Lattices and discrete methods in cooperative games and decisions

Questa tesi si pone l'obiettivo di presentare la teoria dei giochi, in particolare di quelli cooperativi, insieme alla teoria delle decisioni, inquadrandole formalmente in termini di matematica discreta. Si tratta di due campi dove l'indagine si origina idealmente da questioni applicative, e dove tuttavia sono sorti e sorgono problemi più tipicamente teorici che hanno interessato e interessano gli ambienti matematico e informatico. Anche se i contributi iniziali sono stati spesso formulati in ambito continuo e utilizzando strumenti tipici di teoria della misura, tuttavia oggi la scelta di modelli e metodi discreti appare la più idonea. L'idea generale è quindi quella di guardare fin da subito al complesso dei modelli e dei risultati che si intendono presentare attraverso la lente della teoria dei reticoli. Ciò consente di avere una visione globale più nitida e di riuscire agilmente ad intrecciare il discorso considerando congiuntamente la teoria dei giochi e quella delle decisioni. Quindi, dopo avere introdotto gli strumenti necessari, si considerano modelli e problemi con il fine preciso di analizzare dapprima risultati storici e solidi, proseguendo poi verso situazioni più recenti, più complesse e nelle quali i risultati raggiunti possono suscitare perplessità. Da ultimo, vengono presentate alcune questioni aperte ed associati spunti per la ricerca.

Simulazione di fluidi in Computer Graphics

Questa tesi si focalizza sullo studio dei modelli fisico-matematici attualmente in uso per la simulazione di fluidi al calcolatore con l’obiettivo di fornire nozioni di base e avanzate sull’utilizzo di tali metodi. La trattazione ha lo scopo di facilitare la comprensione dei principi su cui si fonda la simulazione di fluidi e rappresenta una base per la creazione di un proprio simulatore. E’ possibile studiare le caratteristiche di un fluido in movimento mediante due approcci diversi, l’approccio lagrangiano e l’approccio euleriano. Mentre l’approccio lagrangiano ha lo scopo di conoscere il valore, nel tempo, di una qualsiasi proprietà di ciascuna particella che compone il fluido, l’approccio euleriano, fissato uno o più punti del volume di spazio occupato da quest’ultimo, vuole studiare quello che accade, nel tempo, in quei punti. In particolare, questa tesi approfondisce lo studio delle equazioni di Navier-Stokes, approcciandosi al problema in maniera euleriana. La soluzione numerica del sistema di equazioni differenziali alle derivate parziali derivante dalle equazioni sopracitate, approssima la velocità del fluido, a partire dalla quale è possibile risalire a tutte le grandezze che lo caratterizzano. Attenzione viene riservata anche ad un modello facente parte dell’approccio semi-lagrangiano, il Lattice Boltzmann, considerato una via di mezzo tra i metodi puramente euleriani e quelli lagrangiani, che si basa sulla soluzione dell’equazione di Boltzmann mediante modelli di collisione di particelle. Infine, analogamente al metodo di Lattice Boltzmann, viene trattato il metodo Smoothed Particles Hydrodynamics, tipicamente lagrangiano, secondo il quale solo le proprietà delle particelle comprese dentro il raggio di una funzione kernel, centrata nella particella di interesse, influenzano il valore della particella stessa. Un resoconto pratico della teoria trattata viene dato mediante delle simulazioni realizzate tramite il software Blender 2.76b.

Radiation dose reduction in computer assisted navigation for functional endoscopic sinus surgery--cadaver head experiments and clinical implementation

Computed tomography based navigation for endoscopic sinus surgery is inflationary used despite of major public concern about iatrogenic radiation induced cancer risk. Studies on dose reduction for CAS-CT are almost nonexistent. We validate the use of radiation dose reduced CAS-CT for clinically applied surface registration.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Lessons in Software Evolution Learned by Listening to Smalltalk

The biggest challenge facing software developers today is how to gracefully evolve complex software systems in the face of changing requirements. We clearly need software systems to be more dynamic, compositional and model-centric, but instead we continue to build systems that are static, baroque and inflexible. How can we better build change-enabled systems in the future? To answer this question, we propose to look back to one of the most successful systems to support change, namely Smalltalk. We briefly introduce Smalltalk with a few simple examples, and draw some lessons for software evolution. Smalltalk's simplicity, its reflective design, and its highly dynamic nature all go a long way towards enabling change in Smalltalk applications. We then illustrate how these lessons work in practice by reviewing a number of research projects that support software evolution by exploiting Smalltalk's design. We conclude by summarizing open issues and challenges for change-enabled systems of the future.

Steerable intravitreal inserts for drug delivery: in vitro and ex vivo mobility experiments

The progress of wet age-related macular degeneration can now be controlled by intravitreal drug injection. This approach requires repeated injections, which could be avoided by delivering the drug to the retina. Intraocular implants are a promising solution for drug delivery near the retina. Currently, their accurate placement is challenging, and they can only be removed after a vitrectomy. In this paper, we introduce an approach for minimally invasive retinal drug delivery using magnetic intraocular inserts. We briefly discuss the electromagnetic-control system for magnetic implants and then focus on evaluating their ability to move in the vitreous humor. The mobility of magnetic intraocular implants is estimated in vitro with synthesized vitreous humors, and ex vivo with experiments on cadaver porcine eyes. Preliminary results show that with such magnetic implants a vitrectomy can be avoided.

Influence of Engineering Instructors' Teaching and Learning Beliefs on Pedagogies in Engineering Science Courses

This study explored how academics' beliefs about teaching and learning influenced their teaching in engineering science courses typically taught in the second or third year of 4-year engineering undergraduate degrees. Data were collected via a national survey of 166 U. S. statics instructors and interviews at two different institutions with 17 instructors of engineering science courses such as thermodynamics, circuits and statics. The study identified a number of common beliefs about how to best support student learning of these topics; each is discussed in relation to the literature about student development and learning. Specific recommendations are given for educational developers to encourage use of research-based instructional strategies in these courses.

Endoscopic Image Overlay for the Targeting of Hidden Anatomy in Laparoscopic Visceral Surgery

Limitations associated with the visual information provided to surgeons during laparoscopic surgery increases the difficulty of procedures and thus, reduces clinical indications and increases training time. This work presents a novel augmented reality visualization approach that aims to improve visual data supplied for the targeting of non visible anatomical structures in laparoscopic visceral surgery. The approach aims to facilitate the localisation of hidden structures with minimal damage to surrounding structures and with minimal training requirements. The proposed augmented reality visualization approach incorporates endoscopic images overlaid with virtual 3D models of underlying critical structures in addition to targeting and depth information pertaining to targeted structures. Image overlay was achieved through the implementation of camera calibration techniques and integration of the optically tracked endoscope into an existing image guidance system for liver surgery. The approach was validated in accuracy, clinical integration and targeting experiments. Accuracy of the overlay was found to have a mean value of 3.5 mm ± 1.9 mm and 92.7% of targets within a liver phantom were successfully located laparoscopically by non trained subjects using the approach.