Efficient ultrasonic signal processing techniques for aided medical diagnostics

Ultrasound imaging is widely used in medical diagnostics as it is the fastest, least invasive, and least expensive imaging modality. However, ultrasound images are intrinsically difficult to be interpreted. In this scenario, Computer Aided Detection (CAD) systems can be used to support physicians during diagnosis providing them a second opinion. This thesis discusses efficient ultrasound processing techniques for computer aided medical diagnostics, focusing on two major topics: (i) Ultrasound Tissue Characterization (UTC), aimed at characterizing and differentiating between healthy and diseased tissue; (ii) Ultrasound Image Segmentation (UIS), aimed at detecting the boundaries of anatomical structures to automatically measure organ dimensions and compute clinically relevant functional indices. Research on UTC produced a CAD tool for Prostate Cancer detection to improve the biopsy protocol. In particular, this thesis contributes with: (i) the development of a robust classification system; (ii) the exploitation of parallel computing on GPU for real-time performance; (iii) the introduction of both an innovative Semi-Supervised Learning algorithm and a novel supervised/semi-supervised learning scheme for CAD system training that improve system performance reducing data collection effort and avoiding collected data wasting. The tool provides physicians a risk map highlighting suspect tissue areas, allowing them to perform a lesion-directed biopsy. Clinical validation demonstrated the system validity as a diagnostic support tool and its effectiveness at reducing the number of biopsy cores requested for an accurate diagnosis. For UIS the research developed a heart disease diagnostic tool based on Real-Time 3D Echocardiography. Thesis contributions to this application are: (i) the development of an automated GPU based level-set segmentation framework for 3D images; (ii) the application of this framework to the myocardium segmentation. Experimental results showed the high efficiency and flexibility of the proposed framework. Its effectiveness as a tool for quantitative analysis of 3D cardiac morphology and function was demonstrated through clinical validation.

Development and characterization of micromachined devices for separation techniques

Nowadays microfluidic is becoming an important technology in many chemical and biological processes and analysis applications. The potential to replace large-scale conventional laboratory instrumentation with miniaturized and self-contained systems, (called lab-on-a-chip (LOC) or point-of-care-testing (POCT)), offers a variety of advantages such as low reagent consumption, faster analysis speeds, and the capability of operating in a massively parallel scale in order to achieve high-throughput. Micro-electro-mechanical-systems (MEMS) technologies enable both the fabrication of miniaturized system and the possibility of developing compact and portable systems. The work described in this dissertation is towards the development of micromachined separation devices for both high-speed gas chromatography (HSGC) and gravitational field-flow fractionation (GrFFF) using MEMS technologies. Concerning the HSGC, a complete platform of three MEMS-based GC core components (injector, separation column and detector) is designed, fabricated and characterized. The microinjector consists of a set of pneumatically driven microvalves, based on a polymeric actuating membrane. Experimental results demonstrate that the microinjector is able to guarantee low dead volumes, fast actuation time, a wide operating temperature range and high chemical inertness. The microcolumn consists of an all-silicon microcolumn having a nearly circular cross-section channel. The extensive characterization has produced separation performances very close to the theoretical ideal expectations. A thermal conductivity detector (TCD) is chosen as most proper detector to be miniaturized since the volume reduction of the detector chamber results in increased mass and reduced dead volumes. The microTDC shows a good sensitivity and a very wide dynamic range. Finally a feasibility study for miniaturizing a channel suited for GrFFF is performed. The proposed GrFFF microchannel is at early stage of development, but represents a first step for the realization of a highly portable and potentially low-cost POCT device for biomedical applications.

Milk and dairy products: evaluation of bioactive components by analytical techniques

Milk and dairy products are important source of bioactive compounds useful to satisfy the nutritional and physiological needs of any newborns of mammalian species and useful to guarantee adequate growth and development of infants as well as provide a complete nourishment of adults. Physico-chemical, nutritional and organoleptic properties of the main constituents and the “minor” components have a crucial role in the quality of milk and milk products. Although in the past decades dietary milk fat was often regarded as harmful for the human health, recent researches suggest that milk contains specific fatty acids with nutritional and physiological health benefits. For these reasons, a major attention is given to the quantity and quality of total fat intake. In the recent years, as a result of the new concept of multifunctional agriculture and the changing behaviours about diet, consumer demands in favor of high-quality, security and safety dairy products are increased. Moreover, milk proteins and milk-derived bioactive peptides are recognized to have a high nutritive value, several health-promoting functional activities and excellent technological properties. Accordingly, growing interest in the development of functional dairy products and preparation of infant formulae for babies who cannot be breast-fed, has been give in order to meet the specific consumer’s requests. This manuscript presents the main results obtained during my PhD research aimed to evaluate the main bioactive lipids and proteins in milk and dairy products using innovative analytical techniques. The experimental section of this manuscript is divided in two sections where are reported the main results obtained during my research activities on dairy products and human milks in order to characterize their bioactive compounds for functional food applications.

Advanced Techniques for Face Recognition under Challenging Environments

Automatically recognizing faces captured under uncontrolled environments has always been a challenging topic in the past decades. In this work, we investigate cohort score normalization that has been widely used in biometric verification as means to improve the robustness of face recognition under challenging environments. In particular, we introduce cohort score normalization into undersampled face recognition problem. Further, we develop an effective cohort normalization method specifically for the unconstrained face pair matching problem. Extensive experiments conducted on several well known face databases demonstrate the effectiveness of cohort normalization on these challenging scenarios. In addition, to give a proper understanding of cohort behavior, we study the impact of the number and quality of cohort samples on the normalization performance. The experimental results show that bigger cohort set size gives more stable and often better results to a point before the performance saturates. And cohort samples with different quality indeed produce different cohort normalization performance. Recognizing faces gone after alterations is another challenging problem for current face recognition algorithms. Face image alterations can be roughly classified into two categories: unintentional (e.g., geometrics transformations introduced by the acquisition devide) and intentional alterations (e.g., plastic surgery). We study the impact of these alterations on face recognition accuracy. Our results show that state-of-the-art algorithms are able to overcome limited digital alterations but are sensitive to more relevant modifications. Further, we develop two useful descriptors for detecting those alterations which can significantly affect the recognition performance. In the end, we propose to use the Structural Similarity (SSIM) quality map to detect and model variations due to plastic surgeries. Extensive experiments conducted on a plastic surgery face database demonstrate the potential of SSIM map for matching face images after surgeries.

Innovative techniques for sport turf managing: Agronomic and phyisiological implications

The growing substrate of the putting greens is considered a key factor for a healthy turf ecosystem. Actually detailed study on the effects of growth promoting bacteria and biostimulants on a professional sport turf are very limited. This thesis aimed to study the effectiveness of different microorganisms and biostimulants in order to improve the knowledge relative to the relationship between the beneficial microflora and root apparatus of sport turfs. The research project was divided in three principal steps: Initially, commercial products based on biostimulants and microorganisms were tested on a Lolium perenne L. essence grown in a controlled-environment. The principal evaluations were the study of the habitus of plants, biomass production and length of leaves and roots. Were studied the capacity of colonization of microorganisms within root tissues and rhizosphere. In the second step were developed two different biostimulant solutions based on effective microorganisms, mycorrhizae and humic acids. This test was conducted both on an Agrostis stolonifera putting green (Modena Golf & Country Club) in a semi-field condition and within a growth chamber on a Lolium perenne L. essence. Fungicide and chemicals applications were suspended in order to assess the effectiveness of the inoculants for nutrition and control of pests. In the last step, different microorganism mixes and biostimulants were tested on an experimental putting green in the Turf Research Center (TRC) (Virginia Tech, United States) in a real managing situation. The effects of different treatments were studied maintaining all chemicals and mechanicals managements scheduled during a sport season. Both growth-chamber and field results confirmed the capacity of microorganisms based biostimulants to promote the physiologic conditions of the plants, improve the growth of the roots and enhance the aesthetic performance of the turf. Molecular analysis confirmed the capacity of microorganisms to colonize the root tissues.

Electric techniques for the assessment of quality parameters of foodstuffs

In food industry, quality assurance requires low cost methods for the rapid assessment of the parameters that affect product stability. Foodstuffs are complex in their structure, mainly composed by gaseous, liquid and solid phases which often coexist in the same product. Special attention is given to water, concerned as natural component of the major food product or as added ingredient of a production process. Particularly water is structurally present in the matrix and not completely available. In this way, water can be present in foodstuff in many different states: as water of crystallization, bound to protein or starch molecules, entrapped in biopolymer networks or adsorbed on solid surfaces of porous food particles. The traditional technique for the assessment of food quality give reliable information but are destructive, time consuming and unsuitable for on line application. The techniques proposed answer to the limited disposition of time and could be able to characterize the main compositional parameters. Dielectric interaction response is mainly related to water and could be useful not only to provide information on the total content but also on the degree of mobility of this ubiquitous molecule in different complex food matrix. In this way the proposal of this thesis is to answer at this need. Dielectric and electric tool can be used for the scope and led us to describe the complex food matrix and predict food characteristic. The thesis is structured in three main part, in the first one some theoretical tools are recalled to well assess the food parameter involved in the quality definition and the techniques able to reply at the problem emerged. The second part explains the research conducted and the experimental plans are illustrated in detail. Finally the last section is left for rapid method easily implementable in an industrial process.

Application of Deep Learning techniques in the search for BSM Higgs bosons in the $\mu\mu$ final state in CMS

The Standard Model (SM) of particle physics predicts the existence of a Higgs field responsible for the generation of particles' mass. However, some aspects of this theory remain unsolved, supposing the presence of new physics Beyond the Standard Model (BSM) with the production of new particles at a higher energy scale compared to the current experimental limits. The search for additional Higgs bosons is, in fact, predicted by theoretical extensions of the SM including the Minimal Supersymmetry Standard Model (MSSM). In the MSSM, the Higgs sector consists of two Higgs doublets, resulting in five physical Higgs particles: two charged bosons $H^{\pm}$, two neutral scalars $h$ and $H$, and one pseudoscalar $A$. The work presented in this thesis is dedicated to the search of neutral non-Standard Model Higgs bosons decaying to two muons in the model independent MSSM scenario. Proton-proton collision data recorded by the CMS experiment at the CERN LHC at a center-of-mass energy of 13 TeV are used, corresponding to an integrated luminosity of $35.9\ \text{fb}^{-1}$. Such search is sensitive to neutral Higgs bosons produced either via gluon fusion process or in association with a $\text{b}\bar{\text{b}}$ quark pair. The extensive usage of Machine and Deep Learning techniques is a fundamental element in the discrimination between signal and background simulated events. A new network structure called parameterised Neural Network (pNN) has been implemented, replacing a whole set of single neural networks trained at a specific mass hypothesis value with a single neural network able to generalise well and interpolate in the entire mass range considered. The results of the pNN signal/background discrimination are used to set a model independent 95\% confidence level expected upper limit on the production cross section times branching ratio, for a generic $\phi$ boson decaying into a muon pair in the 130 to 1000 GeV range.

Big Data approaches as a support for precision livestock farming techniques

With the advent of new technologies it is increasingly easier to find data of different nature from even more accurate sensors that measure the most disparate physical quantities and with different methodologies. The collection of data thus becomes progressively important and takes the form of archiving, cataloging and online and offline consultation of information. Over time, the amount of data collected can become so relevant that it contains information that cannot be easily explored manually or with basic statistical techniques. The use of Big Data therefore becomes the object of more advanced investigation techniques, such as Machine Learning and Deep Learning. In this work some applications in the world of precision zootechnics and heat stress accused by dairy cows are described. Experimental Italian and German stables were involved for the training and testing of the Random Forest algorithm, obtaining a prediction of milk production depending on the microclimatic conditions of the previous days with satisfactory accuracy. Furthermore, in order to identify an objective method for identifying production drops, compared to the Wood model, typically used as an analytical model of the lactation curve, a Robust Statistics technique was used. Its application on some sample lactations and the results obtained allow us to be confident about the use of this method in the future.