21,315 research outputs found
Bayesian modelling and quantification of Raman spectroscopy
Raman spectroscopy can be used to identify molecules such as DNA by the
characteristic scattering of light from a laser. It is sensitive at very low
concentrations and can accurately quantify the amount of a given molecule in a
sample. The presence of a large, nonuniform background presents a major
challenge to analysis of these spectra. To overcome this challenge, we
introduce a sequential Monte Carlo (SMC) algorithm to separate each observed
spectrum into a series of peaks plus a smoothly-varying baseline, corrupted by
additive white noise. The peaks are modelled as Lorentzian, Gaussian, or
pseudo-Voigt functions, while the baseline is estimated using a penalised cubic
spline. This latent continuous representation accounts for differences in
resolution between measurements. The posterior distribution can be
incrementally updated as more data becomes available, resulting in a scalable
algorithm that is robust to local maxima. By incorporating this representation
in a Bayesian hierarchical regression model, we can quantify the relationship
between molecular concentration and peak intensity, thereby providing an
improved estimate of the limit of detection, which is of major importance to
analytical chemistry
Comparative power spectral analysis of simultaneous elecroencephalographic and magnetoencephalographic recordings in humans suggests non-resistive extracellular media
The resistive or non-resistive nature of the extracellular space in the brain
is still debated, and is an important issue for correctly modeling
extracellular potentials. Here, we first show theoretically that if the medium
is resistive, the frequency scaling should be the same for electroencephalogram
(EEG) and magnetoencephalogram (MEG) signals at low frequencies (<10 Hz). To
test this prediction, we analyzed the spectrum of simultaneous EEG and MEG
measurements in four human subjects. The frequency scaling of EEG displays
coherent variations across the brain, in general between 1/f and 1/f^2, and
tends to be smaller in parietal/temporal regions. In a given region, although
the variability of the frequency scaling exponent was higher for MEG compared
to EEG, both signals consistently scale with a different exponent. In some
cases, the scaling was similar, but only when the signal-to-noise ratio of the
MEG was low. Several methods of noise correction for environmental and
instrumental noise were tested, and they all increased the difference between
EEG and MEG scaling. In conclusion, there is a significant difference in
frequency scaling between EEG and MEG, which can be explained if the
extracellular medium (including other layers such as dura matter and skull) is
globally non-resistive.Comment: Submitted to Journal of Computational Neuroscienc
Improved EMD Using doubly-iterative sifting and high order spline interpolation
Empirical mode decomposition (EMD) is a signal analysis method which has received much attention lately due to its application in a number of fields. The main disadvantage of EMD is that it lacks a theoretical analysis and, therefore, our understanding of EMD comes from an intuitive and experimental validation of the method. Recent research on EMD revealed improved criteria for the interpolation points selection. More specifically, it was shown that the performance of EMD can be significantly enhanced if, as interpolation points, instead of the signal extrema, the extrema of the subsignal having the higher instantaneous frequency are used. Even if the extrema of the subsignal with the higher instantaneous frequency are not known in advance, this new interpolation points criterion can be effectively exploited in doubly-iterative sifting schemes leading to improved decomposition performance. In this paper, the possibilities and limitations of the developments above are explored and the new methods are compared with the conventional EMD
Time-varying model identification for time-frequency feature extraction from EEG data
A novel modelling scheme that can be used to estimate and track time-varying properties of nonstationary signals is investigated. This scheme is based on a class of time-varying AutoRegressive with an eXogenous input (ARX) models where the associated time-varying parameters are represented by multi-wavelet basis functions. The orthogonal least square (OLS) algorithm is then applied to refine the model parameter estimates of the time-varying ARX model. The main features of the multi-wavelet approach is that it enables smooth trends to be tracked but also to capture sharp changes in the time-varying process parameters. Simulation studies and applications to real EEG data show that the proposed algorithm can provide important transient information on the inherent dynamics of nonstationary processes
Evidence for a 304-day Orbital Period for GX 1+4
In this paper we report strong evidence for a ~304-day periodicity in the
spin history of the accretion-powered pulsar GX 1+4 that is very likely to be a
signature of the orbital period of the system. Using BATSE public-domain data,
we show a highly-significant periodic modulation of the pulsar frequency from
1991 to date which is in excellent agreement with the ephemeris proposed by
Cutler, Dennis & Dolan in 1986, which were based on a few events of enhanced
spin-up that occurred during the pulsar's spin-up era in the 1970s. Our results
indicate that the orbital period of GX 1+4 is 303.8+-1.1 days, making it by far
the widest low-mass X-ray binary system known. A likely scenario for this
system is an elliptical orbit in which the neutron star decreases its spin-down
rate (or even exhibits a momentary spin-up behavior) at periastron passages due
to the higher torque exerted by the accretion disk onto the magnetosphere of
the neutron star.Comment: 5 pages, 2 figures, 1 single PS file, to appear in "Proceedings of
the 5th Compton Symposium on Gamma-Ray Astrophysics", AI
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