Search results for "Principal Component Analysis"
showing 10 items of 486 documents
Air quality assessment via functional principal component analysis
2009
The knowledge of the global urban air quality situation represents the first step to face air pollution issues. For the last decades many urban areas can rely on a monitoring network, recording hourly data for the main pollutants. Such data need to be aggregated according to different dimensions, such as time, space and type of pollutant, in order to provide a synthetic air quality index which takes into account interactions among pollutants and correlation among monitoring sites.This paper focuses on Functional Principal Component techniques for the statistical analysis of a set of environmental data x(spt), where s stands for the monitoring site, p for the pollutant and t for time, usuall…
Principal component analysis for the selection of variables in the application of the H-point and generalised H-point standard addition method
2000
The present paper deals with the selection of variables for the H-point and generalised H-point standard additions methods (HPSAM and GHPSAM, respectively). Both methods are applied for the resolution of spectroscopic interfered signals in the UV-vis range. The HPSAM is a suitable method for the resolution of binary and ternary mixtures when the interferent is known. The GHPSAM is applied for the resolution of samples that contain unknown interferents. In this paper, a method based on the study of a principal components analysis (PCA) for the selection of variables for the HPSAM and GHPSAM is proposed. The PCA results show the isolation of the analyte signal from the sample signal, achieved…
Machine Learning-Based Classification of Vector Vortex Beams.
2020
Structured light is attracting significant attention for its diverse applications in both classical and quantum optics. The so-called vector vortex beams display peculiar properties in both contexts due to the non-trivial correlations between optical polarization and orbital angular momentum. Here we demonstrate a new, flexible experimental approach to the classification of vortex vector beams. We first describe a platform for generating arbitrary complex vector vortex beams inspired to photonic quantum walks. We then exploit recent machine learning methods -- namely convolutional neural networks and principal component analysis -- to recognize and classify specific polarization patterns. O…
On the Left Ventricular Remodeling of Patients with Stenotic Aortic Valve: A Statistical Shape Analysis
2021
The left ventricle (LV) constantly changes its shape and function as a response to pathological conditions, and this process is known as remodeling. In the presence of aortic stenosis (AS), the degenerative process is not limited to the aortic valve but also involves the remodeling of LV. Statistical shape analysis (SSA) offers a powerful tool for the visualization and quantification of the geometrical and functional patterns of any anatomic changes. In this paper, a SSA method was developed to determine shape descriptors of the LV under different degrees of AS and thus to shed light on the mechanistic link between shape and function. A total of n=86 patients underwent computed tomography (…
Statistical shape analysis of ascending thoracic aortic aneurysm: Correlation between shape and biomechanical descriptors
2020
An ascending thoracic aortic aneurysm (ATAA) is a heterogeneous disease showing different patterns of aortic dilatation and valve morphologies, each with distinct clinical course. This study aimed to explore the aortic morphology and the associations between shape and function in a population of ATAA, while further assessing novel risk models of aortic surgery not based on aortic size. Shape variability of n = 106 patients with ATAA and different valve morphologies (i.e., bicuspid versus tricuspid aortic valve) was estimated by statistical shape analysis (SSA) to compute a mean aortic shape and its deformation. Once the computational atlas was built, principal component analysis (PCA) allow…
Exploring automatic grouping procedures in ceramic petrology
2004
Although a small number of studies have attempted to introduce automatic grouping procedures into thin section petrography of archaeological ceramics, the majority of studies continue to be carried out by non-automatic means. Although such an approach with the single observer grouping samples has a number of advantages, it is problematic when dealing with large numbers of samples. This paper aims to explore different coding systems and statistical analyses for grouping ceramic thin sections. In the example discussed a number of variables are defined, codified and analysed by correspondence analysis, classical multidimensional scaling, non-metric isotonic multidimensional scaling and Sammon …
Mosaic floors of roman Villa del Casale: Principal component analysis on spectrophotometric and colorimetric data
2013
Abstract Spectrophotometric and colorimetric data obtained during a measurement campaign aimed at supporting the Roman "Villa del Casale" (Piazza Armerina, Sicily, Italy) conservation activities, are presented. Special attention was paid to the possible variation of the chromatic coordinates, possibly due to the interventions of cleaning, consolidation, and protection. Data have been analyzed by the Principal Component Analysis (PCA) statistical technique, with the attempt to investigate its role in data variability reduction and verify its effectiveness in interpreting the phenomena occurring on the mosaic surface of the Villa, through grouping the observations into homogenous clusters. Ef…
Biological mineral content in Iberian skeletal cremains for control of diagenetic factors employing multivariate statistics
2013
Abstract The aim of this study was to define a strategy for a correct selection of bone samples by employing inductively coupled plasma optical emission spectroscopy (ICP-OES) for reconstructing the biological mineral content in bones through the determination of major elements, trace elements and Rare Earth Elements (REE, lanthanides) in skeletal cremains of ancient Iberians (III–II B.C), discovered in the Necropolis of Corral de Saus (Moixent, Valencia) between 1972 and 1979. The biological mineral content was determined taking into account diagenetic factors. A control method for a better reading of results was applied. To explore large geochemical datasets and to reduce the number of va…
Using machine learning to disentangle LHC signatures of Dark Matter candidates
2019
We study the prospects of characterising Dark Matter at colliders using Machine Learning (ML) techniques. We focus on the monojet and missing transverse energy (MET) channel and propose a set of benchmark models for the study: a typical WIMP Dark Matter candidate in the form of a SUSY neutralino, a pseudo-Goldstone impostor in the shape of an Axion-Like Particle, and a light Dark Matter impostor whose interactions are mediated by a heavy particle. All these benchmarks are tensioned against each other, and against the main SM background ($Z$+jets). Our analysis uses both the leading-order kinematic features as well as the information of an additional hard jet. We explore different representa…