Optical Design for Automatic Identification and Portable Systems

This thesis proposes design methods and test tools, for optical systems, which may be used in an industrial environment, where not only precision and reliability but also ease of use is important. The approach to the problem has been conceived to be as general as possible, although in the present work, the design of a portable device for automatic identification applications has been studied, because this doctorate has been funded by Datalogic Scanning Group s.r.l., a world-class producer of barcode readers. The main functional components of the complete device are: electro-optical imaging, illumination and pattern generator systems. For what concerns the electro-optical imaging system, a characterization tool and an analysis one has been developed to check if the desired performance of the system has been achieved. Moreover, two design tools for optimizing the imaging system have been implemented. The first optimizes just the core of the system, the optical part, improving its performance ignoring all other contributions and generating a good starting point for the optimization of the whole complex system. The second tool optimizes the system taking into account its behavior with a model as near as possible to reality including optics, electronics and detection. For what concerns the illumination and the pattern generator systems, two tools have been implemented. The first allows the design of free-form lenses described by an arbitrary analytical function exited by an incoherent source and is able to provide custom illumination conditions for all kind of applications. The second tool consists of a new method to design Diffractive Optical Elements excited by a coherent source for large pattern angles using the Iterative Fourier Transform Algorithm. Validation of the design tools has been obtained, whenever possible, comparing the performance of the designed systems with those of fabricated prototypes. In other cases simulations have been used.

Studio del metodo dell'assorbimento diretto della CO2 per l'analisi del radiocarbonio

L’attuale rilevanza rappresentata dalla stretta relazione tra cambiamenti climatici e influenza antropogenica ha da tempo posto l’attenzione sull’effetto serra e sul surriscaldamento planetario così come sull’aumento delle concentrazioni atmosferiche dei gas climaticamente attivi, in primo luogo la CO2. Il radiocarbonio è attualmente il tracciante ambientale per eccellenza in grado di fornire mediante un approccio “top-down” un valido strumento di controllo per discriminare e quantificare il diossido di carbonio presente in atmosfera di provenienza fossile o biogenica. Ecco allora che ai settori applicativi tradizionali del 14C, quali le datazioni archeometriche, si affiancano nuovi ambiti legati da un lato al settore energetico per quanto riguarda le problematiche associate alle emissioni di impianti, ai combustibili, allo stoccaggio geologico della CO2, dall’altro al mercato in forte crescita dei cosiddetti prodotti biobased costituiti da materie prime rinnovabili. Nell’ambito del presente lavoro di tesi è stato quindi esplorato il mondo del radiocarbonio sia dal punto di vista strettamente tecnico e metodologico che dal punto di vista applicativo relativamente ai molteplici e diversificati campi d’indagine. E’ stato realizzato e validato un impianto di analisi basato sul metodo radiometrico mediante assorbimento diretto della CO2 ed analisi in scintillazione liquida apportando miglioramenti tecnologici ed accorgimenti procedurali volti a migliorare le performance del metodo in termini di semplicità, sensibilità e riproducibilità. Il metodo, pur rappresentando generalmente un buon compromesso rispetto alle metodologie tradizionalmente usate per l’analisi del 14C, risulta allo stato attuale ancora inadeguato a quei settori applicativi laddove è richiesta una precisione molto puntuale, ma competitivo per l’analisi di campioni moderni ad elevata concentrazione di 14C. La sperimentazione condotta su alcuni liquidi ionici, seppur preliminare e non conclusiva, apre infine nuove linee di ricerca sulla possibilità di utilizzare questa nuova classe di composti come mezzi per la cattura della CO2 e l’analisi del 14C in LSC.

Models and Algorihtm for the Optimization of Real-World Routing and Logistics Problems

Logistics involves planning, managing, and organizing the flows of goods from the point of origin to the point of destination in order to meet some requirements. Logistics and transportation aspects are very important and represent a relevant costs for producing and shipping companies, but also for public administration and private citizens. The optimization of resources and the improvement in the organization of operations is crucial for all branches of logistics, from the operation management to the transportation. As we will have the chance to see in this work, optimization techniques, models, and algorithms represent important methods to solve the always new and more complex problems arising in different segments of logistics. Many operation management and transportation problems are related to the optimization class of problems called Vehicle Routing Problems (VRPs). In this work, we consider several real-world deterministic and stochastic problems that are included in the wide class of the VRPs, and we solve them by means of exact and heuristic methods. We treat three classes of real-world routing and logistics problems. We deal with one of the most important tactical problems that arises in the managing of the bike sharing systems, that is the Bike sharing Rebalancing Problem (BRP). We propose models and algorithms for real-world earthwork optimization problems. We describe the 3DP process and we highlight several optimization issues in 3DP. Among those, we define the problem related to the tool path definition in the 3DP process, the 3D Routing Problem (3DRP), which is a generalization of the arc routing problem. We present an ILP model and several heuristic algorithms to solve the 3DRP.

Perceiving the 3d world from single images

Depth represents a crucial piece of information in many practical applications, such as obstacle avoidance and environment mapping. This information can be provided either by active sensors, such as LiDARs, or by passive devices like cameras. A popular passive device is the binocular rig, which allows triangulating the depth of the scene through two synchronized and aligned cameras. However, many devices that are already available in several infrastructures are monocular passive sensors, such as most of the surveillance cameras. The intrinsic ambiguity of the problem makes monocular depth estimation a challenging task. Nevertheless, the recent progress of deep learning strategies is paving the way towards a new class of algorithms able to handle this complexity. This work addresses many relevant topics related to the monocular depth estimation problem. It presents networks capable of predicting accurate depth values even on embedded devices and without the need of expensive ground-truth labels at training time. Moreover, it introduces strategies to estimate the uncertainty of these models, and it shows that monocular networks can easily generate training labels for different tasks at scale. Finally, it evaluates off-the-shelf monocular depth predictors for the relevant use case of social distance monitoring, and shows how this technology allows to overcome already existing strategies limitations.