Digital predistortion for compensation of nonlinearities in Radio over Fiber Links

In order to cope up with the ever increasing demand for larger transmission bandwidth, Radio over Fiber technology is a very beneficial solution. These systems are expected to play a major role within future fifth generation wireless networks due to their inherent capillary distribution properties. Nonlinear compensation techniques are becoming increasingly important to improve the performance of telecommunication channels by compensating for channel nonlinearities. Indeed, significant bounds on the technology usability and performance degradation occur due to nonlinear characteristics of optical transmitter, nonlinear generation of spurious frequencies, which, in the case of RoF links exploiting Directly Modulated Lasers , has the combined effect of laser chirp and optical fiber dispersion among its prevailing causes. The purpose of the research is to analyze some of the main causes of harmonic and intermodulation distortion present in Radio over Fiber (RoF) links, and to suggest a solution to reduce their effects, through a digital predistortion technique. Predistortion is an effective and interesting solution to linearize and this allows to demonstrate that the laser’s chirp and the optical fiber’s dispersion are the main causes which generate harmonic distortion. The improvements illustrated are only theoretical, based on a feasibility point of view. The simulations performed lead to significant improvements for short and long distances of radio over fiber link lengths. The algorithm utilized for simulation has been implemented on MATLAB. The effects of chirp and fiber nonlinearity in a directly modulated fiber transmission system are investigated by simulation, and a cost effective and rather simple technique for compensating these effects is discussed. A detailed description of its functional model is given, and its attractive features both in terms of quality improvement of the received signal, and cost effectiveness of the system are illustrated.

Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models

Most of the existing open-source search engines, utilize keyword or tf-idf based techniques to find relevant documents and web pages relative to an input query. Although these methods, with the help of a page rank or knowledge graphs, proved to be effective in some cases, they often fail to retrieve relevant instances for more complicated queries that would require a semantic understanding to be exploited. In this Thesis, a self-supervised information retrieval system based on transformers is employed to build a semantic search engine over the library of Gruppo Maggioli company. Semantic search or search with meaning can refer to an understanding of the query, instead of simply finding words matches and, in general, it represents knowledge in a way suitable for retrieval. We chose to investigate a new self-supervised strategy to handle the training of unlabeled data based on the creation of pairs of ’artificial’ queries and the respective positive passages. We claim that by removing the reliance on labeled data, we may use the large volume of unlabeled material on the web without being limited to languages or domains where labeled data is abundant.

Knowledge-based Chord Embeddings

This thesis develops AI methods as a contribution to computational musicology, an interdisciplinary field that studies music with computers. In systematic musicology a composition is defined as the combination of harmony, melody and rhythm. According to de La Borde, harmony alone "merits the name of composition". This thesis focuses on analysing the harmony from a computational perspective. We concentrate on symbolic music representation and address the problem of formally representing chord progressions in western music compositions. Informally, chords are sets of pitches played simultaneously, and chord progressions constitute the harmony of a composition. Our approach combines ML techniques with knowledge-based techniques. We design and implement the Modal Harmony ontology (MHO), using OWL. It formalises one of the most important theories in western music: the Modal Harmony Theory. We propose and experiment with different types of embedding methods to encode chords, inspired by NLP and adapted to the music domain, using both statistical (extensional) knowledge by relying on a huge dataset of chord annotations (ChoCo), intensional knowledge by relying on MHO and a combination of the two. The methods are evaluated on two musicologically relevant tasks: chord classification and music structure segmentation. The former is verified by comparing the results of the Odd One Out algorithm to the classification obtained with MHO. Good performances (accuracy: 0.86) are achieved. We feed a RNN for the latter, using our embeddings. Results show that the best performance (F1: 0.6) is achieved with embeddings that combine both approaches. Our method outpeforms the state of the art (F1 = 0.42) for symbolic music structure segmentation. It is worth noticing that embeddings based only on MHO almost equal the best performance (F1 = 0.58). We remark that those embeddings only require the ontology as an input as opposed to other approaches that rely on large datasets.

Synthesis, characterization, and application of PHB-based gels for cultural heritage conservation

The aim of my master thesis is developing novel, greener approaches for the cleaning of artworks: such treatment consists in the removal of old varnish layers which tend to discolor or darken with time, thus allowing replacement with a new protecting coat. While protocols presently applied can be effective in the cleaning of the artworks, none of them take into account conservators’ health safety and environmental issues. Thus, using biomass-derived components, which are non-toxic and reusable and/or compostable might bring into the heritage conservation an additional awareness about safety and environmental claiming. The laboratory work for the thesis is a collaborative work between different groups. The biggest part of the work was at the Polymer group where gels were synthesized using Polyhydroxybutyrate (PHB) from sustainable resources and green solvents. The use of the gels might help to reduce the volatilization of solvents and contributes to the localization of the cleaning action. After the preparation of the gels, different characterization methods were used in order to estimate their properties and shelf-life. Finally, the work was completed on the application of the gels on sculpture, coated with undesired layers to be removed. Here, pre-mapping of the areas of interest was realized with different optical techniques, followed by the application of the gels for the cleaning and analyzing the effectiveness of cleaning.

Detecting social patterns within 20th century documentary photos: a deep learning based approach

The job of a historian is to understand what happened in the past, resorting in many cases to written documents as a firsthand source of information. Text, however, does not amount to the only source of knowledge. Pictorial representations, in fact, have also accompanied the main events of the historical timeline. In particular, the opportunity of visually representing circumstances has bloomed since the invention of photography, with the possibility of capturing in real-time the occurrence of a specific events. Thanks to the widespread use of digital technologies (e.g. smartphones and digital cameras), networking capabilities and consequent availability of multimedia content, the academic and industrial research communities have developed artificial intelligence (AI) paradigms with the aim of inferring, transferring and creating new layers of information from images, videos, etc. Now, while AI communities are devoting much of their attention to analyze digital images, from an historical research standpoint more interesting results may be obtained analyzing analog images representing the pre-digital era. Within the aforementioned scenario, the aim of this work is to analyze a collection of analog documentary photographs, building upon state-of-the-art deep learning techniques. In particular, the analysis carried out in this thesis aims at producing two following results: (a) produce the date of an image, and, (b) recognizing its background socio-cultural context,as defined by a group of historical-sociological researchers. Given these premises, the contribution of this work amounts to: (i) the introduction of an historical dataset including images of “Family Album” among all the twentieth century, (ii) the introduction of a new classification task regarding the identification of the socio-cultural context of an image, (iii) the exploitation of different deep learning architectures to perform the image dating and the image socio-cultural context classification.

Theoretical and experimental studies of gold catalysts for glucose oxidation

The glucaric acid (GLA) has been identified as a “top value-added chemical from biomass” that can be employed for many uses; for instance, it could be a precursor of adipic acid, a monomer of Nylon-6,6. GLA can be synthetized by the oxidation of glucose (GLU), passing through the intermediate gluconic acid (GLO). In recent years, a new process has been sought to obtain GLA in an economic and environmental sustainable way, in order to replace the current use of HNO3 as a stoichiometric oxidant, or electrocatalysis and biochemical synthesis, which show several disadvantages. Thereby, this work is focused on the study of catalysts based on gold nanoparticles supported on activated carbon for the oxidation reaction of GLU to GLA using O2 as an oxidant agent and NaOH as base. The sol-immobilization method leads us to obtain small and well dispersed nanoparticles, characterized by UV-Vis, XRD and TEM techniques. Repeating the reaction on different batches of catalyst, both the synthesis and the reaction were confirmed to be reproducible. The effect of the reaction time feeding GLO as reagent was studied: the results show that the conversion of GLO increases as the reaction time increases; however, the yields of GLA and others increase up to 1 hour, and then they remain constant. In order to obtain information on the catalytic mechanism at the atomistic level, a computational study based on density functional theory and atomistic modeling of the gold nano-catalyst were performed. Highly symmetric (icosahedral and cubo-octahedral) and distorted Au55 nanoparticles have been optimized along with Au(111) and Au(100) surfaces. Distorted structures were found to be more stable than symmetrical ones due to relativistic effects. On these various models the adsorptions of various species involved in the catalysis have been studied, including OH- species, GLU and GLO. The study carried out aims to provide a method for approaching to the study of nanoparticellary catalytic systems.

Theoretical and experimental investigations on homologation of ethanol to butanol using a ruthenium-based catalyst

The mechanism of homologation of bioethanol to butanol and higher alcohols via the Guerbet reaction was computationally and experimentally investigated. The catalytic pathway involves a ruthenium-based complex and a base co-catalyst which work simultaneously. Due to selectivity issues, secondary products were formed and high competition between main pathway and side reactions was recorded. Herein, the overall catalytic mechanism for all the processes involved in was investigated, also considering the principal side reactions, using density functional theory (DFT) methods and experiments to confirm theoretical outcomes. Due to the complexity of the reaction network, kinetic simulations were established from DFT results, confirming experimental products distribution and giving insights into the factors governing the reaction mechanism.

Multi-scalar critical theories: first steps for a non perturbative functional renormalization group analysis

In this work the fundamental ideas to study properties of QFTs with the functional Renormalization Group are presented and some examples illustrated. First the Wetterich equation for the effective average action and its flow in the local potential approximation (LPA) for a single scalar field is derived. This case is considered to illustrate some techniques used to solve the RG fixed point equation and study the properties of the critical theories in D dimensions. In particular the shooting methods for the ODE equation for the fixed point potential as well as the approach which studies a polynomial truncation with a finite number of couplings, which is convenient to study the critical exponents. We then study novel cases related to multi field scalar theories, deriving the flow equations for the LPA truncation, both without assuming any global symmetry and also specialising to cases with a given symmetry, using truncations based on polynomials of the symmetry invariants. This is used to study possible non perturbative solutions of critical theories which are extensions of known perturbative results, obtained in the epsilon expansion below the upper critical dimension.