3,347 research outputs found

    Improving the performance of translation wavelet transform using BMICA

    Get PDF
    Research has shown Wavelet Transform to be one of the best methods for denoising biosignals. Translation-Invariant form of this method has been found to be the best performance. In this paper however we utilize this method and merger with our newly created Independent Component Analysis method – BMICA. Different EEG signals are used to verify the method within the MATLAB environment. Results are then compared with those of the actual Translation-Invariant algorithm and evaluated using the performance measures Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Signal to Interference Ratio (SIR). Experiments revealed that the BMICA Translation-Invariant Wavelet Transform out performed in all four measures. This indicates that it performed superior to the basic Translation- Invariant Wavelet Transform algorithm producing cleaner EEG signals which can influence diagnosis as well as clinical studies of the brain

    BMICA-independent component analysis based on B-spline mutual information estimator

    Get PDF
    The information theoretic concept of mutual information provides a general framework to evaluate dependencies between variables. Its estimation however using B-Spline has not been used before in creating an approach for Independent Component Analysis. In this paper we present a B-Spline estimator for mutual information to find the independent components in mixed signals. Tested using electroencephalography (EEG) signals the resulting BMICA (B-Spline Mutual Information Independent Component Analysis) exhibits better performance than the standard Independent Component Analysis algorithms of FastICA, JADE, SOBI and EFICA in similar simulations. BMICA was found to be also more reliable than the 'renown' FastICA

    Evolution of mechanisms and behaviour important for pain

    Get PDF
    Our understanding of the biology of pain is limited by our ignorance about its evolution. We know little about how states in other species showing various degrees of apparent similarity to human pain states are related to human pain, or how the mechanisms essential for pain-related states evolved. Nevertheless, insights into the evolution of mechanisms and behaviour important for pain are beginning to emerge from wide-ranging investigations of cellular mechanisms and behavioural responses linked to nociceptor activation, tissue injury, inflammation and the environmental context of these responses in diverse species. In February 2019, an unprecedented meeting on the evolution of pain hosted by the Royal Society brought together scientists from disparate fields who investigate nociception and pain-related behaviour in crustaceans, insects, leeches, gastropod and cephalopod molluscs, fish and mammals (primarily rodents and humans). Here, we identify evolutionary themes that connect these research efforts, including adaptive and maladaptive features of pain-related behavioural and neuronal alterations-some of which are quite general, and some that may apply primarily to humans. We also highlight major questions, including how pain should be defined, that need to be answered as we seek to understand the evolution of pain. This article is part of the Theo Murphy meeting issue 'Evolution of mechanisms and behaviour important for pain'

    Toroidal prefactorization algebras associated to holomorphic fibrations and a relationship to vertex algebras

    Get PDF
    Let XX be a complex manifold, π:EX\pi: E \rightarrow X a locally trivial holomorphic fibration with fiber FF, and g\mathfrak{g} a Lie algebra with an invariant symmetric form. We associate to this data a holomorphic prefactorization algebra Fg,π\mathcal{F}_{\mathfrak{g}, \pi} on XX in the formalism of Costello-Gwilliam. When X=CX=\mathbb{C}, g\mathfrak{g} is simple, and FF is a smooth affine variety, we extract from Fg,π\mathcal{F}_{\mathfrak{g}, \pi} a vertex algebra which is a vacuum module for the universal central extension of the Lie algebra gH0(F,O)[z,z1]\mathfrak{g} \otimes H^{0}(F, \mathcal{O})[z,z^{-1}]. As a special case, when FF is an algebraic torus (C)n(\mathbb{C}^{*})^n, we obtain a vertex algebra naturally associated to an (n+1)(n+1)--toroidal algebra, generalizing the affine vacuum module

    The Effects of Postpartum Depression on Children\u27s Social Development

    Get PDF
    The increased incidence of postpartum depression has had significant effects on children’s social development. The purpose of this systematic review is to bring attention to the growing problem in such a vulnerable population. In addition, it was designed to shed light on the lack of research in this area of healthcare. The methods used to conduct the study include various peer reviewed, scholarly and evidenced based articles from databases such as Academic Search Complete, PsycNet, and Pubmed. Each article has been critically evaluated based on the following guidelines: a population group of children under the age of four, specifically maternal postpartum depression rather than paternal, and studies focused on childhood social development. The general consensus of the twenty articles conclude that maternal postpartum depression disrupts the social development of children. Specifically, decreased levels of attachment have been a common trend along with a developmental delay of communication. Based on the evidence collected during the systematic review future evidence-based practice should involve more rigorous screening of the mother child dyad in relation to promotion of mental health. How are children internationally, from birth to four years old, impacted by postpartum depression in relation to social development? Keywords: postpartum depression, development, social development, cognitive development, pediatrics, mental health, infan

    A Cascade Neural Network Architecture investigating Surface Plasmon Polaritons propagation for thin metals in OpenMP

    Full text link
    Surface plasmon polaritons (SPPs) confined along metal-dielectric interface have attracted a relevant interest in the area of ultracompact photonic circuits, photovoltaic devices and other applications due to their strong field confinement and enhancement. This paper investigates a novel cascade neural network (NN) architecture to find the dependance of metal thickness on the SPP propagation. Additionally, a novel training procedure for the proposed cascade NN has been developed using an OpenMP-based framework, thus greatly reducing training time. The performed experiments confirm the effectiveness of the proposed NN architecture for the problem at hand
    corecore