Sandra Jimena. Universidad Favaloro; Argentina. Universidad Diego Portales; Chile. Universidad Catolica Argentina; Argentina. Instituto de Neurología Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Marino, Julián Carlos. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Roca, María. Universidad Favaloro; Argentina. Instituto de Neurología Cognitiva; Argentina. Universidad Diego Portales; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Manes, Facundo Francisco. Instituto de Neurología Cognitiva; Argentina. Universidad Favaloro; Argentina. Universidad Diego Portales; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Centre of Excellence in Cognition and its Disorders; AustriaFil: Ibanez Barassi, Agustin Mariano. Instituto de Neurología Cognitiva; Argentina. Universidad Favaloro; Argentina. Universidad Diego Portales; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Centre of Excellence in Cognition and its Disorders; Austria. Universidad Autonoma del Caribe; Colombi

Welding Defect Detection with Deep Learning Architectures

Welding automation is a fundamental process in manufacturing industries. Production lines integrate welding quality controls to reduce wastes and optimize the production chain. Early detection is fundamental as defects at any stage could determine the rejection of the entire product. In the last years, following the industry 4.0 paradigm, industrial automation lines have seen the introduction of modern technologies. Although the majority of the inspection systems still rely on traditional sensing and data processing, especially in the computer vision domain, some initiatives have been taken toward the employment of machine learning architectures. This chapter introduces deep neural networks in the context of welding defect detection, starting by analyzing common problems in the industrial applications of such technologies and discussing possible solutions in the specific case of quality checks in fuel injectors welding during the production stage