Tesi di Dottorato
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Item Integrazione di tecniche ottiche e acustiche di imaging 3D in ambiente subacqueo(2015-12-16) Lagudi, Antonio; Pagnotta, Leonardo; Rizzuti, Sergio; Bruno, FabioItem <> investigation on the usability of the Kinect by Microsoft in upper limb rehabilitation after stroke(2016-12-15) Lupinacci, Giorgia; Pagnotta, Leonardo; Gatti, GianlucaL’Ictus rappresenta la seconda causa di disabilità in Europa e la sesta nel mondo. Il danneggiamento del tessuto nervoso, a seguito di Ictus, può portare ad una perdita della mobilità dell’arto superiore, limitando così l’indipendenza del soggetto nello svolgimento delle normali attività quotidiane. La Neuro-riabilitazione e le tecnologie avanzate propongono nuove soluzioni, per promuovere la neuro-plasticità attraverso l’apprendimento e il controllo motorio. In questa tesi si e studiata l’applicabilità di un sensore di profondità commerciale per la riabilitazione dell’arto superiore, il Kinect for Windows. Il Kinect e stato adottato per analisi delle performance di soggetti e come strumento per l’interazione con un ambiente virtuale. Sono stati eseguiti test con soggetti sani e pazienti. Innanzitutto, si e effettuata una valutazione dell’accuratezza del Kinect v2.0, nonché del comportamento del sensore se posizionato in diverse posizioni e orientamenti. I risultati ottenuti in questa prima fase di studio hanno trovato conferma nell’accuratezza del classificatore di postura, il cui training è stato effettuato con dati ottenuti da un Kinect v2.0. Infine, si è realizzato un sistema per lo sviluppo di video-games riabilitativi. Il sistema prevede l’utilizzo di un Kinect per l’interazione con un ambiente virtuale personalizzabile e ha il vantaggio di essere flessibile e altamente orientato-al-paziente. Il Kinect rappresenta un buon compromesso tra accuratezza e utilizzabilità, e il percorso seguito ha consentito di mettere in evidenza le potenzialità e i limiti di questo strumento. Per la valutazione degli effetti a lungo termine sul recupero della mobilità, sarà necessario eseguire opportuni test clinici.Item A Domain-Specific approach for Programming Wireless Body Sensor Network Systems(2011-11-23) Gravina, Raffaele; Palopoli, Luigi; Fortino, GiancarloThe progress of science and medicine during the last years has contributed to signi cantly increase the average life expectancy. The increase of elderly population will have a large impact especially on the health care system. Furthermore, especially in more developed countries, there is an always growing interest in maintaining, and improving the quality of life. Wireless Body Sensor Networks (BSNs) can contribute to improve the quality of health care services. BSNs involve wireless wearable physiological sensors applied to the human body for strictly medical and non medical purposes. They can enhance many human-centered application domains such as e-Health, sport and wellness, and even social applications such as physical/ virtual social interactions. However, there are still open issues that limit their wide di usion in real life; primarily, the programming complexity of these systems, due to lack of high-level software abstractions, and to hardware constraints of wearable devices. In contrast to low-level programming and general-purpose middleware, domain-speci c frameworks are an emerging programming paradigm designed to ful ll the lack of suitable BSN programming support. With this aim, this thesis proposes a novel domain-speci c approach for programming signal-processing intensive BSN applications. The de nition of this approach resulted in a domain-speci c programming framework named SPINE (Signal Processing in Node Environment) which is one important contribution of this thesis, along with other interesting contributions derived from enhancements and variants to the main proposal. Additionally, to provide validation and performance evaluation of the proposed approach, a number of BSN applications (including human activity monitoring, physical energy expenditure estimation, emotional stress detection, and step-counting) have been developed atop SPINE. These research prototypes showed the e ectiveness and e ciency of the proposed approach and improved their respective state-of-the-art. Finally, a Platform-Based Design (PBD) methodology, which is widely adopted for the design of traditional embedded systems, is proposed for the design of BSN systems.Item Autonomic computing-based wireless sensor networks(2013-11-27) Galzarano, Stefano; Fortino, Giancarlo; Liotta, Antonio; Greco, SergioWireless Sensor Networks (WSNs) have grown in popularity in the last years by proving to be a bene cial technology for a wide range of application do- mains, including but not limited to health-care, environment and infrastruc- ture monitoring, smart home automation, industrial control, intelligent agri- culture, and emergency management. However, developing applications on such systems requires many e orts due to the lack of proper software abstractions and the di culties in man- aging resource-constrained embedded environments. Moreover, these appli- cations have to meet a combination of con icting requirements. Achieving accuracy, e ciency, correctness, fault-tolerance, adaptability and reliability on WSN is a major issue because these features have to be provided beyond the design/implementation phase, notably at execution time. This thesis explores the viability and convenience of Autonomic Comput- ing in the context of WSNs by providing a novel paradigm to support the development of autonomic WSN applications as well as speci c self-adaptive protocols at networking levels. In particular, this thesis provides three main contributions. The rst is the design and realization of a novel framework for the development of e cient distributed signal processing applications on heterogeneous WSNs, called SPINE2. It provides a programming abstraction based on the task-oriented paradigm for abstracting away low-level details and has a platform-independent architecture enabling code reusability and portability, application interoperability and platform heterogeneity. The sec- ond contribution is the development of SPINE-* which is an enhancement of SPINE2 by means of an autonomic plane, a way for separating out the provision of self-* techniques from the WSN application logic. Such a separa- tion of concerns leads to an ease of deployment and run-time management of new applications. We nd that this enhancement brings not only considerable functional improvements but also measurable performance bene ts. Third, since we advocate that the agent-oriented paradigm is a well-suited approach in the context of autonomic computing, we propose MAPS, an agent-based programming framework for WSNs. Speci cally designed for supporting Java- iii based sensor platforms, MAPS allows the development of general-purpose mobile multi-agent applications by adopting a multi-plane state machine for- malism for de ning agents' behavior. Finally, the fourth contribution regards the design, analysis, and simulations of a self-adaptive AODV routing protocol enhancement, CG-AODV, and a novel contention-based MAC protocol, QL- MAC. CG-AODV adopts a \node concentration-driven gossiping" approach for limiting the ooding of control packets, whereas QL-MAC, based on a Q-learning approach, aims to nd an e cient radio wake-up/sleep scheduling strategy to reduce energy consumption on the basis of the actual network load of the neighborhood. Simulation results show that CG-AODV outper- forms AODV, whereas QL-MAC provides better performance over standard MAC protocols.Item Classification models and algorithms in application of multi-sensor systems to detection and identification of gases(2014-06-04) Khalaf, Walaa; Cocurullo, Giuseppe; Gaudosio, Manlio; Pace, CalogeroThe objective of the thesis is to adopt advanced machine learning tech- niques in the analysis of the output of sensor systems. In particular we have focused on the SVM (Support Vector Machine) approach to classi- ¯cation and regression, and we have tailored such approach for the area of sensor systems of the "electronic nose" type. We designed an Electronic Nose (ENose), containing 8 sensors, 5 of them being gas sensors, and the other 3 being a Temperature, a Humidity, and a Pressure sensor, respectively. Our system (Electronic Nose) has the ability to identify the type of gas, and then to estimate its concentration. To identify the type of gas we used as classi¯cation and regression technique the so called Support Vector Machine (SVM) approach, which is based on statistical learning theory and has been proposed in the broad learning ¯eld. The Kernel methods are applied in the context of SVM, to improve the classi¯cation quality. Classi¯cation means ¯nding the best divider (separator) between two or more di®erent classes without or with minimum number of errors. Many methods for pattern recognition or classi¯cation are based on neural network or other complex mathematical models. In this thesis we describe the hardware equipment which has been designed and implemented. We survey the SVM approach for machine learning and report on our experimentation.