Procedures of quality control and data analysis of multi-site ground-based observations for the absolute flux calibration of Gaia

The Gaia space mission is a major project for the European astronomical community. As challenging as it is, the processing and analysis of the huge data-flow incoming from Gaia is the subject of thorough study and preparatory work by the DPAC (Data Processing and Analysis Consortium), in charge of all aspects of the Gaia data reduction. This PhD Thesis was carried out in the framework of the DPAC, within the team based in Bologna. The task of the Bologna team is to define the calibration model and to build a grid of spectro-photometric standard stars (SPSS) suitable for the absolute flux calibration of the Gaia G-band photometry and the BP/RP spectrophotometry. Such a flux calibration can be performed by repeatedly observing each SPSS during the life-time of the Gaia mission and by comparing the observed Gaia spectra to the spectra obtained by our ground-based observations. Due to both the different observing sites involved and the huge amount of frames expected (≃100000), it is essential to maintain the maximum homogeneity in data quality, acquisition and treatment, and a particular care has to be used to test the capabilities of each telescope/instrument combination (through the “instrument familiarization plan”), to devise methods to keep under control, and eventually to correct for, the typical instrumental effects that can affect the high precision required for the Gaia SPSS grid (a few % with respect to Vega). I contributed to the ground-based survey of Gaia SPSS in many respects: with the observations, the instrument familiarization plan, the data reduction and analysis activities (both photometry and spectroscopy), and to the maintenance of the data archives. However, the field I was personally responsible for was photometry and in particular relative photometry for the production of short-term light curves. In this context I defined and tested a semi-automated pipeline which allows for the pre-reduction of imaging SPSS data and the production of aperture photometry catalogues ready to be used for further analysis. A series of semi-automated quality control criteria are included in the pipeline at various levels, from pre-reduction, to aperture photometry, to light curves production and analysis.

Calcolo della posizione di reti di stazioni permanenti

Con il trascorrere del tempo, le reti di stazioni permanenti GNSS (Global Navigation Satellite System) divengono sempre più un valido supporto alle tecniche di rilevamento satellitare. Esse sono al tempo stesso un’efficace materializzazione del sistema di riferimento e un utile ausilio ad applicazioni di rilevamento topografico e di monitoraggio per il controllo di deformazioni. Alle ormai classiche applicazioni statiche in post-processamento, si affiancano le misure in tempo reale sempre più utilizzate e richieste dall’utenza professionale. In tutti i casi risulta molto importante la determinazione di coordinate precise per le stazioni permanenti, al punto che si è deciso di effettuarla tramite differenti ambienti di calcolo. Sono stati confrontati il Bernese, il Gamit (che condividono l’approccio differenziato) e il Gipsy (che utilizza l’approccio indifferenziato). L’uso di tre software ha reso indispensabile l’individuazione di una strategia di calcolo comune in grado di garantire che, i dati ancillari e i parametri fisici adottati, non costituiscano fonte di diversificazione tra le soluzioni ottenute. L’analisi di reti di dimensioni nazionali oppure di reti locali per lunghi intervalli di tempo, comporta il processamento di migliaia se non decine di migliaia di file; a ciò si aggiunge che, talora a causa di banali errori, oppure al fine di elaborare test scientifici, spesso risulta necessario reiterare le elaborazioni. Molte risorse sono quindi state investite nella messa a punto di procedure automatiche finalizzate, da un lato alla preparazione degli archivi e dall’altro all’analisi dei risultati e al loro confronto qualora si sia in possesso di più soluzioni. Dette procedure sono state sviluppate elaborando i dataset più significativi messi a disposizione del DISTART (Dipartimento di Ingegneria delle Strutture, dei Trasporti, delle Acque, del Rilevamento del Territorio - Università di Bologna). E’ stato così possibile, al tempo stesso, calcolare la posizione delle stazioni permanenti di alcune importanti reti locali e nazionali e confrontare taluni fra i più importanti codici scientifici che assolvono a tale funzione. Per quanto attiene il confronto fra i diversi software si è verificato che: • le soluzioni ottenute dal Bernese e da Gamit (i due software differenziati) sono sempre in perfetto accordo; • le soluzioni Gipsy (che utilizza il metodo indifferenziato) risultano, quasi sempre, leggermente più disperse rispetto a quelle degli altri software e mostrano talvolta delle apprezzabili differenze numeriche rispetto alle altre soluzioni, soprattutto per quanto attiene la coordinata Est; le differenze sono però contenute in pochi millimetri e le rette che descrivono i trend sono comunque praticamente parallele a quelle degli altri due codici; • il citato bias in Est tra Gipsy e le soluzioni differenziate, è più evidente in presenza di determinate combinazioni Antenna/Radome e sembra essere legato all’uso delle calibrazioni assolute da parte dei diversi software. E’ necessario altresì considerare che Gipsy è sensibilmente più veloce dei codici differenziati e soprattutto che, con la procedura indifferenziata, il file di ciascuna stazione di ciascun giorno, viene elaborato indipendentemente dagli altri, con evidente maggior elasticità di gestione: se si individua un errore strumentale su di una singola stazione o se si decide di aggiungere o togliere una stazione dalla rete, non risulta necessario il ricalcolo dell’intera rete. Insieme alle altre reti è stato possibile analizzare la Rete Dinamica Nazionale (RDN), non solo i 28 giorni che hanno dato luogo alla sua prima definizione, bensì anche ulteriori quattro intervalli temporali di 28 giorni, intercalati di sei mesi e che coprono quindi un intervallo temporale complessivo pari a due anni. Si è così potuto verificare che la RDN può essere utilizzata per l’inserimento in ITRF05 (International Terrestrial Reference Frame) di una qualsiasi rete regionale italiana nonostante l’intervallo temporale ancora limitato. Da un lato sono state stimate le velocità ITRF (puramente indicative e non ufficiali) delle stazioni RDN e, dall’altro, è stata effettuata una prova di inquadramento di una rete regionale in ITRF, tramite RDN, e si è verificato che non si hanno differenze apprezzabili rispetto all’inquadramento in ITRF, tramite un congruo numero di stazioni IGS/EUREF (International GNSS Service / European REference Frame, SubCommission for Europe dello International Association of Geodesy).

Kinematic description of the rupture from strong motion data: strategies for a robust inversion

We present a non linear technique to invert strong motion records with the aim of obtaining the final slip and rupture velocity distributions on the fault plane. In this thesis, the ground motion simulation is obtained evaluating the representation integral in the frequency. The Green’s tractions are computed using the discrete wave-number integration technique that provides the full wave-field in a 1D layered propagation medium. The representation integral is computed through a finite elements technique, based on a Delaunay’s triangulation on the fault plane. The rupture velocity is defined on a coarser regular grid and rupture times are computed by integration of the eikonal equation. For the inversion, the slip distribution is parameterized by 2D overlapping Gaussian functions, which can easily relate the spectrum of the possible solutions with the minimum resolvable wavelength, related to source-station distribution and data processing. The inverse problem is solved by a two-step procedure aimed at separating the computation of the rupture velocity from the evaluation of the slip distribution, the latter being a linear problem, when the rupture velocity is fixed. The non-linear step is solved by optimization of an L2 misfit function between synthetic and real seismograms, and solution is searched by the use of the Neighbourhood Algorithm. The conjugate gradient method is used to solve the linear step instead. The developed methodology has been applied to the M7.2, Iwate Nairiku Miyagi, Japan, earthquake. The estimated magnitude seismic moment is 2.6326 dyne∙cm that corresponds to a moment magnitude MW 6.9 while the mean the rupture velocity is 2.0 km/s. A large slip patch extends from the hypocenter to the southern shallow part of the fault plane. A second relatively large slip patch is found in the northern shallow part. Finally, we gave a quantitative estimation of errors associates with the parameters.

Data Mining in Clinical Practice for the Quantification of Motor Impairment in Parkinson's Disease

Advances in biomedical signal acquisition systems for motion analysis have led to lowcost and ubiquitous wearable sensors which can be used to record movement data in different settings. This implies the potential availability of large amounts of quantitative data. It is then crucial to identify and to extract the information of clinical relevance from the large amount of available data. This quantitative and objective information can be an important aid for clinical decision making. Data mining is the process of discovering such information in databases through data processing, selection of informative data, and identification of relevant patterns. The databases considered in this thesis store motion data from wearable sensors (specifically accelerometers) and clinical information (clinical data, scores, tests). The main goal of this thesis is to develop data mining tools which can provide quantitative information to the clinician in the field of movement disorders. This thesis will focus on motor impairment in Parkinson's disease (PD). Different databases related to Parkinson subjects in different stages of the disease were considered for this thesis. Each database is characterized by the data recorded during a specific motor task performed by different groups of subjects. The data mining techniques that were used in this thesis are feature selection (a technique which was used to find relevant information and to discard useless or redundant data), classification, clustering, and regression. The aims were to identify high risk subjects for PD, characterize the differences between early PD subjects and healthy ones, characterize PD subtypes and automatically assess the severity of symptoms in the home setting.

Ultra-low power WSNs: distributed signal processing and dynamic resource management

This thesis presents several data processing and compression techniques capable of addressing the strict requirements of wireless sensor networks. After introducing a general overview of sensor networks, the energy problem is introduced, dividing the different energy reduction approaches according to the different subsystem they try to optimize. To manage the complexity brought by these techniques, a quick overview of the most common middlewares for WSNs is given, describing in detail SPINE2, a framework for data processing in the node environment. The focus is then shifted on the in-network aggregation techniques, used to reduce data sent by the network nodes trying to prolong the network lifetime as long as possible. Among the several techniques, the most promising approach is the Compressive Sensing (CS). To investigate this technique, a practical implementation of the algorithm is compared against a simpler aggregation scheme, deriving a mixed algorithm able to successfully reduce the power consumption. The analysis moves from compression implemented on single nodes to CS for signal ensembles, trying to exploit the correlations among sensors and nodes to improve compression and reconstruction quality. The two main techniques for signal ensembles, Distributed CS (DCS) and Kronecker CS (KCS), are introduced and compared against a common set of data gathered by real deployments. The best trade-off between reconstruction quality and power consumption is then investigated. The usage of CS is also addressed when the signal of interest is sampled at a Sub-Nyquist rate, evaluating the reconstruction performance. Finally the group sparsity CS (GS-CS) is compared to another well-known technique for reconstruction of signals from an highly sub-sampled version. These two frameworks are compared again against a real data-set and an insightful analysis of the trade-off between reconstruction quality and lifetime is given.