Efficient and low-computational predictive models for spectral sensors

Spectral sensors are a wide class of devices that are extremely useful for detecting essential information of the environment and materials with high degree of selectivity. Recently, they have achieved high degrees of integration and low implementation cost to be suited for fast, small, and non-invasive monitoring systems. However, the useful information is hidden in spectra and it is difficult to decode. So, mathematical algorithms are needed to infer the value of the variables of interest from the acquired data. Between the different families of predictive modeling, Principal Component Analysis and the techniques stemmed from it can provide very good performances, as well as small computational and memory requirements. For these reasons, they allow the implementation of the prediction even in embedded and autonomous devices. In this thesis, I will present 4 practical applications of these algorithms to the prediction of different variables: moisture of soil, moisture of concrete, freshness of anchovies/sardines, and concentration of gasses. In all of these cases, the workflow will be the same. Initially, an acquisition campaign was performed to acquire both spectra and the variables of interest from samples. Then these data are used as input for the creation of the prediction models, to solve both classification and regression problems. From these models, an array of calibration coefficients is derived and used for the implementation of the prediction in an embedded system. The presented results will show that this workflow was successfully applied to very different scientific fields, obtaining autonomous and non-invasive devices able to predict the value of physical parameters of choice from new spectral acquisitions.

Longterm peripheral baroreflex and chemoreflex function after bilateral eversion carotid endarterectomy

Introduction The “eversion” technique for carotid endarterectomy (e-CEA), that involves the transection of the internal carotid artery at the carotid bulb and its eversion over the atherosclerotic plaque, has been associated with an increased risk of postoperative hypertension possibly due to a direct iatrogenic damage to the carotid sinus fibers. The aim of this study is to assess the long-term effect of the e-CEA on arterial baroreflex and peripheral chemoreflex function in humans. Methods A retrospective review was conducted on a prospectively compiled computerized database of 3128 CEAs performed on 2617 patients at our Center between January 2001 and March 2006. During this period, a total of 292 patients who had bilateral carotid stenosis ≥70% at the time of the first admission underwent staged bilateral CEAs. Of these, 93 patients had staged bilateral e-CEAs, 126 staged bilateral s- CEAs and 73 had different procedures on each carotid. CEAs were performed with either the eversion or the standard technique with routine Dacron patching in all cases. The study inclusion criteria were bilateral CEA with the same technique on both sides and an uneventful postoperative course after both procedures. We decided to enroll patients submitted to bilateral e-CEA to eliminate the background noise from contralateral carotid sinus fibers. Exclusion criteria were: age >70 years, diabetes mellitus, chronic pulmonary disease, symptomatic ischemic cardiac disease or medical therapy with b-blockers, cardiac arrhythmia, permanent neurologic deficits or an abnormal preoperative cerebral CT scan, carotid restenosis and previous neck or chest surgery or irradiation. Young and aged-matched healthy subjects were also recruited as controls. Patients were assessed by the 4 standard cardiovascular reflex tests, including Lying-to-standing, Orthostatic hypotension, Deep breathing, and Valsalva Maneuver. Indirect autonomic parameters were assessed with a non-invasive approach based on spectral analysis of EKG RR interval, systolic arterial pressure, and respiration variability, performed with an ad hoc software. From the analysis of these parameters the software provides the estimates of spontaneous baroreflex sensitivity (BRS). The ventilatory response to hypoxia was assessed in patients and controls by means of classic rebreathing tests. Results A total of 29 patients (16 males, age 62.4±8.0 years) were enrolled. Overall, 13 patients had undergone bilateral e-CEA (44.8%) and 16 bilateral s-CEA (55.2%) with a mean interval between the procedures of 62±56 days. No patient showed signs or symptoms of autonomic dysfunction, including labile hypertension, tachycardia, palpitations, headache, inappropriate diaphoresis, pallor or flushing. The results of standard cardiovascular autonomic tests showed no evidence of autonomic dysfunction in any of the enrolled patients. At spectral analysis, a residual baroreflex performance was shown in both patient groups, though reduced, as expected, compared to young controls. Notably, baroreflex function was better maintained in e-CEA, compared to standard CEA. (BRS at rest: young controls 19.93 ± 2.45 msec/mmHg; age-matched controls 7.75 ± 1.24; e-CEA 13.85 ± 5.14; s-CEA 4.93 ± 1.15; ANOVA P=0.001; BRS at stand: young controls 7.83 ± 0.66; age-matched controls 3.71 ± 0.35; e-CEA 7.04 ± 1.99; s-CEA 3.57 ± 1.20; ANOVA P=0.001). In all subjects ventilation (VÝ E) and oximetry data fitted a linear regression model with r values > 0.8. Oneway analysis of variance showed a significantly higher slope both for ΔVE/ΔSaO2 in controls compared with both patient groups which were not different from each other (-1.37 ± 0.33 compared with -0.33±0.08 and -0.29 ±0.13 l/min/%SaO2, p<0.05, Fig.). Similar results were observed for and ΔVE/ΔPetO2 (-0.20 ± 0.1 versus -0.01 ± 0.0 and -0.07 ± 0.02 l/min/mmHg, p<0.05). A regression model using treatment, age, baseline FiCO2 and minimum SaO2 achieved showed only treatment as a significant factor in explaining the variance in minute ventilation (R2= 25%). Conclusions Overall, we demonstrated that bilateral e-CEA does not imply a carotid sinus denervation. As a result of some expected degree of iatrogenic damage, such performance was lower than that of controls. Interestingly though, baroreflex performance appeared better maintained in e-CEA than in s-CEA. This may be related to the changes in the elastic properties of the carotid sinus vascular wall, as the patch is more rigid than the endarterectomized carotid wall that remains in the e-CEA. These data confirmed the safety of CEA irrespective of the surgical technique and have relevant clinical implication in the assessment of the frequent hemodynamic disturbances associated with carotid angioplasty stenting.

NMR, Metabonomics and Molecular Profiles: Applications to the Quality Assessment of Foodstuffs

Nuclear Magnetic Resonance (NMR) is a branch of spectroscopy that is based on the fact that many atomic nuclei may be oriented by a strong magnetic field and will absorb radiofrequency radiation at characteristic frequencies. The parameters that can be measured on the resulting spectral lines (line positions, intensities, line widths, multiplicities and transients in time-dependent experi-ments) can be interpreted in terms of molecular structure, conformation, molecular motion and other rate processes. In this way, high resolution (HR) NMR allows performing qualitative and quantitative analysis of samples in solution, in order to determine the structure of molecules in solution and not only. In the past, high-field NMR spectroscopy has mainly concerned with the elucidation of chemical structure in solution, but today is emerging as a powerful exploratory tool for probing biochemical and physical processes. It represents a versatile tool for the analysis of foods. In literature many NMR studies have been reported on different type of food such as wine, olive oil, coffee, fruit juices, milk, meat, egg, starch granules, flour, etc using different NMR techniques. Traditionally, univariate analytical methods have been used to ex-plore spectroscopic data. This method is useful to measure or to se-lect a single descriptive variable from the whole spectrum and , at the end, only this variable is analyzed. This univariate methods ap-proach, applied to HR-NMR data, lead to different problems due especially to the complexity of an NMR spectrum. In fact, the lat-ter is composed of different signals belonging to different mole-cules, but it is also true that the same molecules can be represented by different signals, generally strongly correlated. The univariate methods, in this case, takes in account only one or a few variables, causing a loss of information. Thus, when dealing with complex samples like foodstuff, univariate analysis of spectra data results not enough powerful. Spectra need to be considered in their wholeness and, for analysing them, it must be taken in consideration the whole data matrix: chemometric methods are designed to treat such multivariate data. Multivariate data analysis is used for a number of distinct, differ-ent purposes and the aims can be divided into three main groups: • data description (explorative data structure modelling of any ge-neric n-dimensional data matrix, PCA for example); • regression and prediction (PLS); • classification and prediction of class belongings for new samples (LDA and PLS-DA and ECVA). The aim of this PhD thesis was to verify the possibility of identify-ing and classifying plants or foodstuffs, in different classes, based on the concerted variation in metabolite levels, detected by NMR spectra and using the multivariate data analysis as a tool to inter-pret NMR information. It is important to underline that the results obtained are useful to point out the metabolic consequences of a specific modification on foodstuffs, avoiding the use of a targeted analysis for the different metabolites. The data analysis is performed by applying chemomet-ric multivariate techniques to the NMR dataset of spectra acquired. The research work presented in this thesis is the result of a three years PhD study. This thesis reports the main results obtained from these two main activities: A1) Evaluation of a data pre-processing system in order to mini-mize unwanted sources of variations, due to different instrumental set up, manual spectra processing and to sample preparations arte-facts; A2) Application of multivariate chemiometric models in data analy-sis.

Spectral-Element and Adjoint 3D Full-Wave Tomography for the Lithosphere of Central Italy

The primary objective of this thesis is to obtain a better understanding of the 3D velocity structure of the lithosphere in central Italy. To this end, I adopted the Spectral-Element Method to perform accurate numerical simulations of the complex wavefields generated by the 2009 Mw 6.3 L’Aquila event and by its foreshocks and aftershocks together with some additional events within our target region. For the mainshock, the source was represented by a finite fault and different models for central Italy, both 1D and 3D, were tested. Surface topography, attenuation and Moho discontinuity were also accounted for. Three-component synthetic waveforms were compared to the corresponding recorded data. The results of these analyses show that 3D models, including all the known structural heterogeneities in the region, are essential to accurately reproduce waveform propagation. They allow to capture features of the seismograms, mainly related to topography or to low wavespeed areas, and, combined with a finite fault model, result into a favorable match between data and synthetics for frequencies up to ~0.5 Hz. We also obtained peak ground velocity maps, that provide valuable information for seismic hazard assessment. The remaining differences between data and synthetics led us to take advantage of SEM combined with an adjoint method to iteratively improve the available 3D structure model for central Italy. A total of 63 events and 52 stations in the region were considered. We performed five iterations of the tomographic inversion, by calculating the misfit function gradient - necessary for the model update - from adjoint sensitivity kernels, constructed using only two simulations for each event. Our last updated model features a reduced traveltime misfit function and improved agreement between data and synthetics, although further iterations, as well as refined source solutions, are necessary to obtain a new reference 3D model for central Italy tomography.

New techniques for the remote sensing of foliar nitrogen concentration in forest ecosystems.

During my Doctoral study I researched about the remote detection of canopy N concentration in forest stands, its potentials and problems, under many overlapping perspectives. The study consisted of three parts. In S. Rossore 2000 dataset analysis, I tested regressions between N concentration and NIR reflectances derived from different sources (field samples, airborne and satellite sensors). The analysis was further expanded using a larger dataset acquired in year 2009 as part of a new campaign funded by the ESA. In both cases, a good correlation was observed between Landsat NIR, using both TM (2009) and ETM+ (2000) imagery, and N concentration measured by a CHN elemental analyzer. Concerning airborne sensors I did not obtain the same good results, mainly because of the large FOV of the two instruments, and to the anisotropy of vegetation reflectance. We also tested the relation between ground based ASD measures and nitrogen concentration, obtaining really good results. Thus, I decided to expand my study to the regional level, focusing only on field and satellite measures. I analyzed a large dataset for the whole of Catalonia, Spain; MODIS imagery was used, in consideration of its spectral characteristics and despite its rather poor spatial resolution. Also in this case a regression between nitrogen concentration and reflectances was found, but not so good as in previous experiences. Moreover, vegetation type was found to play an important role in the observed relationship. We concluded that MODIS is not the most suitable satellite sensor in realities like Italy and Catalonia, which present a patchy and inhomogeneous vegetation cover; so it could be utilized for the parameterization of eco-physiological and biogeochemical models, but not for really local nitrogen estimate. Thus multispectral sensors similar to Landsat Thematic Mapper, with better spatial resolution, could be the most appropriate sensors to estimate N concentration.

Seismic sequences analysis for estimation of earthquake source parameters: corner frequency, stress drop, and seismic moment observations

The present study has been carried out with the following objectives: i) To investigate the attributes of source parameters of local and regional earthquakes; ii) To estimate, as accurately as possible, M0, fc, Δσ and their standard errors to infer their relationship with source size; iii) To quantify high-frequency earthquake ground motion and to study the source scaling. This work is based on observational data of micro, small and moderate -earthquakes for three selected seismic sequences, namely Parkfield (CA, USA), Maule (Chile) and Ferrara (Italy). For the Parkfield seismic sequence (CA), a data set of 757 (42 clusters) repeating micro-earthquakes (0 ≤ MW ≤ 2), collected using borehole High Resolution Seismic Network (HRSN), have been analyzed and interpreted. We used the coda methodology to compute spectral ratios to obtain accurate values of fc , Δσ, and M0 for three target clusters (San Francisco, Los Angeles, and Hawaii) of our data. We also performed a general regression on peak ground velocities to obtain reliable seismic spectra of all earthquakes. For the Maule seismic sequence, a data set of 172 aftershocks of the 2010 MW 8.8 earthquake (3.7 ≤ MW ≤ 6.2), recorded by more than 100 temporary broadband stations, have been analyzed and interpreted to quantify high-frequency earthquake ground motion in this subduction zone. We completely calibrated the excitation and attenuation of the ground motion in Central Chile. For the Ferrara sequence, we calculated moment tensor solutions for 20 events from MW 5.63 (the largest main event occurred on May 20 2012), down to MW 3.2 by a 1-D velocity model for the crust beneath the Pianura Padana, using all the geophysical and geological information available for the area. The PADANIA model allowed a numerical study on the characteristics of the ground motion in the thick sediments of the flood plain.

Spectral estimates and preconditioning for saddle point systems arising from optimization problems

In this thesis, we consider the problem of solving large and sparse linear systems of saddle point type stemming from optimization problems. The focus of the thesis is on iterative methods, and new preconditioning srategies are proposed, along with novel spectral estimtates for the matrices involved.