Enhancement of inductive power transfer with flat spiral resonators

The aim of this thesis is to develop a depth analysis of the inductive power transfer (or wireless power transfer, WPT) along a metamaterial composed of cells arranged in a planar configuration, in order to deliver power to a receiver sliding on them. In this way, the problem of the efficiency strongly affected by the weak coupling between emitter and receiver can be obviated, and the distance of transmission can significantly be increased. This study is made using a circuital approach and the magnetoinductive wave (MIW) theory, in order to simply explain the behavior of the transmission coefficient and efficiency from the circuital and experimental point of view. Moreover, flat spiral resonators are used as metamaterial cells, particularly indicated in literature for WPT metamaterials operating at MHz frequencies (5-30 MHz). Finally, this thesis presents a complete electrical characterization of multilayer and multiturn flat spiral resonators and, in particular, it proposes a new approach for the resistance calculation through finite element simulations, in order to consider all the high frequency parasitic effects. Multilayer and multiturn flat spiral resonators are studied in order to decrease the operating frequency down to kHz, maintaining small external dimensions and allowing the metamaterials to be supplied by electronic power converters (resonant inverters).

Kinematics and statics of cable-driven parallel robots by interval-analysis-based methods

In the past two decades the work of a growing portion of researchers in robotics focused on a particular group of machines, belonging to the family of parallel manipulators: the cable robots. Although these robots share several theoretical elements with the better known parallel robots, they still present completely (or partly) unsolved issues. In particular, the study of their kinematic, already a difficult subject for conventional parallel manipulators, is further complicated by the non-linear nature of cables, which can exert only efforts of pure traction. The work presented in this thesis therefore focuses on the study of the kinematics of these robots and on the development of numerical techniques able to address some of the problems related to it. Most of the work is focused on the development of an interval-analysis based procedure for the solution of the direct geometric problem of a generic cable manipulator. This technique, as well as allowing for a rapid solution of the problem, also guarantees the results obtained against rounding and elimination errors and can take into account any uncertainties in the model of the problem. The developed code has been tested with the help of a small manipulator whose realization is described in this dissertation together with the auxiliary work done during its design and simulation phases.

Studi epidemiologici su Pseudomonas syringae pv.actinidiae agente causale del cancro batterico in actinidia

Il cancro batterico dell’actinidia causato da Pseudomonas syringae pv.actinidiae (Psa) suscita grande interesse a livello globale a partire dal 2008. La malattia è comparsa in Giappone e in due anni ha avuto una diffusione epidemica in tutte le aree di coltivazione mondiale di actinidia. Gravi perdite economiche hanno attirato l’attenzione internazionale su questa problematica e grandi sforzi sono stati rivolti allo studio di questo patosistema ancora poco conosciuto. E’ emerso infatti che il patogeno può rimanere in fase latente per lunghi periodi senza causare sintomi caratteristici nelle piante infette, e che dalla comparsa dei sintomi la pianta muore nell’arco di un paio d’anni. Il monitoraggio ed il controllo della situazione è perciò di fondamentale importanza ed è ancora più importante prevenire la comparsa di nuovi focolai di infezione. A questo proposito sarebbe opportuno l’impiego di materiale vegetale di propagazione non infetto, ma in molti casi questo diventa difficile, dal momento che il materiale impiegato è generalmente quello asintomatico, non analizzato precedentemente per la presenza del patogeno. Negli ultimi anni sono state perciò messe a punto molte tecniche molecolari per l’identificazione di Psa direttamente da materiale vegetale. L’obiettivo di questo lavoro è stato quello di studiare l’epidemiologia di Psa in piante adulte infette e di verificare l’efficacia di metodi di diagnosi precoce per prevenire la malattia. A tale scopo il lavoro sperimentale è stato suddiviso in diverse fasi: i) studio della localizzazione, traslocazione e sopravvivenza di Psa nelle piante, a seguito di inoculazione in piante adulte di actinidia di ceppi marcati Psa::gfp; ii) studio della capacità di Psa di essere mantenuto in germogli di actinidia attraverso sette generazioni di micropropagazione dopo l’inoculazione delle piante madri con lo stesso ceppo marcato Psa::gfp; iii) studio ed applicazioni di un nuovo metodo di diagnosi precoce di Psa basato sull’analisi molecolare del “pianto”.

Sviluppo e caratterizzazione microstrutturale e meccanica di leghe innovative di magnesio

Le attività di ricerca svolte nel corso del dottorato di ricerca, sono state focalizzate principalmente sullo studio dell’evoluzione microstrutturale, delle proprietà meccaniche e tribologiche di una particolare lega da fonderia, EV31A, con alte percentuali di terre rare (Nd e Gd > 4% in peso). Le analisi microstrutturali sono state eseguite tramite microscopia ottica (OM), elettronica in scansione (SEM) ed elettronica in trasmissione (TEM), mentre le proprietà meccaniche sono state determinate attraverso prove di trazione e prove di fatica a flessione rotante. Al fine di incrementare le proprietà tribologiche delle leghe di magnesio è stata valutata l’efficacia del trattamento PEO sia sulla lega EV31A, sia sulle più comuni leghe AZ80 e AZ91D, effettuando test tribologici in modalità pattino su cilindro (Block on Ring). Infine è stato condotto uno studio sull’efficacia del trattamento di fusione superficiale laser (LSM), analizzandone gli effetti sia sull’evoluzione microstrutturale, sia sulle proprietà meccaniche e sulla resistenza a corrosione. Le attività svolte nel corso del dottorato di ricerca sono state svolte presso il Dipartimento di Ingegneria Industriale DIN della Scuola di Ingegneria e Architettura dell’Università di Bologna sotto la supervisione della Prof. ssa Lorella Ceschini.

Inertial and magnetic sensors for upper limb kinematics: modeling, calibration, sensor fusion and clinical applications

The primary aim of the research activity presented in this PhD thesis was the development of an innovative hardware and software solution for creating a unique tool for kinematics and electromyographic analysis of the human body in an ecological setting. For this purpose, innovative algorithms have been proposed regarding different aspects of inertial and magnetic data elaboration: magnetometer calibration and magnetic field mapping (Chapter 2), data calibration (Chapter 3) and sensor-fusion algorithm. Topics that may conflict with the confidentiality agreement between University of Bologna and NCS Lab will not be covered in this thesis. After developing and testing the wireless platform, research activities were focused on its clinical validation. The first clinical study aimed to evaluate the intra and interobserver reproducibility in order to evaluate three-dimensional humero-scapulo-thoracic kinematics in an outpatient setting (Chapter 4). A second study aimed to evaluate the effect of Latissimus Dorsi Tendon Transfer on shoulder kinematics and Latissimus Dorsi activation in humerus intra - extra rotations (Chapter 5). Results from both clinical studies have demonstrated the ability of the developed platform to enter into daily clinical practice, providing useful information for patients' rehabilitation.

Fractal descriptors for quantifying brain complexity in MRI

Magnetic Resonance Imaging (MRI) is the in vivo technique most commonly employed to characterize changes in brain structures. The conventional MRI-derived morphological indices are able to capture only partial aspects of brain structural complexity. Fractal geometry and its most popular index, the fractal dimension (FD), can characterize self-similar structures including grey matter (GM) and white matter (WM). Previous literature shows the need for a definition of the so-called fractal scaling window, within which each structure manifests self-similarity. This justifies the existence of fractal properties and confirms Mandelbrot’s assertion that "fractals are not a panacea; they are not everywhere". In this work, we propose a new approach to automatically determine the fractal scaling window, computing two new fractal descriptors, i.e., the minimal and maximal fractal scales (mfs and Mfs). Our method was implemented in a software package, validated on phantoms and applied on large datasets of structural MR images. We demonstrated that the FD is a useful marker of morphological complexity changes that occurred during brain development and aging and, using ultra-high magnetic field (7T) examinations, we showed that the cerebral GM has fractal properties also below the spatial scale of 1 mm. We applied our methodology in two neurological diseases. We observed the reduction of the brain structural complexity in SCA2 patients and, using a machine learning approach, proved that the cerebral WM FD is a consistent feature in predicting cognitive decline in patients with small vessel disease and mild cognitive impairment. Finally, we showed that the FD of the WM skeletons derived from diffusion MRI provides complementary information to those obtained from the FD of the WM general structure in T1-weighted images. In conclusion, the fractal descriptors of structural brain complexity are candidate biomarkers to detect subtle morphological changes during development, aging and in neurological diseases.

Methods for objective and interpretative evaluation of function and motor control: gait in perturbed condition and in the aquatic therapy

Gait analysis allows to characterize motor function, highlighting deviations from normal motor behavior related to an underlying pathology. The widespread use of wearable inertial sensors has opened the way to the evaluation of ecological gait, and a variety of methodological approaches and algorithms have been proposed for the characterization of gait from inertial measures (e.g. for temporal parameters, motor stability and variability, specific pathological alterations). However, no comparative analysis of their performance (i.e. accuracy, repeatability) was available yet, in particular, analysing how this performance is affected by extrinsic (i.e. sensor location, computational approach, analysed variable, testing environmental constraints) and intrinsic (i.e. functional alterations resulting from pathology) factors. The aim of the present project was to comparatively analyze the influence of intrinsic and extrinsic factors on the performance of the numerous algorithms proposed in the literature for the quantification of specific characteristics (i.e. timing, variability/stability) and alterations (i.e. freezing) of gait. Considering extrinsic factors, the influence of sensor location, analyzed variable, and computational approach on the performance of a selection of gait segmentation algorithms from a literature review was analysed in different environmental conditions (e.g. solid ground, sand, in water). Moreover, the influence of altered environmental conditions (i.e. in water) was analyzed as referred to the minimum number of stride necessary to obtain reliable estimates of gait variability and stability metrics, integrating what already available in the literature for over ground gait in healthy subjects. Considering intrinsic factors, the influence of specific pathological conditions (i.e. Parkinson’s Disease) was analyzed as affecting the performance of segmentation algorithms, with and without freezing. Finally, the analysis of the performance of algorithms for the detection of gait freezing showed how results depend on the domain of implementation and IMU position.

Ruolo del gene umano CYYR1: dallo studio funzionale dell'ortologo in Danio rerio alla potenziale implicazione in processi tumorigenici

Il locus CYYR1 identificato e clonato sul cromosoma 21 umano è stato caratterizzato dal punto di vista molecolare come un sistema multitrascritto, esclusivo dei vertebrati che ad oggi è orfano di una funzione specifica. Dati presenti in lettura e rintracciati mostrano una possibile relazione tra il gene CYYR1 e il pathway di Sonic Hedgehog (SHH). In questo progetto di tesi è stato utilizzato il modello animale Danio rerio per indagare il ruolo funzionale dell’ortologo (cyyr1), attraverso esperimenti di gain e loss of function che hanno permesso di dimostrare un suo coinvolgimento nello sviluppo del sistema nervoso centrale, del cuore e del tessuto muscolare. Lo studio dell’ortologo in zebrafish è stato associato all’utilizzo di linee cellulari di rabdomiosarcoma umano. I risultati ottenuti dall’induzione al differenziamento miogenico di queste linee, insieme ai dati ottenuti in Danio rerio, confermano il possibile coinvolgimento del gene CYYR1 nella miogenesi. Lo studio delle relazione tra il pathway di SHH e l’espressione del gene CYYR1 è stato condotto in entrambi i modelli con l’utilizzo di differenti inibitori della via di segnalazione. I risultati ottenuti mostrano che sistemi inibitori agenti direttamente sul recettore SMO riducono l’espressione del gene. Un dato inaspettato in Danio rerio ottenuto durante questi esperimenti di inibizione, ha aperto una nuova linea di ricerca in collaborazione con l’Università di Warwick tesa a verificare la relazione tra il gene cyyr1 e il gene lefty1. Gli esperimenti condotti presso il laboratorio della Prof.ssa Sampath hanno dimostrato la localizzazione del prodotto proteico cyyr1 in Danio rerio e indagato co-localizzazioni con la proteina lefty1. Infine, in collaborazione con Dr. Deflorian e della Prof.ssa Pistocchi, è stato generato un mutante di Danio rerio deleto per il gene cyyr1 con la tecnica CRISPR/Cas9. La caratterizzazione del mutante cyyr1 -/- ha confermato alcuni dei dati ottenuti attraverso esperimenti di loss of function.

Aging assessment of polyethylene-based insulated low-voltage cables for nuclear applications

This thesis aims at investigating the evolution of physico-chemical and electrical properties relevant to low-voltage power cables for nuclear application when subjected to typical nuclear power plant (NPP) environments i.e., to gamma radiation and high temperature. This research is part of the European Project Horizon 2020 TeaM Cables, which aims at providing a novel methodology for efficient and reliable NPP cable aging management to NPP operators. The analyzed samples consist of both coaxial and twisted pair cables with different polymeric compounds used as primary insulation. Insulating materials are based on the same silane cross-linked polyethylene matrix with different additives and fillers. In order to characterize the material response to the environmental stresses, various experimental techniques have been used. These characterizations range from the microscale chemical response e.g. by FTIR, OIT and DSC, to the macroscale electrical and mechanical behavior. A significant part of this Thesis is given to the correlation of the response to aging among the different measured properties. It has been shown that it could be possible to connect both the chemical and mechanical properties of the investigated XLPE cables with the electrical ones. In particular, the high-frequency dielectric response allows an effective monitoring of both the early periods of aging, controlled by the antioxidant consumption kinetics, and then the subsequent oxidation of the polymer matrix. Therefore, dielectric spectroscopy showed to be capable of assessing the LV cable aging state and, it might be used as an aging marker for cable diagnostic. The last part of the manuscript focuses on the building of a predictive modelling approach of LV cable conditions subjected to radio-chemical aging. It resulted into obtaining a lifetime curve which relates the aging factor to which the cable is subjected to, namely the dose rate, with the limit value of the considered electrical property (tanδ).

mHealth-based Methods for Neuromotor Assessment and Rehabilitation

Recent years observed massive growth in wearable technology, everything can be smart: phones, watches, glasses, shirts, etc. These technologies are prevalent in various fields: from wellness/sports/fitness to the healthcare domain. The spread of this phenomenon led the World-Health-Organization to define the term 'mHealth' as "medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices". Furthermore, mHealth solutions are suitable to perform real-time wearable Biofeedback (BF) systems: sensors in the body area network connected to a processing unit (smartphone) and a feedback device (loudspeaker) to measure human functions and return them to the user as (bio)feedback signal. During the COVID-19 pandemic, this transformation of the healthcare system has been dramatically accelerated by new clinical demands, including the need to prevent hospital surges and to assure continuity of clinical care services, allowing pervasive healthcare. Never as of today, we can say that the integration of mHealth technologies will be the basis of this new era of clinical practice. In this scenario, this PhD thesis's primary goal is to investigate new and innovative mHealth solutions for the Assessment and Rehabilitation of different neuromotor functions and diseases. For the clinical assessment, there is the need to overcome the limitations of subjective clinical scales. Creating new pervasive and self-administrable mHealth solutions, this thesis investigates the possibility of employing innovative systems for objective clinical evaluation. For rehabilitation, we explored the clinical feasibility and effectiveness of mHealth systems. In particular, we developed innovative mHealth solutions with BF capability to allow tailored rehabilitation. The main goal that a mHealth-system should have is improving the person's quality of life, increasing or maintaining his autonomy and independence. To this end, inclusive design principles might be crucial, next to the technical and technological ones, to improve mHealth-systems usability.

Robotic Sensing and Manipulation of Deformable Linear Objects with Learning-based methods

Nowadays robotic applications are widespread and most of the manipulation tasks are efficiently solved. However, Deformable-Objects (DOs) still represent a huge limitation for robots. The main difficulty in DOs manipulation is dealing with the shape and dynamics uncertainties, which prevents the use of model-based approaches (since they are excessively computationally complex) and makes sensory data difficult to interpret. This thesis reports the research activities aimed to address some applications in robotic manipulation and sensing of Deformable-Linear-Objects (DLOs), with particular focus to electric wires. In all the works, a significant effort was made in the study of an effective strategy for analyzing sensory signals with various machine learning algorithms. In the former part of the document, the main focus concerns the wire terminals, i.e. detection, grasping, and insertion. First, a pipeline that integrates vision and tactile sensing is developed, then further improvements are proposed for each module. A novel procedure is proposed to gather and label massive amounts of training images for object detection with minimal human intervention. Together with this strategy, we extend a generic object detector based on Convolutional-Neural-Networks for orientation prediction. The insertion task is also extended by developing a closed-loop control capable to guide the insertion of a longer and curved segment of wire through a hole, where the contact forces are estimated by means of a Recurrent-Neural-Network. In the latter part of the thesis, the interest shifts to the DLO shape. Robotic reshaping of a DLO is addressed by means of a sequence of pick-and-place primitives, while a decision making process driven by visual data learns the optimal grasping locations exploiting Deep Q-learning and finds the best releasing point. The success of the solution leverages on a reliable interpretation of the DLO shape. For this reason, further developments are made on the visual segmentation.

Distributed Large-scale Mixed-Integer Optimization with Application to Energy and Multi-robot Networks

Several decision and control tasks in cyber-physical networks can be formulated as large- scale optimization problems with coupling constraints. In these "constraint-coupled" problems, each agent is associated to a local decision variable, subject to individual constraints. This thesis explores the use of primal decomposition techniques to develop tailored distributed algorithms for this challenging set-up over graphs. We first develop a distributed scheme for convex problems over random time-varying graphs with non-uniform edge probabilities. The approach is then extended to unknown cost functions estimated online. Subsequently, we consider Mixed-Integer Linear Programs (MILPs), which are of great interest in smart grid control and cooperative robotics. We propose a distributed methodological framework to compute a feasible solution to the original MILP, with guaranteed suboptimality bounds, and extend it to general nonconvex problems. Monte Carlo simulations highlight that the approach represents a substantial breakthrough with respect to the state of the art, thus representing a valuable solution for new toolboxes addressing large-scale MILPs. We then propose a distributed Benders decomposition algorithm for asynchronous unreliable networks. The framework has been then used as starting point to develop distributed methodologies for a microgrid optimal control scenario. We develop an ad-hoc distributed strategy for a stochastic set-up with renewable energy sources, and show a case study with samples generated using Generative Adversarial Networks (GANs). We then introduce a software toolbox named ChoiRbot, based on the novel Robot Operating System 2, and show how it facilitates simulations and experiments in distributed multi-robot scenarios. Finally, we consider a Pickup-and-Delivery Vehicle Routing Problem for which we design a distributed method inspired to the approach of general MILPs, and show the efficacy through simulations and experiments in ChoiRbot with ground and aerial robots.

Criteri e strumenti per l'applicazione dell'H-BIM. La gestione digitalizzata dei processi tra teoria e prassi

La ricerca si pone l’obiettivo di analizzare strumenti e metodi per l’applicazione dell’H-BIM comprendendone le criticità e fornendo soluzioni utili in questo campo. Al contempo la finalità non è circoscrivibile alla semplice produzione di modelli 3D semanticamente strutturati e parametrici a partire da una nuvola di punti ottenuta con un rilievo digitale, ma si propone di definire i criteri e le metodiche di applicazione delle H-BIM all’interno dell’intero processo. L’impostazione metodologica scelta prevede un processo che parte dalla conoscenza dello stato dell’arte in tema di H-BIM con lo studio dell’attuale normativa in materia e i casi studio di maggior rilevanza. Si è condotta una revisione critica completa della letteratura in merito alla tecnologia BIM e H-BIM, analizzando esperienze di utilizzo della tecnologia BIM nel settore edile globale. Inoltre, al fine di promuovere soluzioni intelligenti all’interno del Facility Management è stato necessario analizzare le criticità presenti nelle procedure, rivedere i processi e i metodi per raccogliere e gestire i dati, nonché individuare le procedure adeguate per garantire il successo dell’implementazione. Sono state evidenziate le potenzialità procedurali e operative legate all’uso sistematico delle innovazioni digitali nell’ottica del Facility Management, oltre che allo studio degli strumenti di acquisizione ed elaborazione dei dati e di post-produzione. Si è proceduto al testing su casi specifici per l’analisi della fase di Scan-to-BIM, differenziati per tipologia di utilizzo, data di costruzione, proprietà e localizzazione. Il percorso seguito ha permesso di porre in luce il significato e le implicazioni dell’utilizzo del BIM nell’ambito del Facility Management, sulla base di una differenziazione delle applicazioni del modello BIM al variare delle condizioni in essere. Infine, sono state definite le conclusioni e formulate raccomandazioni riguardo al futuro utilizzo della tecnologia H-BIM nel settore delle costruzioni. In particolare, definendo l’emergente frontiera del Digital Twin, quale veicolo necessario nel futuro della Costruzione 4.0.

Development of a patient-specific model for stroke risk assessment in atrial fibrillation patients

Atrial fibrillation is associated with a five-fold increase in the risk of cerebrovascular events,being responsible of 15-18% of all strokes.The morphological and functional remodelling of the left atrium caused by atrial fibrillation favours blood stasis and, consequently, stroke risk. In this context, several clinical studies suggest that stroke risk stratification could be improved by using haemodynamic information on the left atrium (LA) and the left atrial appendage (LAA). The goal of this study was to develop a personalized computational fluid-dynamics (CFD) model of the left atrium which could clarify the haemodynamic implications of atrial fibrillation on a patient specific basis. The developed CFD model was first applied to better understand the role of LAA in stroke risk. Infact, the interplay of the LAA geometric parameters such as LAA length, tortuosity, surface area and volume with the fluid-dynamics parameters and the effects of the LAA closure have not been investigated. Results demonstrated the capabilities of the CFD model to reproduce the real physiological behaviour of the blood flow dynamics inside the LA and the LAA. Finally, we determined that the fluid-dynamics parameters enhanced in this research project could be used as new quantitative indexes to describe the different types of AF and open new scenarios for the patient-specific stroke risk stratification.

New methodologies in CT perfusion and MRI analysis to develop cancer imaging biomarkers

Quantitative imaging in oncology aims at developing imaging biomarkers for diagnosis and prediction of cancer aggressiveness and therapy response before any morphological change become visible. This Thesis exploits Computed Tomography perfusion (CTp) and multiparametric Magnetic Resonance Imaging (mpMRI) for investigating diverse cancer features on different organs. I developed a voxel-based image analysis methodology in CTp and extended its use to mpMRI, for performing precise and accurate analyses at single-voxel level. This is expected to improve reproducibility of measurements and cancer mechanisms’ comprehension and clinical interpretability. CTp has not entered the clinical routine yet, although its usefulness in the monitoring of cancer angiogenesis, due to different perfusion computing methods yielding unreproducible results. Instead, machine learning applications in mpMRI, useful to detect imaging features representative of cancer heterogeneity, are mostly limited to clinical research, because of results’ variability and difficult interpretability, which make clinicians not confident in clinical applications. In hepatic CTp, I investigated whether, and under what conditions, two widely adopted perfusion methods, Maximum Slope (MS) and Deconvolution (DV), could yield reproducible parameters. To this end, I developed signal processing methods to model the first pass kinetics and remove any numerical cause hampering the reproducibility. In mpMRI, I proposed a new approach to extract local first-order features, aiming at preserving spatial reference and making their interpretation easier. In CTp, I found out the cause of MS and DV non-reproducibility: MS and DV represent two different states of the system. Transport delays invalidate MS assumptions and, by correcting MS formulation, I have obtained the voxel-based equivalence of the two methods. In mpMRI, the developed predictive models allowed (i) detecting rectal cancers responding to neoadjuvant chemoradiation showing, at pre-therapy, sparse coarse subregions with altered density, and (ii) predicting clinically significant prostate cancers stemming from the disproportion between high- and low- diffusivity gland components.