Search results for "Time delay"
showing 10 items of 72 documents
Dynamic Pattern Recognition in Sport by Means of Artificial Neural Networks
2008
Behavioural processes like those in sports, motor activities or rehabilitation are often the object of optimization methods. Such processes are often characterized by a complex structure. Measurements considering them may produce a huge amount of data. It is an interesting challenge not only to store these data, but also to transform them into useful information. Artificial Neural Networks turn out to be an appropriate tool to transform abstract numbers into informative patterns that help to understand complex behavioural phenomena. The contribution presents some basic ideas of neural network approaches and several examples of application. The aim is to give an impression of how neural meth…
Neural Networks in ECG Classification
2011
In this chapter, we review the vast field of application of artificial neural networks in cardiac pathology discrimination based on electrocardiographic signals. We discuss advantages and drawbacks of neural and adaptive systems in cardiovascular medicine and catch a glimpse of forthcoming developments in machine learning models for the real clinical environment. Some problems are identified in the learning tasks of beat detection, feature selection/extraction, and classification, and some proposals and suggestions are given to alleviate the problems of interpretability, overfitting, and adaptation. These have become important problems in recent years and will surely constitute the basis of…
Design environment for hardware generation of SLFF neural network topologies with ELM training capability
2015
Extreme Learning Machine (ELM) is a noniterative training method suited for Single Layer Feed Forward Neural Networks (SLFF-NN). Typically, a hardware neural network is trained before implementation in order to avoid additional on-chip occupation, delay and performance degradation. However, ELM provides fixed-time learning capability and simplifies the process of re-training a neural network once implemented in hardware. This is an important issue in many applications where input data are continuously changing and a new training process must be launched very often, providing self-adaptation. This work describes a general SLFF-NN design environment to assist in the definition of neural netwo…
The nuclear structure of 229Th
2002
Abstract The γ -rays following the β − decay of 229 Ac have been investigated by means of γ -ray singles and γγ -coincidence measurements using Ge detectors. Multipolarities of 40 transitions in 229 Th have been established by measuring conversion electrons with a mini-orange electron spectrometer. The half-lives of the 146.35, 164.53 and 261.96 keV levels have been measured using the advanced time delayed βγγ (t) method. The low-lying states in 229 Th and observed transition rates have been interpreted within the quasiparticle–phonon model with inclusion of Coriolis coupling. Two octupole correlated parity partner bands, with K π =5/2 ± and K π =3/2 ± , were identified in 229 Th.
SU-E-T-343: Valencia Applicator Commissioning Using a Micro-Chamber Array
2014
Purpose: In the commissioning and QA of surface isotope-based applicators, source-indexer distance (SID) has a great influence in the flatness, symmetry and output. To these purposes, methods described in the literature are the use of a special insert at the entrance of dwell chamber or radiochromic films. Here we present the experience with a micro-chamber array to perform the commissioning and QA of Valencia applicators. Methods: Valencia applicators have been used, the classic and the new extra-shielded version. A micro-chamber array has been employed, 1000 SRS (PTW), with 977 liquid filled, 2.3×2.3×0.5 mm3 sized ion chambers covering 11×11 cm2, which spacing is 2.5 mm in the central 5.5…
Size of the accretion disk in the gravitationally lensed quasar SDSS J1004+4112 from the statistics of microlensing magnifications
2016
We present eight monitoring seasons of the four brightest images of the gravitational lens SDSS J1004+4112 observed between December 2003 and October 2010. Using measured time delays for the images A, B and C and the model predicted time delay for image D we have removed the intrinsic quasar variability, finding microlensing events of about 0.5 and 0.7 mag of amplitude in the images C and D. From the statistics of microlensing amplitudes in images A, C, and D, we have inferred the half-light radius (at {\lambda} rest = 2407 {\AA}) for the accretion disk using two different methods, $R_{1/2}=8.7^{+18.5}_{-5.5} \sqrt{M/0.3 M_\odot}$ (histograms product) and $R_{1/2} = 4.2^{+3.2}_{-2.2} \sqrt{…
A NEURAL NETWORK PRIMER
1994
Neural networks are composed of basic units somewhat analogous to neurons. These units are linked to each other by connections whose strength is modifiable as a result of a learning process or algorithm. Each of these units integrates independently (in paral lel) the information provided by its synapses in order to evaluate its state of activation. The unit response is then a linear or nonlinear function of its activation. Linear algebra concepts are used, in general, to analyze linear units, with eigenvectors and eigenvalues being the core concepts involved. This analysis makes clear the strong similarity between linear neural networks and the general linear model developed by statisticia…
Frequency-Sliding Generalized Cross-Correlation: A Sub-Band Time Delay Estimation Approach
2020
The generalized cross correlation (GCC) is regarded as the most popular approach for estimating the time difference of arrival (TDOA) between the signals received at two sensors. Time delay estimates are obtained by maximizing the GCC output, where the direct-path delay is usually observed as a prominent peak. Moreover, GCCs play also an important role in steered response power (SRP) localization algorithms, where the SRP functional can be written as an accumulation of the GCCs computed from multiple sensor pairs. Unfortunately, the accuracy of TDOA estimates is affected by multiple factors, including noise, reverberation and signal bandwidth. In this paper, a sub-band approach for time del…
Neural networks for animal science applications: Two case studies
2006
Abstract Artificial neural networks have shown to be a powerful tool for system modelling in a wide range of applications. In this paper, we focus on neural network applications to intelligent data analysis in the field of animal science. Two classical applications of neural networks are proposed: time series prediction and clustering. The first task is related to the prediction of weekly milk production in goat flocks, which includes a knowledge discovery stage in order to analyse the relative relevance of the different variables. The second task is the clustering of goat flocks; it is used to analyse different livestock surveys by using self-organizing maps and the adaptive resonance theo…
Analysis of motor control and behavior in multi agent systems by means of artificial neural networks
2004
Abstract This article gives a short introduction to Self-Organizing Maps, a particular form of Artificial Neural Networks and shows by some examples, how these approaches can be used in order to analyze and visualize time series data originating from complex systems. The methods shown in this article have originally been developed for the analysis of RoboCup robot soccer games, a special kind of so-called Multi Agent Systems. Although this application has no direct connection to biomechanics, the examples shown here may give an impression of the abilities of Neural Networks in the field of Time Series Analysis in general. Because of the abstractness of the methods, it appears to be very lik…