Search results for "NETWORK"
showing 10 items of 7718 documents
Information – theoretic characterization of concurrent activity of neural spike trains
2021
The analysis of massively parallel spike train recordings facilitates investigation of communications and synchronization in neural networks. In this work we develop and evaluate a measure of concurrent neural activity, which is based on intrinsic firing properties of the recorded neural units. An overall single neuron activity is unfolded in time and decomposed into working and non-firing state, providing a coarse, binary representation of the neurons functional state. We propose a modified measure of mutual information to reflect the degree of simultaneous activation and concurrency in neural firing patterns. The measure is shown to be sensitive to both correlations and anti-correlations,…
Graphical Schemes Designed to Display and Study the Long-term Variations of Schumann Resonance
2019
This work proposes and illustrates a graphical approach aimed at studying a wide range of features of the ELF horizontal magnetic field signal recorded at the Sierra Nevada station (Spain). In addition to the traditional long-term variations in the parameters of the first three Schumann resonances (their amplitudes, central frequencies and widths), many other properties such as the saturations of the magnetometers, anomalous values for the parameters or spectra with any kind of particularities are taken into consideration in this work. These features can provide us with complementary information about the long-term variation of Schumann resonances, give an estimation of the extent up to whi…
Progressive transmission of secured images with authentication using decompositions into monovariate functions
2014
International audience; We propose a progressive transmission approach of an image authenticated using an overlapping subimage that can be removed to restore the original image. Our approach is different from most visible water- marking approaches that allow one to later remove the watermark, because the mark is not directly introduced in the two-dimensional image space. Instead, it is rather applied to an equivalent monovariate representation of the image. Precisely, the approach is based on our progressive transmission approach that relies on a modified Kolmogorov spline network, and therefore inherits its advantages: resilience to packet losses during transmis- sion and support of hetero…
Bluetooth-3G wireless transmission system for neural signal telemetry
2007
In this contribution a wireless transmission system for neural signals is developed. This system includes data compression algorithms at the information source, namely neural signals recorded by micro-electrode arrays. The signals are transmitted over Bluetooth to a mobile device that, without any processing or storing, retransmits it over 3G to a remote server where signal post-processing and analysis is performed. The overall transmission rate of the system is limited by the Bluetooth link between the information source and the mobile phone, as well as by the limited processing capabilities of the mobile device and also by the 3G-link. Data compression allows the transmission of up to 7 n…
Deep learning algorithms for gravitational waves core-collapse supernova detection
2021
The detection of gravitational waves from core-collapse supernova (CCSN) explosions is a challenging task, yet to be achieved, in which it is key the connection between multiple messengers, including neutrinos and electromagnetic signals. In this work, we present a method for detecting these kind of signals based on machine learning techniques. We tested its robustness by injecting signals in the real noise data taken by the Advanced LIGO-Virgo network during the second observation run, O2. We trained three newly developed convolutional neural networks using time-frequency images corresponding to injections of simulated phenomenological signals, which mimic the waveforms obtained in 3D nume…
Energy balance in single exposure multispectral sensors
2013
International audience; Recent simulations of multispectral sensors are based on a simple Gaussian model, which includes filters transmittance and substrate absorption. In this paper we want to make the distinction between these two layers. We discuss the balance of energy by channel in multispectral solid state sensors and propose an updated simple Gaussian model to simulate multispectral sensors. Results are based on simulation of typical sensor configurations.
On-line tools for microscopic and macroscopic monitoring of microwave processing
2007
International audience; Direct monitoring of temperature, chemistry and microstructure is required to understand microwave heating in more detail, in order to fully exploit the unique features this non-equilibrium processing method can offer. In this paper, we show first that microwave radiometry can be used to follow volumetrically the thermal trajectory of microwave-heated aluminium powder. In-situ Raman spectroscopy is then shown to evidence thermal gradients between diamond and silicon grains in a binary powder mixture. Finally, perspectives and preliminary results of microstructural analysis obtained from X-ray microtomography are presented.
Structural and dynamical properties of sodium silicate melts: An investigation by molecular dynamics computer simulation
2001
We present the results of large scale computer simulations in which we investigate the static and dynamic properties of sodium disilicate and sodium trisilicate melts. We study in detail the static properties of these systems, namely the coordination numbers, the temperature dependence of the Q^(n) species and the static structure factor, and compare them with experiments. We show that the structure is described by a partially destroyed tetrahedral SiO_4 network and the homogeneously distributed sodium atoms which are surrounded on average by 16 silicon and other sodium atoms as nearest neighbors. We compare the diffusion of the ions in the sodium silicate systems with that in pure silica a…
Generalized singly-implicit Runge-Kutta methods with arbitrary knots
1985
The aim of this paper is to derive Butcher's generalization of singly-implicit methods without restrictions on the knots. Our analysis yields explicit computable expressions for the similarity transformations involved which allow the efficient implementation of the first phase of the method, i.e. the solution of the nonlinear equations. Furthermore, simple formulas for the second phase of the method, i.e. computation of the approximations at the next nodal point, are established. Finally, the matrix which governs the stability of the method is studied.
Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images.
2021
Chest computed tomography (CT) imaging has become indispensable for staging and managing coronavirus disease 2019 (COVID-19), and current evaluation of anomalies/abnormalities associated with COVID-19 has been performed majorly by the visual score. The development of automated methods for quantifying COVID-19 abnormalities in these CT images is invaluable to clinicians. The hallmark of COVID-19 in chest CT images is the presence of ground-glass opacities in the lung region, which are tedious to segment manually. We propose anamorphic depth embedding-based lightweight CNN, called Anam-Net, to segment anomalies in COVID-19 chest CT images. The proposed Anam-Net has 7.8 times fewer parameters …