Search results for " statistics"
showing 10 items of 1891 documents
Feature selection using support vector machines and bootstrap methods for ventricular fibrillation detection
2012
Early detection of ventricular fibrillation (VF) is crucial for the success of the defibrillation therapy in automatic devices. A high number of detectors have been proposed based on temporal, spectral, and time-frequency parameters extracted from the surface electrocardiogram (ECG), showing always a limited performance. The combination ECG parameters on different domain (time, frequency, and time-frequency) using machine learning algorithms has been used to improve detection efficiency. However, the potential utilization of a wide number of parameters benefiting machine learning schemes has raised the need of efficient feature selection (FS) procedures. In this study, we propose a novel FS…
Maximizing reading: pattern analysis to describe points of gaze
2006
As people read texts, their points of gaze can be described either as a sequence or as a pattern of dots. If reading fixations are visualized as a pattern and their duration is graphically attributed to the 3 rd dimension, image processing techniques can be employed to describe individual reading styles. Two reader groups of text editors and of University students were matching according to parametric tests. Yet they appeared to have marked inter-subject variability of fixation distribution when individual cases were considered. To illustrate this, we applied a simple "Coulomb law" - like model that takes both fixation duration and spacing into account. Further the image entropy filter was …
Hidden attractors on one path : Glukhovsky-Dolzhansky, Lorenz, and Rabinovich systems
2017
In this report, by the numerical continuation method we visualize and connect hidden chaotic sets in the Glukhovsky-Dolzhansky, Lorenz and Rabinovich systems using a certain path in the parameter space of a Lorenz-like system.
A Parallel Approach to HRTF Approximation and Interpolation Based on a Parametric Filter Model
2017
[EN] Spatial audio-rendering techniques using head-related transfer functions (HRTFs) are currently used in many different contexts such as immersive teleconferencing systems, gaming, or 3-D audio reproduction. Since all these applications usually involve real-time constraints, efficient processing structures for HRTF modeling and interpolation are necessary for providing real-time binaural audio solutions. This letter presents a parametric parallel model that allows us to perform HRTF filtering and interpolation efficiently from an input HRTF dataset. The resulting model, which is an adaptation from a recently proposed modeling technique, not only reduces the size of HRTF datasets signific…
Mixed-aspect fractal surfaces
2013
In order to provide accurate tools to model original surfaces in a Computer Aided Geometric Design context, we develop a formalism based on iterated function systems. This model enables us to represent both smooth and fractal free-form curves and surfaces. But, because of the self-similarity property underlying the iterated function systems, curves and surfaces can only have homogeneous roughness. The aim of our work was to elaborate a method to build parametric shapes (curves, surfaces, ...) with a non-uniform local aspect: every point is assigned a ''geometric texture'' that evolves continuously from a smooth to a rough aspect. The principle is to blend shapes with uniform aspects to defi…
Deep-Learning-Enabled Fast Optical Identification and Characterization of Two-Dimensional Materials
2019
Advanced microscopy and/or spectroscopy tools play indispensable role in nanoscience and nanotechnology research, as it provides rich information about the growth mechanism, chemical compositions, crystallography, and other important physical and chemical properties. However, the interpretation of imaging data heavily relies on the "intuition" of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, we use the optical characterization of two-dimensional (2D) materials as a case stu…
Percolation on correlated random networks
2011
We consider a class of random, weighted networks, obtained through a redefinition of patterns in an Hopfield-like model and, by performing percolation processes, we get information about topology and resilience properties of the networks themselves. Given the weighted nature of the graphs, different kinds of bond percolation can be studied: stochastic (deleting links randomly) and deterministic (deleting links based on rank weights), each mimicking a different physical process. The evolution of the network is accordingly different, as evidenced by the behavior of the largest component size and of the distribution of cluster sizes. In particular, we can derive that weak ties are crucial in o…
Organization and evolution of synthetic idiotypic networks
2012
We introduce a class of weighted graphs whose properties are meant to mimic the topological features of idiotypic networks, namely the interaction networks involving the B-core of the immune system. Each node is endowed with a bit-string representing the idiotypic specificity of the corresponding B cell and a proper distance between any couple of bit-strings provides the coupling strength between the two nodes. We show that a biased distribution of the entries in bit-strings can yield fringes in the (weighted) degree distribution, small-worlds features, and scaling laws, in agreement with experimental findings. We also investigate the role of ageing, thought of as a progressive increase in …
"Table 2" of "Measurement of the shape of the boson transverse momentum distribution in p anti-p ---> Z / gamma* ---> e+ e- + X events produced…
2008
Correlation matrix for all rapidity Z bosons for the 12 bins used for PT < 30.
Experiments on the dynamics of the Bose–Einstein condensate at finite temperatures
2009
This paper presents the results of our recent experiments on the finite-temperature Bose?Einstein condensate of 87Rb atoms in a magnetic trap, and is devoted to the study of the hydrodynamic properties and dynamics of an ultra-cold atomic gas near the critical temperature. Measurements of the aspect ratio of an expanding atomic cloud allow for verification of the condensate models and study of the interaction between condensed and non-condensed fractions of a finite-temperature sample.