Dipartimento di Fisica - Tesi di Dottorato

Permanent URI for this collectionhttps://lisa.unical.it/handle/10955/35

Questa collezione raccoglie le Tesi di Dottorato afferenti al Dipartimento di Fisica dell'Università della Calabria.

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Author: search.filters.author.Pantano, PietroHas files: Yes

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    Identification of Brain Structures and Functional Cortico - Muscular Networks: Machine Learning Object Recognition and Network Physiology Approach
    (Università della Calabria, 2020-03-05) Rizzo, Rossella; Critelli, Salvatore; Pantano, Pietro; Ivanov, Plamen
    The brain is the most complex part in the human body. This organ is responsible for our intelligence, interpreting sensation, initiating body movement, and controlling all of our behaviors. Over hundreds of years, scientists have learned much about the brain, from a microscopic and macroscopic point of view. We now know the general rules under which information is transferred from neuron to neuron and we can differentiate between various brain structures and brain areas, each of them responsible of a particular function in the human organism. However, due to the vast complexity of the brain, much remains to be discovered. Researchers continue to explore the mechanics regulating a healthy brain that functions quickly and automatically, but we are still at the point where much work remains to identify the key differences between a physiological and a pathological situation in anatomic brain structures and functionality of the brain. The lack of information in this sense affects the diagnostic process of many neurodegenerative disorders, that can be discovered only from the symptoms shown by the subject and that, therefore, can be treated to reduce the pain and to give better conditions of life. The present research aims to better understand anatomic brain structures and functional interactions networks in the brain in order to early diagnose the most common neurodegenerative diseases. In the framework of the investigation of the anatomic brain structures the Neuroimaging is the most powerful tool used in basic research and clinical field. The Magnetic Resonance Imaging (MRI) is one of the most recent techniques of brain imaging and largely used for its low degree of invasion in the human body. It can provide valuable information in the detection of morphological markers that can highlight on the healthy status of the subject. A fundamental step in the pre-processing and analysis of magnetic resonance images is the individuation of the Mid-Sagittal Plane (MSP), where the mid brain is located, in order to set a coordinate reference system for the MRI scan images, and to precisely measure small changes in the surfaces, volumes and distances between different brain areas, which are used as biomarkers in the diagnostic process of certain diseases, such as Parkinson, Alzheimer, Progressive Supra-Nuclear Palsy. In this regard, part of the present research involves the improvement of brain MRIs analysis, with the use of machine learning techniques applied for the automatic identification of the MSP. In particular, the proposed method, Image Pixel Intensity (IPI) algorithm, is implemented in MatLab and is based on the k-mean, which allow to automatically segment the 2D MRIs in different brain tissues, and automatically identifies the slice where the brain tissues are most distinct from each other exploiting the intensity of the resonance signal expressed in the MRI by the color of the grayscale pixels. The results of this algorithm have been compared with the evaluation of four medical experts who manually identified the Mid-Sagittal, providing an average percentage error of 1.84%, and demonstrating that the proposed algorithm is promising and could be directly incorporated into larger diagnostic support systems. Following the main aim of the present research, the early diagnosis of neurodegenerative diseases, another machine learning technique, elastic net, has been implemented in Matlab in order to automatically predict the brain age, exploiting relationships involving the amount of gray matter present in the brain of the subjects analyzed, through a structural MRI study. The outcome of this work is the identification of profound correlations between the expected brain age and the general cognitive state: semantic verbal fluidity, processing speed, visual attention and cognitive flexibility, and visual attention and cognitive flexibility. Among the neurodegenerative diseases Parkinson lately acquired particular interest, due to its growing diffusion even within forty years old patients. This led to the study of functional interactions networks between the brain and the locomotor system during different sleep stages. Electroencephalography (EEG) and electromyography (EMG) data of healthy subjects and Parkinson's patients have been analyzed highlighting the correlations between different frequency bands present in the electrical signals emitted in the different brain areas and in the muscles of the chin and leg. Synchronous bursts in electrical activity signals in the brain and muscles have been analyzed, using the innovative method of Time Delay Stability (TDS), based on the cross-correlation function in consecutive time windows between two different signals. Links between the different frequency bands of different brain areas and the muscles with a long stable delay of the peak in the cross-correlation function are considered more stable, then stronger. The same analysis has been conducted on healthy and Parkinson's subjects, showing substantial differences in the networks of cortico-muscular interactions involving different frequencies between a physiological situation and a pathological one. Each sleep stage is uniquely identified by a particular pattern in the brain-muscle interactions. For Parkinson’s subjects these functional patterns change during each sleep stage; moreover, in general the strength of the links decreases during wake and light sleep but increases or remains the same during REM and deep sleep, especially for the brain-leg interactions, showing that during the waking phase the brain is not able to adequately control the muscles of the lower limbs. Analyzing in details the behavior of muscles the electric activity of different muscle fibers has been studied, considering subjects of different age groups (children, young adults and elderly subjects) in situations of stress or rest. In particular, EMG signals from the muscles of the leg and the back have been taken into account. The analysis shows that rest and stress have very different patterns, due to the different types of muscle fibers involved and how they behave during muscle relaxation and contraction; these relationships also change with age, identifying patterns that uniquely identify the age of the subjects analyzed and also vary during the same exercise by marking the precise point at which the subject reaches fatigue first and exhaustion afterwards.
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    Applicazione dei big data nel turismo, marketing ed education
    (Università della Calabria, 2020-03-18) Giglio, Simona; Critelli, Salvatore; Pantano, Pietro
    Il mondo è attualmente inondato da dati e l’avanzare delle tecnologie digitali amplifica questo fenomeno in modo esponenziale. Tale fenomeno viene etichettato con il concetto di Big Data ovvero le tracce digitali che le nostre attività quotidiane lasciano per effetto dell’uso massiccio dei sistemi ICT (Information Communication Technologies). I Big Data sono diventati il nuovo microscopio che rende “misurabile” la società. Per tali ragioni, la ricerca è incentrata sull’analisi dei Big data, estratti dai social media, da indagini online, da piattaforme di recensioni e da database, attraverso l’applicazione di tecniche e strumenti sviluppati nell’ambito dell’Intelligenza Artificiale. Algoritmi di machine learning, analisi semantica ed analisi statistica sono stati utilizzati per estrarre, dai Big Data, informazione sotto forma di “conoscenza” e “valore”, dimostrando come dati di grandi dimensioni possano fungere da ricca fonte di informazione, da un lato, per comprendere il comportamento dell’utente, parte integrante di una società complessa (conoscenza), e dall’altro, per sostenere i processi decisionali e i servizi forniti agli utenti/consumatori (valore). Il lavoro si caratterizza per un approccio multidisciplinare tra settori differenti quali le scienze sociali, le scienze statistiche e l’informatica. Questo ha permesso di fondare la ricerca sui Big Data nella teoria, e fornire un efficace recupero e analisi dei dati nella pratica. Le tecniche di machine learning sono state applicate per (i) il riconoscimento delle immagini, (ii) per la creazione di cluster, (iii) per l’analisi del testo (sentiment analysis) e (iv) per la profilazione di classi di utenti. Per il riconoscimento delle immagini l’approccio ha richiamato le reti neurali artificiali (deep artificial neural networks), algoritmi e sistemi computazionali ispirati al cervello umano utilizzando le potenzialità del programma Wolfram Mathematica e la disponibilità di dati estratti da social network quali Flickr, Twitter, Instagram ed altre piattaforme come TripAdvisor. Gli strumenti utilizzati nella ricerca hanno permesso di indagare e di rilevare in modo oggettivo dall’analisi di immagini e di testi condivisi sul web, alcuni comportamenti cognitivi degli utenti/consumatori alla base delle loro scelte nonché l’attrattività di una destinazione turistica e la qualità dell’esperienza dell’utente. Lo studio del significato delle parole nel testo ha aperto la strada al web semantico che permette ad un utente di acquisire informazioni approfondite durante una ricerca attraverso un sistema formato da una rete di relazioni e connessioni tra documenti. Partendo dalle ricerche di Ogden e Richards sullo studio del significato e di Jakobson che studiò i processi comunicativi, si è cercato di strutturare e sistematizzare un processo che riflette un atto comunicativo ed informativo tale che un simbolo (immagine) attraverso l’applicazione di un significante (machine learning che si sostituisce al processo mentale proprio dell’uomo) permettesse l’esplicitazione di un referente (oggetto\etichetta) che opportunatamente porta alla trasmissione di un messaggio sotto forma di conoscenza. Il tutto coordinato da un sistema in grado di coniugare fattori differenti in un’ottica interdisciplinare dove l’analisi dei dati combacia perfettamente con la linguistica. Attingendo da studi precedenti, i risultati raggiunti dimostrano che gli algoritmi di analisi dei Big Data quali l’apprendimento automatico contribuiscono da un lato alla comprensione sull’esperienza dell’utente verso un luogo, una destinazione; d’altra parte, la loro analisi fornisce una conoscenza sistematica delle valutazioni dei consumatori su un determinato prodotto o servizio e verso lo sviluppo di una sorta di “intelligenza sociale”. Inoltre i risultati della ricerca propongono come, un approccio più sofisticato al monitoraggio dei social media nel contesto turistico e nel marketing, nonché nel settore dell’education, possa contribuire a migliorare le decisioni strategiche e le politiche operative degli stakeholder nonché ad avere una visione psicologica sugli atteggiamenti e sul comportamento di un ampio spettro di utenti.
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    Nano materials and innovative laser-based accelerators for cultural heritage
    (2017-07-12) Veltri, Simona; Pantano, Pietro; Bonanno, Assunta; Antici, Patrizio
    Università della Calabria, Dipartimento di Fisica
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    La comunicazione scientifica nell'arte tra realtà e realtà aumentata
    (2014-06-05) Rinaudo, Daniela; Pantano, Pietro
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    Experimental and theoretical study of polyhedral carbon Nano-Onions
    (2018-02-23) Basantes Valverde, Marlon Danilo; Pantano, Pietro; Caputi, Lorenzo; De Luca, Giorgio
    Carbon nano-onions (CNOs), in their spherical or polyhedral forms, represent an important class of nanomaterials due to their peculiar physical and electrochemical properties. Among the different methods of production, arc discharge between graphite electrodes sustained by deionized water is one of the most promising to obtain good quality CNOs. The arc discharge method is applied to optimize the production of CNOs, and the synthesized nanomaterials by TEM was studied. An innovative experimental arrangement is used to obtain CNOs dispersed in water together with other carbon nanomaterials, and a black hard cathodic deposit. A simple mechanical grinding of the deposit it allowed to obtain turbostratic polyhedral CNOs with different aspect ratios, which exhibited higher stability towards burning in air, compared to CNOs found in water. A mechanism for the formation of the CNOs contained in the deposit, different from the generally accepted mechanism responsible for the synthesis of CNOs dispersed in water, is hypothesized. These spherical or polyhedral multi-shell fullerenes are widely studied owing to their interesting electronic and mechanical proprieties; nevertheless, comparative studies on these nanoparticles remain scarce. Herein, some key electronic proprieties of single and double walled icosahedral fullerenes as function of their sizes were calculated in the frame of the Density Functional Theory. In particular, structures of icosahedral polyhedral fullerenes, previously validated, were used to get the gap between the Highest Occupied Molecular Orbital and the Lowest Unoccupied Molecular Orbital levels (H-L gap), electron affinity, first ionization potential, electronegativity as well as the Density of the electronic States. This work shows that the H-L gap of the single-wall fullerenes decreases as the nanoparticles size increases, whereas an opposite trend was obtained for the double walled fullerenes. Going from single to double wall nanoparticles; a systematic and marked decrease of the H-L gap was found although, this difference reduces increasing the size of the double walled up obtaining an inversion. The DOS structures of SW nanoparticles changes radically adding a second shell, and the extent of these changes depends on the sizes of the analyzed fullerenes
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    Study of the electronic and structural properties of tin dioxide and armchair graphene nanoribbons
    (2016-02-02) Villamagua Conza, Luis Miguel; Pantano, Pietro; Carini, Manuela; Stashans, Arvids
    This dissertation is focused on the study of the electrical and structural properties of two emerging materials, the tin dioxide (SnO2) and graphene, which have attracted the interest of the semiconductor-device community due to their extraordinary characteristics. The SnO2 has been studied by means of ab initio simulations (Vienna ab initio Simulation Package, VASP). Both n-type and p-type conductivities were investigated. The intrinsic n-type conductivity has been achieved through two schemes: the first one through the combination of oxygen deficiencies and interstitial atoms inside the SnO2 lattice, whereas in the second one, through the combination of interstitial and/or substitutional hydrogen atoms inside the SnO2 lattice. On the other hand, the p-type conductivity was achieved by codoping n-type SnO2 (from earlier configurations) with low concentrations of nitrogen and aluminum impurities. The performed theoretical studies, to a good extent, agree with the experimental results provided by our collaboration group at the National Central University, Jhong-Li (Taiwan). In prospective, these results confirmed that SnO2 is a promising candidate to replace indium in transparent conductive oxides (TCOs) used in photovoltaic, thin-film transistor, and transparent electronic applications. The theoretical study of graphene has been conducted by means of a tight-binding approach (Atomistic ToolKit simulation package, ATK): the electrical and structural properties of edge-defected armchair graphene nanoribbons (AGNRs) were studied. It was found that Stone-Wales defect (very common in carbon allotropes) placed at the edges of the AGNRs can spark an extra opening of the energy gap in graphene, in addition to that obtained through the quantum confinement of electrons. Moreover, an experimental study on the electrical properties of graphene was carried out at the Tyndall National Institute (Ireland) to understand the influence of multiple cleaning treatments on graphene field-effect transistor (GFET) devices. Debris from residual polymers that appeared during device fabrication was swept off the graphene surface without significantly degrading the electronic properties of the graphene flake. The results suggest that the unusual but extraordinary properties of these graphene allotropes can be considered as a very innovative booster for semiconductor devices, allowing the improvement of the scaling trend beyond that obtained with conventional materials.
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    Urban magnetism:understanding cities through the lens of geo-tagged photography
    (2016-02-02) Paldino, Silvia; Pantano, Pietro; Sobolevsky, Stanislav
    This research work presents a method for a new mapping process, based on social networks (especially geo-referred pictures downloaded from the websites of photosharing) to work alongside the maps commonly used in spatial planning. In the vast world of social, I choose to consider in particular the photographs, because people choose to photograph specific places or times that they consider important for some reasons. This would allow a truly innovative reading of the territory, making the concept of smart city in its dual aspects: the technological (because they take into account the social networks by creating dynamic maps of the territories) and human (because it takes into account the actual participation and objective citizens and more generally of land users, without the burden of being directly involved, but only through the daily activities that each of us carries on social networks). In this way it is possible to monitor urban areas that should be protected, managed, potentiated, discovered, making liveble and lived all the city and the territory to have smarter and safer city.
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    New methodologies and instrumentations for power semiconductor devices testing
    (2016-02-02) Hernandez Ambato, Jorge Luis; Pantano, Pietro; Pace, Calogero; Fragomeni, Letizia
    Nowadays electronic applications involve a high density of power Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) which represent the major percentage of energy flow to be controlled. Moreover, new technologies, such as Silicon Carbide (SiC), have been well involved in the existing power applications. Therefore, the reliability of power devices is highly demanded. Since decades, a widely used accelerated test to evaluate the reliability of MOSFETs is the so-called High Temperature Reverse Bias (HTRB). In this stress test, the Devices Under Test (DUTs) are reverse polarized at a certain percentage of the rated breakdown voltage and maintained in this condition at high temperature for a determined long time. A typical HTRB test also incorporates Electrical Characterization Tests (ECTs) of DUTs before and after each stress period, seeking for failed devices. However, time elapsed between ECTs are long and degradation and failure information of DUTs might not be registered. In this context, an advanced methodology for HTRB test is proposed. The latter consists of applying more stress cycles of short duration together with more frequent ECTs at a relatively high temperature that can be directly compared to that of normal operation in power applications (i. e. 125 °C). With this methodology, more detailed information about degradation trends in electrical parameters, time of failures and stopping of stress test on degrading devices before full breakdown can be performed. The latter can be very useful in R&D stages, where the post-failure analysis of well-degraded devices, but not broken, is important. An automatized instrumentation, aimed to apply this methodology, has been implemented. The latter utilizes individual Thermal Control Modules (TCMs) to control the test temperature per single DUT. The temperature control is performed through an opposite mini-heater and firmware running on an 8-bit microcontroller. TCMs can be set remotely to apply test temperatures in the range [30-200 °C]. In addition, Switch Matrix Modules (SSMs) are implemented to configure the electrical connections required for HTRB or ECT tests remotely. A PC application controls all the modules through a Master Communication Module (MCM) also implemented. A commercial Source and Measure Unit (SMU) is used for the electrical stress. Full customization of HTRB and ECTs test parameters can be performed to optimize the stress and degradation data acquisition. Combining the advanced methodology and instrumentation above mentioned, more stressful conditions can be applied to shorten the overall test time without losing the electrical degradation trends of failing devices. In fact, features of the implemented instrumentation allow for controlling unbeneficial thermal runaway process on the single device, isolating thermal and electric of degraded devices, acquiring frequently electrical parameters data, performing ECTs at a relatively high temperature between shorter stress cycles, managing real-time control of HTRB test. These features are useful to get reliability data in a shorter time than a typical application of HTRB tests while preserving DUTs for post-failure analysis. The advanced methodology and automatized instrumentation have been applied to Si and SiC power MOSFETs with interesting results, demonstrating to be suitable for both shorter and more accelerated HTRB tests to acquire critical information necessary for the study of degradation processes and reliability in power devices. Moreover, results have demonstrated that degradation trends are not affected when more frequent ECTs at slightly different temperature are performed in the DUTs. In addition, accurate test results have shown that drawbacks of typical HTRB implementation have been solved through the advanced methodology and instrumentation reported. Complementing the work presented, Low-Frequency Noise Measurements (LFNMs) were also applied as valuable tool to investigate the degradation process in power MOSFETs after stressing them through HTRB test. A correlation between the results from advanced HTRB test and LFNM in power MOSFETs demonstrates that the electrical degradation is represented by a noise spectrum different to that for intrinsic 1/f noise.
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    Development of virtuual advanced learning environments
    (2016-02-02) Olmedo Vizueta, Diana Elizabeth; Pantano, Pietro; Bilotta, Eleonora
    The fact that the humanity is living in the digital era is undeniable. In fact, the advancement of the technology and the interconnection of everyday devices has become the Virtual Learning Environments in a powerful alternative acquiring knowledge. This is possible through an infinite access to information and also a dynamic interaction among people around the world. The capacity of these environments to transmit immersion sensations motivates the users to perform useful activities. Consequently, the users occupy their time on strengthening skills while enhancing their intellectual and social development. The literature review about advanced 3D virtual environments has been a useful and fundamental starting point. This has allowed deepen on the potentialities and possible benefits of the relationship between education and new technologies such as the Internet, multimedia systems, augmented reality, intelligent tutoring, 3D immersion systems among others. Hence, the study of educational games, mobile learning applications, virtual museums and laboratories, from a theoretical point of view, has to be linked to modern learning fields like STEAM (Science, Technology, Engineering, Arts, and Mathematics) education and Special education as well. Based on the before mentioned approaches, an interactive Virtual Advanced Learning Environments (VALE) have been developed. It consists of different advanced 3D virtual scenarios devoted to the teaching of STEAM Education and Special Education fields. Inside these environments, several educational activities are performed in an entertaining way. These learning activities are carried out through virtual laboratories, interactive museums, games challenges, video-modelling activities and training/tests applications. In particular, the VALE for STEAM education includes three scenarios devoted to the Chaos Theory, Educational Robotics and Programming in Scratch. While the VALE for Special Education consists of two applications. The first one is aimed to enhance the Simple Social Skills, and the Social Communication Concepts in people with special needs. The second one is devoted to strengthen skills of Emotion Recognition also in people with special needs. All the activities available inside the VALE give to the users the opportunity of freely discover and learn interesting concepts and develop skills in a fun way while they develop knowledge. The results of the experimentation of the VALE have been satisfactory. In general, the participants showed efficient response during the development of the activities demonstrating an affinity with the knowledge learned. Furthermore, the developed applications for Special Education needs have proven to be useful in specific contexts such as emotion recognition. This has been demonstrated by assessments conducted with people with autism and mental retardation