Search results for "Online"

Assessment of qualitative judgements for conditional events in expert systems

1991

Comprehensive Strategy for Proton Chemical Shift Prediction: Linear Prediction with Nonlinear Corrections

2014

A fast 3D/4D structure-sensitive procedure was developed and assessed for the chemical shift prediction of protons bonded to sp3carbons, which poses the maybe greatest challenge in the NMR spectral parameter prediction. The LPNC (Linear Prediction with Nonlinear Corrections) approach combines three well-established multivariate methods viz. the principal component regression (PCR), the random forest (RF) algorithm, and the k nearest neighbors (kNN) method. The role of RF is to find nonlinear corrections for the PCR predicted shifts, while kNN is used to take full advantage of similar chemical environments. Two basic molecular models were also compared and discussed: in the MC model the desc…

Flexible design of multifocal metalenses based on autofocused Airy beams

2018

Extreme miniaturization of on-demand optical devices such as ultrathin lenses is currently leading to significant advancements in manufacturing novel materials and nanotechnologies. Flexibility and tunability of engineered layouts enable efficient integration of complex photonic modules. In this regard, here we propose an autofocused Airy (AFA)-based metalens that operates, depending on the molded phase profile, as a multifocal focusing lens, which to the best of our knowledge has not been reported before. To do this, we call attention to the fact that the two conjugate focal points of an AFA beam can be brought into real space by applying a proper convex lens phase profile. Considering ful…

Weighted nonlinear correlation for controlled discrimination capability

2002

We recently demonstrated the high discrimination capability as well as the high sensitivity to small intensity variations of the sliced orthogonal nonlinear generalized (SONG) correlation. This nonlinear correlation has a correlation matrix representation. Previous papers considered only the principal diagonal elements of the correlation matrix. We propose using the off-diagonal non-zero elements of the SONG correlation matrix in order to achieve variable discrimination performance and controlled detection adapted to the gray-scale variations. Moreover, we introduce negative coefficients in order to improve the discrimination properties of the SONG correlation. To control the degree of reco…

Problematic internet use prior to and during the COVID-19 pandemic

2021

The health and socio-economic challenges arising from the COVID-19 pandemic have led to greater reliance on the internet to meet basic needs and responsibilities. Greater engagement in online activities may have negative mental and physical health consequences for some vulnerable individuals, particularly under mandatory self-isolation or ‘lockdown’ conditions. The present study investigated whether changes in levels of involvement in online activities during the COVID-19 pandemic (i.e., watching TV series,online sexual activities, video games, social networks, gambling, online shopping, and instant messaging) were associated with problematic internet use, as well as whether certain psychol…

Dynamics of Vertebral Column Observed by Stereovision and Recurrent Neural Network Model

2005

A new non-invasive method for investigation of movement of selected points on the vertebral column is presented. The registration of position of points marked on patient's body is performed by 4 infrared cameras. This experiment enables to reconstruct 3-dimensional trajectories of displacement of marked points. We introduce recurrent neural networks as formal nonlinear dynamical models of each point trajectory. These models are based only on experimental data and are set up of minimal number of parameters. Therefore they are suitable for pattern recognition problems.

Semisupervised kernel orthonormalized partial least squares

2012

This paper presents a semisupervised kernel orthonormalized partial least squares (SS-KOPLS) algorithm for non-linear feature extraction. The proposed method finds projections that minimize the least squares regression error in Hilbert spaces and incorporates the wealth of unlabeled information to deal with small size labeled datasets. The method relies on combining a standard RBF kernel using labeled information, and a generative kernel learned by clustering all available data. The positive definiteness of the kernels is proven, and the structure and information content of the derived kernels is studied. The effectiveness of the proposed method is successfully illustrated in standard UCI d…

Modified LACIF filtering in background disjoint noise

2011

Abstract This work deals with pattern recognition methods based on correlations for images in the presence of noise. We propose a modification of the nonlinear Locally Adaptive Contrast Invariant Filter (LACIF) that yields correlation peaks that are invariant to linear intensity changes of the target but that has some limitations in the presence low variance nonoverlapping background noise. The modification of the filter implies a normalization by a global variance of several distributions. The estimation of the variance distributions is done locally by means of correlations. Experimental results as well as comparisons with the classical matched filter and the common LACIF are given.

Intensity invariant nonlinear correlation filtering in spatially disjoint noise.

2010

We analyze the performance of a nonlinear correlation called the Locally Adaptive Contrast Invariant Filter in the presence of spatially disjoint noise under the peak-to-sidelobe ratio (PSR) metric. We show that the PSR using the nonlinear correlation improves as the disjoint noise intensity increases, whereas, for common linear filtering, it goes to zero. Experimental results as well as comparisons with a classical matched filter are given.

Stochastic seismic analysis of offshore towers

1984

After a brief review of the main problems and the most common analysis methods for offshore structures, a method of analysis for offshore towers submerged in water and subjected to strong earthquake motions is proposed. Nonlinear drag effects as well as random non-stationary seismic excitations are considered by means of a linearization technique based on a particular step-by-step procedure. Using a discrete lumped-mass model, the standard deviations of nodal displacements and velocities are evaluated. The probability of not exceeding a defined threshold of nodal displacements is also computed.