Multi-Platform Design of Smartphone Applications

Smartphone application developers should support all the main leading platforms which share the market thus increasing time-to-market and development cost. To solve this problem, the work proposes a design flow based on Model-Driven Design to develop a single version of the smartphone application which can be automatically translated into the main platform-dependent versions. We introduce a UML2 profile to represent the elements of the smartphone application independently of the target platform; the behavior of the application is modeled as a finite-state machine while the graphical user interface is modeled by using classes and objects. A set of translation rules are defined to obtain a platform-depended representation and then the actual code. The methodology has been validated by generating an application for the Android and Windows Phone platforms and by comparing it to the versions written in the traditional way

First Step Towards Embedded Vision System for Pruning Wood Estimation

This paper focuses on the development and evaluation of a portable vision-based acquisition device for vineyards, equipped with a GPU-accelerated processing unit. The device is designed to perform in-field image acquisitions with high-resolution and dense information. It includes three vision systems: the Intel® RealSenseTM depth camera D435i, the Intel® RealSenseTM tracking camera T265, and a Basler RGB DART camera. The device is powered by an Nvidia Jetson Nano processing board for both simultaneous data acquisition and real-time processing. The paper presents two specific tasks for which the acquisition device can be useful: wood volume estimation and early bud counting. Acquisition campaigns were conducted in a commercial vineyard in Italy, capturing images of vine shoots and buds using the prototype device. The wood volume estimation software is based on image processing techniques, achieving an RMSE of 2.1 cm3 and a mean deviation of 1.8 cm3. The buds detection task is obtained by fine-tuning the YOLOv8 model on a purposely acquired custom dataset, achieving a promising F1-Score of 0.79

Computer vision-based mapping of grapevine vigor variability for enhanced fertilization strategies through intelligent pruning estimation

The objective of this study is to develop an affordable and non-invasive method using computer vision to estimate pruning weight in commercial vineyards. The study aims to enable controlled fertilization by leveraging pruning data as an indicator of plant vigor [1]. The methodology entails the analysis of RGB and DEPTH images acquired through an embedded platform (Figure 1) in a vineyard cultivating corvina grapes using the guyot method [2]. Initially, pruning weight was evaluated by processing pictures taken manually with a controlled background. Then, we developed an algorithm to estimate pruned wood weight based on these images. Subsequently, a mobile sensor platform was utilized to automatically capture grapevine images without a controlled background. Collected data will then be used to deploy a convolutional neural network (CNN) for intelligent pruning estimation capable of extracting meaningful data from real-world environments. Additionally, we integrated and validated a visual-odometry sensor (Intel Realsense T265) to map the spatial variability of pruning estimation results

STEWIE: eSTimating grapE berries number and radius from images using a Weakly supervIsed nEural network

Counting tasks with overlapping and occluded tar-gets are often tackled by means of neural networks outputting density maps. While this approach has been proven to be highly effective for crowd-counting tasks, it has not been exploited extensively in other fields (like fruit counting). Furthermore, this approach has never been used to infer the shape or the size of the recognized objects. In this paper, we present a novel deep learning-based methodology to automatically estimate the number of grape berries present in an image and evaluate their average radius as a double output of the network. For the model training, we employ a public dataset consisting of 300 vines images, where each berry center has been dot-annotated. Since the dataset does not directly provide information about the berry radii, we first develop a numerical optimization methodology to calculate the radius of the berries, by exploiting the dot annotations, some prior knowledge (berry maximum size), and a current state-of-the-art segmentation model. Then, we employ the combined information (berry center and radius) to train a custom neural network that outputs two density maps, from which we infer the number of berries in the image and their average size

Integration of New Features for Telerobotic Surgery into The Mirosurge System

International audienc

SMART MEASUREMENT SYSTEMS FOR VINEYARD MONITORING

In questo lavoro di tesi vengono presentati diversi metodi che possono essere utilizzati per prevedere e analizzare alcuni aspetti della produzione vitivinicola fondamentali per garantire una gestione ottimale dei vigneti. Nel primo capitolo, è stata condotta un'analisi approfondita dei metodi esistenti per prevedere la resa di un vigneto, lo stato vegetativo delle viti e la maturazione dell'uva. La previsione della resa è essenziale perché è direttamente correlata alla sostenibilità economica del vigneto, fornendo ai viticoltori gli strumenti per ottimizzare l'allocazione delle risorse, pianificare la logistica e definire strategie di mercato informate. La valutazione dello stato vegetativo è vitale per monitorare la salute e la vitalità delle viti, permettendo interventi tempestivi per garantire un equilibrio tra crescita vegetativa e produzione di frutta, assicurando così una qualità dell'uva ottimale. Infine, lo stato di maturazione gioca un ruolo chiave nel determinare il momento ideale di vendemmia, influenzando notevolmente la qualità e il profilo sensoriale del vino prodotto. Dopo un'introduzione sullo stato dell'arte della ricerca viene presentato il contributo dell'autore in ciascuna delle tre aree chiave, attraverso articoli di ricerca. Per quanto riguarda la resa del vigneto, è stato sviluppato un algoritmo in grado di conteggiare gli acini presenti in un grappolo e di stimare il ragggio medio degli acini (dando quindi anche una stima sul volume del grappolo). L'algoritmo è stato inoltre validato metrologicamente al fine di fornire l'incertezza di stima e di identificare quali sono e quanto incidono le diverse componenti di incertezza. Il metodo proposto ha ottenuto risultati superiori allo stato dell'arte nell'ambito del conteggio acini (4 % di errore su immagini contenenti una media di 600 acini) e risultati promettenti per quanto riguarda la stima del raggio medio. Nel campo del riconoscimento dello stato vegetativo è stato implementato un sistema di acquisizione associato ad algoritmi di visione, per il riconoscimento delle gemme (F1-Score = 0.79),la valutazione del volume ligneo delle viti (mean absolute average error = 9.7 %) e la stima dello stato idrico. Infine attraverso l'uso di sensori NIR e tecniche di analisi dati, è stata proposta una metodologia per valutare la maturazione dell'uva, concentrandosi sulla composizione percentuale di zucchero e di acidi. Il metodo proposto ottiene risultati prometteneti soprattutto per quanto riguarda la previsione della quantità di zuccheri solubili (TSS) con un R^2 pari a 0.97. I modelli sviluppati però sono specifici delle cultivar prese in considerazione e non sono generalizzabili senza ulteriori campagne di calibrazione. Nel secondo capitolo, è descritta la progettazione e l'implementazione di un sistema di acquisizione per variabili ambientali e agrometereologiche. Il sistema è sviluppato con lo scopo di massimizzarne la flessibilità, permettendo ai viticoltori di adattare il sistema alle specifiche esigenze e condizioni dei loro vigneti. Il sistema di acquisizione è stato testato posizionando cinque centraline in un vigneto in Franciacorta (Lombardia,Italia) composto da cinque campi diversi al fine di monitorare le variabili ambientali. Il setup sperimentale, per quanto prototipale, ha evidenziato come i cinque campi, nonostante la vicinanza, presentino alcune differenze per quanto riguarda il potenziale idrico del suolo e la bagnatura fogliare. Tali differenze sono significative perchè potrebbero implicare diverse gestioni agronomiche. La seconda parte del capitolo si concentra sulla caratterizzazione e ottimizzazione di un design per uno schermo solare, che verrà utilizzato nella stazione meteorologica per proteggere i sensori di temperatura dalla radiazione solare. La misurazione della temperatura ha un'importanza particolare poiché influisce profondamente sulla crescita, il metabolismo e le fasi fenologiche delle viti.In this thesis work, several methods are presented that can be used to predict and analyze certain aspects of wine production that are fundamental to ensuring optimal vineyard management. In the first chapter, an in-depth analysis of existing methods for predicting a vineyard's yield, vine vegetative state and grape ripening was conducted. Yield prediction is essential because it is directly related to the economic sustainability of the vineyard, providing winegrowers with the tools to optimize resource allocation, plan logistics, and define informed market strategies. Vegetative status assessment is vital for monitoring the health and vitality of vines, enabling timely interventions to ensure a balance between vegetative growth and fruit production, thus ensuring optimal grape quality. Finally, the state of ripeness plays a key role in determining the ideal harvest time, greatly influencing the quality and sensory profile of the wine produced. After an introduction on the state of the art of research, the author's contribution in each of the three key areas is presented through research articles. With regard to vineyard yield, an algorithm was developed that can count the berries present in a cluster and estimate the average cluster size (thus also giving an estimate on cluster volume). The algorithm was also metrologically validated in order to provide the estimation uncertainty and to identify what the different uncertainty components are and how much they affect it. The proposed method obtained results above the state of the art in the area of berry counting (4 % error on images containing an average of 600 berries) and promising results in estimating the average radius. In the field of vegetative state recognition, an acquisition system associated with vision algorithms has been implemented for bud recognition (F1-Score = 0.79),evaluation of vine woody volume (mean absolute average error = 9.7 %) and estimation of water status. Finally through the use of NIR sensors and data analysis techniques, a methodology was proposed to assess grape ripeness, focusing on the percentage composition of sugar and acids. The proposed method obtains promising results especially in predicting the amount of soluble sugars (TSS) with an R^2 of 0.97. However, the developed models are specific to the cultivars under consideration and are not generalizable without further calibration campaigns. In the second chapter, the design and implementation of an acquisition system for environmental and agrometeorological variables is described. The system is developed with the aim of maximizing its flexibility, allowing winemakers to adapt the system to the specific needs and conditions of their vineyards. The acquisition system was tested by placing five control units in a vineyard in Franciacorta (Lombardy,Italy) composed of five different fields in order to monitor environmental variables. The experimental setup, although prototypical, showed that the five fields, despite their proximity, have some differences in soil water potential and foliar wetting. These differences are significant because they could imply different agronomic management. The second part of the chapter focuses on the characterization and optimization of a design for a sun screen, which will be used in the weather station to protect the temperature sensors from solar radiation. The measurement of temperature is of special importance because it profoundly affects the growth, metabolism and phenological stages of vines

MiLE: systematic usability evaluation for e-learning web applications

This paper presents a proven and reusable methodology (MiLE) for performing a cost-effective usability evaluation of an e-learning web application. MiLE is a scenario-driven inspection technique which is based on the concepts of user profile, user goal, scenario, and usability attribute. Mitigating the drawbacks and merging the respective benefits of state-of-the-art methods for usability evaluation, MiLE is intended to be a helpful tool for project managers, instructional designers, and evaluators to carry out a learner-centered validation which can anticipate and analytically justify the usability breakdowns, thus providing organized indications for a focused redesign. Examples of the results that can be obtained using MiLE are showed through a real case study evaluation of a large e-learning corporate platform

GPU Based Physical Cut in Interactive Haptic Simulations

PURPOSE: Interactive, physics based, simulations of deformable bodies are a growing research area with possible applications to computer-aided surgery. Their aim is to create virtual environments where surgeons are free to practice. To ensure the needed realism, the simulations must be performed with deformable bodies. The goal of this paper is to describe the approach to the development of a physics-based surgical simulator with haptic feedback. METHOD:The main development issue is the representation of the organ behavior at the high rates required by haptic realism. Since even high-end computers have inadequate performance, our approach exploits the parallelism of modern Graphics Processing Units (GPU). Particular attention is paid to the simulation of cuts because of their great importance in the surgical practice and the difficulty in handling topological changes in real time. RESULTS: To prove the correctness of our approach, we simulated an interactive, physically based, virtual abdomen. The simulation allows the user to interact with deformable models. Deformable models are updated in real time, thus allowing the rendering of force feedback to the user. The method is optimized to handle high quality scenes: we report results of interactive simulation of two virtual tools interacting with a complex model. CONCLUSIONS: The integration of physics-based deformable models in simulations greatly increases the realism of the virtual environment, taking into account real tissue properties and allowing the user to feel the actual forces exerted by organs on virtual tools. Our method proves the feasibility of exploiting GPU to simulate deformable models in interactive virtual environments

Simulation of networked control systems with applications to telerobotics

Real-time telerobotic systems connected through packet networks belong to the broader family of Networked Control Systems, and can be easily destabilized by communication delay and packet losses, when they are not properly compensated. The solutions available in the literature are mainly based on Control Theory. This classical approach could be improved by the joint design of the network, e.g., the introduction of quality-of-service guarantees as currently done in teleconference applications. Control/network co-design needs a simulation framework where both aspects are properly and jointly addressed. The paper faces this topic starting from the discussion of its critical issues, and proposes a co-simulation tool based on SystemC for the network simulation and Matlab/Simulink