Search results for "Signal Processing"
showing 10 items of 2451 documents
INTEGRAL observations of the peculiar BeX System SAX J2103.5+4545
2004
We present an INTEGRAL data analysis of the X-ray transient \object{SAX J2103.5+4545} during two outbursts detected in December 2002. The INTEGRAL coordinates and error circle agree with the position of the recently proposed optical counterpart. A power-law plus cut-off model provided a good fit to the 4-150 keV spectrum yielding a photon index of 1.0+-0.1, a cut-off energy E_cut=7.6+-2.0 keV and a folding energy E_fold=30.9+-2.5 keV. The X-ray luminosity in the 4-150 keV energy range was found to be 6.0x10^36 erg/s, assuming a distance of 6.5 kpc. This luminosity, together with the derived photon index, indicate that the source is in a bright state. A 354.9$+-0.5 second pulse period is mea…
Accuracy assessment and position correction for low-cost non-differential GPS as applied on an industrial peat bog
1999
A low-cost, non-differentially corrected hand-held GPS receiver was tested on an industrial peat production bog. A correction procedure (‘pseudo-differential correction’) was derived that corrected data points to the nearest position on a line defining the centre of each 15-m wide field. The result was a corrected log of track points for each field for all points lying along the field. It was found that the mean orthogonal distance from a field centreline was linearly correlated with mean uncorrected GPS data error (r 2 0.99) such that as GPS error increased so the accuracy obtained by correction decreased. For a signal with a mean uncorrected error of 30 m it was possible to reduce the err…
A comparison among different techniques for human ERG signals processing and classification
2014
A comparison among different techniques for human ERG signals processing and classification ( Articles not published yet, but available online Article in press About articles in press (opens in a new window) ) Barraco, R.a, Persano Adorno, D.a , Brai, M.a, Tranchina, L.b a Dipartimento di Fisica e Chimica, Università di Palermo and CNISM, Viale delle Scienze, Ed. 18, I-90128 Palermo, Italy b Laboratorio di Fisica e Tecnologie Relative - UniNetLab, Università di Palermo, Viale delle Scienze, Ed. 18, I-90128 Palermo, Italy Abstract Feature detection in biomedical signals is crucial for deepening our knowledge about the involved physiological processes. To achieve this aim, many analytic appro…
Archetypal analysis: an alternative to clustering for unsupervised texture segmentation
2019
Texture segmentation is one of the main tasks in image applications, specifically in remote sensing, where the objective is to segment high-resolution images of natural landscapes into different cover types. Often the focus is on the selection of discriminant textural features, and although these are really fundamental, there is another part of the process that is also influential, partitioning different homogeneous textures into groups. A methodology based on archetype analysis (AA) of the local textural measurements is proposed. AA seeks the purest textures in the image and it can find the borders between pure textures, as those regions composed of mixtures of several archetypes. The prop…
Automatic detection and classification of retinal vascular landmarks
2014
The main contribution of this paper is introducing a method to distinguish between different landmarks of the retina: bifurcations and crossings. The methodology may help in differentiating between arteries and veins and is useful in identifying diseases and other special pathologies, too. The method does not need any special skills, thus it can be assimilated to an automatic way for pinpointing landmarks; moreover it gives good responses for very small vessels. A skeletonized representation, taken out from the segmented binary image (obtained through a preprocessing step), is used to identify pixels with three or more neighbors. Then, the junction points are classified into bifurcations or…
Estimation of fibre orientation from digital images
2001
In this paper, estimation of fibre orientation is studied for fibre systems observable as a blurred greyscale image. The estimation method is based on scaled variograms observed along a set of sampling lines in different directions. The parameters of the orientation distribution are obtained numerically. Simulated data are used to study the statistical properties of the method.
Correlation of the highest-energy cosmic rays with nearby extragalactic objects.
2007
Using data collected at the Pierre Auger Observatory during the past 3.7 years, we demonstrated a correlation between the arrival directions of cosmic rays with energy above ~ 6x10^{19} electron volts and the positions of active galactic nuclei (AGN) lying within ~ 75 megaparsecs. We rejected the hypothesis of an isotropic distribution of these cosmic rays with at least a 99% confidence level from a prescribed a priori test. The correlation we observed is compatible with the hypothesis that the highest energy particles originate from nearby extragalactic sources whose flux has not been substantially reduced by interaction with the cosmic background radiation. AGN or objects having a similar…
Remote sensing image segmentation by active queries
2012
Active learning deals with developing methods that select examples that may express data characteristics in a compact way. For remote sensing image segmentation, the selected samples are the most informative pixels in the image so that classifiers trained with reduced active datasets become faster and more robust. Strategies for intelligent sampling have been proposed with model-based heuristics aiming at the search of the most informative pixels to optimize model's performance. Unlike standard methods that concentrate on model optimization, here we propose a method inspired in the cluster assumption that holds in most of the remote sensing data. Starting from a complete hierarchical descri…
A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living
2015
This research aims to describe pattern recognition models for detecting behavioural and health-related changes in a patient who is monitored continuously in an assisted living environment. The early anticipation of anomalies can improve the rate of disease prevention. Here we present different learning techniques for predicting abnormalities and behavioural trends in various user contexts. In this paper we described a Hidden Markov Model based approach for detecting abnormalities in daily activities, a process of identifying irregularity in routine behaviours from statistical histories and an exponential smoothing technique to predict future changes in various vital signs. The outcomes of t…
Adaptive Kernel Learning for Signal Processing
2018
Adaptive filtering is a central topic in digital signal processing (DSP). By applying linear adaptive filtering principles in the kernel feature space, powerful nonlinear adaptive filtering algorithms can be obtained. This chapter introduces the wide topic of adaptive signal processing, and explores the emerging field of kernel adaptive filtering (KAF). In many signal processing applications, the problem of signal estimation is addressed. Probabilistic models have proven to be very useful in this context. The chapter discusses two families of kernel adaptive filters, namely kernel least mean squares (KLMS) and kernel recursive least‐squares (KRLS) algorithms. In order to design a practical …