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The efficient numerical simulation of the environmnent of underground power cables is a challenging task. The Finite Element Method (FEM) encounters huge problems when it is used for the simulation of the aforementioned physical systems. The objective of the research (in cooperation with Terna s.p.a.) is then to devise alternative formulations which overcome the FEM limitations. Integral methods are well-suited to this task.

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Big Data analytics is one of the major challenges for today’s computing systems, aiming at extracting valuable knowledge and information from an exponentially-increasing amount of data. The distributed and heterogeneous nature of the data sources, as well as the huge computational power that is required, is pushing designers towards novel computing systems that combine HPC, Cloud, and IoT solutions (for efficient and distributed computation closer to the data) with Artificial Intelligence (AI) algorithms (for knowledge extraction and decision making). This thesis will focus on data management, understanding how to create a distributed memory architecture for moving the computation closer to the data in a way that is totally transparent to the user.

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The object of this work is to apply techniques based on Operator Precedence (OP) languages for: code verification through model-checking; analysis of massive structured data; runtime verification of critical applications.

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Keywords: Green IT, Fog Computing, Machine Learning, Service-Oriented Architectures. Fog computing aims at creating a continuity between the cloud, with ideally unlimited resources and higher performance and storage capabilities, and the edge, characterized by limited capabilities but lower latency. In this context, new opportunities for managing application deployment are raising together with new challenges. Modern applications are supposed to run in a heterogeneous environment where tasks can be executed dynamically in the cloud and/or in the edge according to the context in which the application is running. These two environments are strongly heterogeneous in terms of resources capabilities, performance, energy efficiency, and power sources. Moreover, the quality of the connection between the different nodes can change dynamically, introducing performance issues. This thesis aims at proposing a dynamic workload allocation algorithm in a performance and energy aware mixed cloud-edge environment taking into account performance and energy efficiency of the available resources and providing an automatic mechanism to decide where to execute a given workload according to the current resource and network state.

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Keywords: Application Monitoring, Fog Computing, Machine Learning, Service-Oriented Architectures. Fog Computing is a paradigm that exploits computational resources available locally (edge resources) and remotely (cloud resources) according to the features of the application, the availability of computational resources in the different nodes, and the data the application needs to consume during its execution. Fog computing is very effective for managing smart buildings where a huge amount of information is generated every minute and moving data from the edge where it is produced to the cloud where it is needed is very expensive and might impact the quality of service. This thesis aims at exploiting the concept of fog computing for building an adaptive monitoring system for smart buildings where adaptive decisions about which data to monitor and where to deploy the applications involved in consuming the data are taken with the aim of reducing the volume of data that has to be moved from the edge to the cloud.

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Discrete event systems (DES) are characterized by a discrete set of states, and state changes are driven by the occurrence of (asynchronous) discrete events. This is a very general class of models, which is often encountered in automated manufacturing, communication and transportation networks, control applications, etc. In timed DES the transitions between activities are further endowed with time bounds in order to represent the duration of activities. A timed control task involves reaching specific states (while avoiding others) within prescribed time limits. The design of a controller for timed DES is a complex and challenging task that requires the development of suitable tools for reachability analysis, path optimization, and control policy design. This thesis aims at the development of a Matlab library of such tools for time Petri nets and its testing on some timed DES control benchmarks.

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Hybrid systems are heterogeneous dynamical systems whose behavior can be described by the interaction of several time-driven continuous dynamics (continuous states or modes) indexed by event-driven discrete dynamic (discrete states). They can represent technological systems where continuous models such as differential or difference equations describe the physical and mechanical part, and discrete models such as finite-state machines or Petri nets describe the software and logical behavior. For example, while driving, a car follows different dynamics, depending on the specific phase (acceleration/braking, cornering) or the track characteristics (slope, road surface, weather conditions). The goal of this thesis is the development of a black-box technique to identify both the system modes and the switching mechanism directly from data.

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During the paper manufacturing process, some paper break events can occur. If a break happens, the entire process is stopped, any found problem is fixed and the production is resumed. Resuming the process can take more than an hour, which represents a severe cost for the factory. Even a 5% reduction in the break events will yield a significant cost saving. The objective of this thesis is the development of suitable techniques to predict such breaks in advance (early prediction) and identify the potential cause(s) to prevent the break. To build such a prediction model, the applicant will use data collected from a network of sensors deployed to monitor a real manufacturing plant.

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Drilling is a complex process that consists in boring a hole (that can be several thousands of meters long) in order to create a well for oil (or gas) production. A sticking occurs when the drillstring cannot be neither rotated nor moved along the axis of the wellbore. A pipe is considered stuck if it cannot be freed from the hole without damaging the pipe, and without exceeding the drilling rig’s maximum allowed hook load. This phenomenon can be due to pressure-related issues (if the pressure in the annulus exceeds that in the formation being drilled, the drillstring is pulled against the wall of the borehole and held against it) or to mechanical blocking (e.g., due to incomplete removal of the cuttings, or partial collapse of the borehole). Stuck-pipe phenomena can have disastrous effects on drilling performance, with outcomes that may range from time delays (note that a day of drilling can cost millions of euros) to loss of expensive machinery. This thesis aims at the development (using machine learning techniques) of a data-based prediction model that can be used to anticipate the occurrence of the mentioned events. A large dataset of oil&gas wells can be used to train and validate the model. This work is part of an ongoing collaboration with Eni's Drilling & Completion Risk Analysis Department.

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L'elemento base di una matrice di memoria 3D NAND Flash è oggi un transistore MOSFET ultra-scalato con canale in silicio policristallino (polisilicio). La natura granulare di questo materiale e, in particolare, la presenza di una elevata densità di stati trappola ai suoi bordi di grano rende il trasporto di corrente in questi dispositivi molto differente da quello dei dispositivi in silicio monocristallino, ad esempio in termini di mobilità efficace dei portatori di carica e sua dipendenza dalla temperatura. Il lavoro di tesi sarà focalizzato sullo studio mediante simulazione numerica del trasporto di corrente nei dispositivi MOSFET nanometrici con canale in polisilicio, al fine di comprenderne la fisica di base e le limitazioni al funzionamento e all'affidabilità delle odierne tecnologie di memoria 3D NAND Flash. Il lavoro sarà svolto presso il laboratorio di dispositivi elettronici del DEIB.

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La continua ricerca di più elevate densità di memorizzazione ha portato la tecnologia NAND Flash ad evolvere verso una integrazione tridimensionale (3D) delle celle di memoria. Questo non solo ha comportato notevoli cambiamenti nella geometria, nella struttura e nei materiali delle celle di memoria, ma ha anche reso rilevanti nuovi fenomeni fisici durante il funzionamento delle celle stesse. In questo quadro, il lavoro di tesi sarà focalizzato sullo studio sperimentale dei fenomeni fisici limitanti il funzionamento e l'affidabilità di matrici di memoria 3D NAND Flash. Il lavoro sarà svolto nell'ambito di una offerta di stage dell'azienda Micron Semiconductor Italia, i cui laboratori di ricerca sono siti in Vimercate (MB). L'inizio della tesi è previsto per Settembre 2020.

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