Cooperative Wireless Networks for Localization

This thesis adresses the problem of localization, and analyzes its crucial aspects, within the context of cooperative WSNs. The three main issues discussed in the following are: network synchronization, position estimate and tracking. Time synchronization is a fundamental requirement for every network. In this context, a new approach based on the estimation theory is proposed to evaluate the ultimate performance limit in network time synchronization. In particular the lower bound on the variance of the average synchronization error in a fully connected network is derived by taking into account the statistical characterization of the Message Delivering Time (MDT) . Sensor network localization algorithms estimate the locations of sensors with initially unknown location information by using knowledge of the absolute positions of a few sensors and inter-sensor measurements such as distance and bearing measurements. Concerning this issue, i.e. the position estimate problem, two main contributions are given. The first is a new Semidefinite Programming (SDP) framework to analyze and solve the problem of flip-ambiguity that afflicts range-based network localization algorithms with incomplete ranging information. The occurrence of flip-ambiguous nodes and errors due to flip ambiguity is studied, then with this information a new SDP formulation of the localization problem is built. Finally a flip-ambiguity-robust network localization algorithm is derived and its performance is studied by Monte-Carlo simulations. The second contribution in the field of position estimate is about multihop networks. A multihop network is a network with a low degree of connectivity, in which couples of given any nodes, in order to communicate, they have to rely on one or more intermediate nodes (hops). Two new distance-based source localization algorithms, highly robust to distance overestimates, typically present in multihop networks, are presented and studied. The last point of this thesis discuss a new low-complexity tracking algorithm, inspired by the Fano’s sequential decoding algorithm for the position tracking of a user in a WLAN-based indoor localization system.

Diritti umani e diritto alla salute - la questione dell’accesso ai farmaci come un problema di Giustizia Globale

Riconosciuto il problema dell’accesso ai farmaci come un problema di giustizia globale, la dissertazione, da un lato, è incentrata sullo studio dei diritti umani e sul diritto alla salute da una prospettiva giusfilosofica e, dall’altro, è finalizzata ad analizzare la disciplina brevettuale internazionale, sia approfondendo gli interessi realmente in gioco, sia studiando la struttura economica del brevetto stesso. Si è cercato quindi di guardare a tali interessi da una nuova prospettiva, ipotizzando una gerarchia di valori che sia completa e coerente con gli obiettivi che la dottrina, la giurisprudenza, nonché il diritto internazionale formalmente enunciano. Il progetto di ricerca vuole, in definitiva, arrivare a proporre nuove soluzioni giuridiche al problema dell’accesso ai farmaci. La dissertazione svolge pertanto uno studio critico della proposta di Thomas Pogge, di natura politica e giuridica e sorretta da istanze filosofiche, volta alla soluzione del problema dell’accesso ai farmaci, i.e. l’Health Impact Fund (HIF). Proposta che pone radicalmente in discussione, anche concretamente, il dogma del monopolio concesso con la privativa quale ricompensa per i costi di R&D sostenuti dai titolari dei brevetti e che pone, invece, l’accento sull’effettivo impatto sulla salute globale di ogni singola invenzione. Analizzandone approfonditamente gli aspetti più rilevanti, si passano poi in rassegna, criticamente, le proposte, alternative o di riforma, del sistema di proprietà intellettuale, volte al miglioramento dell’accesso ai farmaci; a tal proposito, si propone quindi una riforma transitoria della disciplina brevettuale, c.d. Trading Time for Space (TTS), che prevede un allungamento temporale dell’esclusiva brevettuale (Time) in cambio della vendita da parte del titolare della privativa del farmaco ad un prezzo accessibile nei Paesi in via di sviluppo (Space).

Multiresolution spherical wavelet analysis in global seismic tomography

Every seismic event produces seismic waves which travel throughout the Earth. Seismology is the science of interpreting measurements to derive information about the structure of the Earth. Seismic tomography is the most powerful tool for determination of 3D structure of deep Earth's interiors. Tomographic models obtained at the global and regional scales are an underlying tool for determination of geodynamical state of the Earth, showing evident correlation with other geophysical and geological characteristics. The global tomographic images of the Earth can be written as a linear combinations of basis functions from a specifically chosen set, defining the model parameterization. A number of different parameterizations are commonly seen in literature: seismic velocities in the Earth have been expressed, for example, as combinations of spherical harmonics or by means of the simpler characteristic functions of discrete cells. With this work we are interested to focus our attention on this aspect, evaluating a new type of parameterization, performed by means of wavelet functions. It is known from the classical Fourier theory that a signal can be expressed as the sum of a, possibly infinite, series of sines and cosines. This sum is often referred as a Fourier expansion. The big disadvantage of a Fourier expansion is that it has only frequency resolution and no time resolution. The Wavelet Analysis (or Wavelet Transform) is probably the most recent solution to overcome the shortcomings of Fourier analysis. The fundamental idea behind this innovative analysis is to study signal according to scale. Wavelets, in fact, are mathematical functions that cut up data into different frequency components, and then study each component with resolution matched to its scale, so they are especially useful in the analysis of non stationary process that contains multi-scale features, discontinuities and sharp strike. Wavelets are essentially used in two ways when they are applied in geophysical process or signals studies: 1) as a basis for representation or characterization of process; 2) as an integration kernel for analysis to extract information about the process. These two types of applications of wavelets in geophysical field, are object of study of this work. At the beginning we use the wavelets as basis to represent and resolve the Tomographic Inverse Problem. After a briefly introduction to seismic tomography theory, we assess the power of wavelet analysis in the representation of two different type of synthetic models; then we apply it to real data, obtaining surface wave phase velocity maps and evaluating its abilities by means of comparison with an other type of parametrization (i.e., block parametrization). For the second type of wavelet application we analyze the ability of Continuous Wavelet Transform in the spectral analysis, starting again with some synthetic tests to evaluate its sensibility and capability and then apply the same analysis to real data to obtain Local Correlation Maps between different model at same depth or between different profiles of the same model.

Machine learning methods for prediction of disulphide bonding states of cysteine residues in proteins

The goal of this thesis work is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of cysteine residues in proteins, which is a sub-problem of a bigger and yet unsolved problem of protein structure prediction. Improvement in the prediction of disulfide bonding states of cysteine residues will help in putting a constraint in the three dimensional (3D) space of the respective protein structure, and thus will eventually help in the prediction of 3D structure of proteins. Results of this work will have direct implications in site-directed mutational studies of proteins, proteins engineering and the problem of protein folding. We have used a combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM), the so-called Hidden Neural Network (HNN) as a machine learning technique to develop our prediction method. By using different global and local features of proteins (specifically profiles, parity of cysteine residues, average cysteine conservation, correlated mutation, sub-cellular localization, and signal peptide) as inputs and considering Eukaryotes and Prokaryotes separately we have reached to a remarkable accuracy of 94% on cysteine basis for both Eukaryotic and Prokaryotic datasets, and an accuracy of 90% and 93% on protein basis for Eukaryotic dataset and Prokaryotic dataset respectively. These accuracies are best so far ever reached by any existing prediction methods, and thus our prediction method has outperformed all the previously developed approaches and therefore is more reliable. Most interesting part of this thesis work is the differences in the prediction performances of Eukaryotes and Prokaryotes at the basic level of input coding when ‘profile’ information was given as input to our prediction method. And one of the reasons for this we discover is the difference in the amino acid composition of the local environment of bonded and free cysteine residues in Eukaryotes and Prokaryotes. Eukaryotic bonded cysteine examples have a ‘symmetric-cysteine-rich’ environment, where as Prokaryotic bonded examples lack it.

GNSS interference management techniques against malicious attacks

This thesis collects the outcomes of a Ph.D. course in Telecommunications Engineering and it is focused on the study and design of possible techniques able to counteract interference signal in Global Navigation Satellite System (GNSS) systems. The subject is the jamming threat in navigation systems, that has become a very increasingly important topic in recent years, due to the wide diffusion of GNSS-based civil applications. Detection and mitigation techniques are developed in order to fight out jamming signals, tested in different scenarios and including sophisticated signals. The thesis is organized in two main parts, which deal with management of GNSS intentional counterfeit signals. The first part deals with the interference management, focusing on the intentional interfering signal. In particular, a technique for the detection and localization of the interfering signal level in the GNSS bands in frequency domain has been proposed. In addition, an effective mitigation technique which exploits the periodic characteristics of the common jamming signals reducing interfering effects at the receiver side has been introduced. Moreover, this technique has been also tested in a different and more complicated scenario resulting still effective in mitigation and cancellation of the interfering signal, without high complexity. The second part still deals with the problem of interference management, but regarding with more sophisticated signal. The attention is focused on the detection of spoofing signal, which is the most complex among the jamming signal types. Due to this highly difficulty in detect and mitigate this kind of signal, spoofing threat is considered the most dangerous. In this work, a possible techniques able to detect this sophisticated signal has been proposed, observing and exploiting jointly the outputs of several operational block measurements of the GNSS receiver operating chain.

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”.