673,270 research outputs found
Transition rates for a S \ge 1 model coupled to a phonon bath
We investigate transition rates between different spin configurations for spins weakly coupled to a -dimensional phonon bath. This study is
motivated by understanding observed magnetization relaxation as a function of
temperature in diverse magnetic systems such as arrays of magnetic
nanoparticles and magnetic molecules. We assume that the magnetization of the
spin system relaxes through consecutive emission or absorption of a single
phonon. From a weak, linear spin-phonon coupling Hamiltonian, we derive
transition rates that would be used to examine dynamic properties of the system
in kinetic Monte Carlo simulations. Although the derived phonon-assisted
transition rates satisfy detailed balance, in the case of two and three
dimensional phonon baths, transitions between degenerate states are not
allowed. Thus, if there are no alternative paths along which the spin system
can relax, the relaxation time diverges. Otherwise, the system finds other
paths, which leads to an increase in the relaxation time and energy barrier.
However, when higher-order phonon processes are included in the transition
rates, it is found that the system can reach the states which were inaccessible
due to the forbidden transitions. As a result, the system recovers some of the
dynamic properties obtained using the Glauber transition rate.Comment: 8 figures. Submitted to PR
Data-driven multivariate and multiscale methods for brain computer interface
This thesis focuses on the development of data-driven multivariate and multiscale methods
for brain computer interface (BCI) systems. The electroencephalogram (EEG), the
most convenient means to measure neurophysiological activity due to its noninvasive nature,
is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its
multichannel recording nature require a new set of data-driven multivariate techniques to
estimate more accurately features for enhanced BCI operation. Also, a long term goal
is to enable an alternative EEG recording strategy for achieving long-term and portable
monitoring.
Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully
data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary
EEG signal into a set of components which are highly localised in time and frequency. It
is shown that the complex and multivariate extensions of EMD, which can exploit common
oscillatory modes within multivariate (multichannel) data, can be used to accurately
estimate and compare the amplitude and phase information among multiple sources, a
key for the feature extraction of BCI system. A complex extension of local mean decomposition
is also introduced and its operation is illustrated on two channel neuronal
spike streams. Common spatial pattern (CSP), a standard feature extraction technique
for BCI application, is also extended to complex domain using the augmented complex
statistics. Depending on the circularity/noncircularity of a complex signal, one of the
complex CSP algorithms can be chosen to produce the best classification performance
between two different EEG classes.
Using these complex and multivariate algorithms, two cognitive brain studies are
investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user
attention to a sound source among a mixture of sound stimuli, which is aimed at improving
the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments
elicited by taste and taste recall are examined to determine the pleasure and displeasure
of a food for the implementation of affective computing. The separation between two
emotional responses is examined using real and complex-valued common spatial pattern
methods.
Finally, we introduce a novel approach to brain monitoring based on EEG recordings
from within the ear canal, embedded on a custom made hearing aid earplug. The new
platform promises the possibility of both short- and long-term continuous use for standard
brain monitoring and interfacing applications
Multi-frequency based location search algorithm of small electromagnetic inhomogeneities embedded in two-layered medium
In this paper, we consider a problem for finding the locations of
electromagnetic inhomogeneities completely embedded in homogeneous two layered
medium. For this purpose, we present a filter function operated at several
frequencies and design an algorithm for finding the locations of such
inhomogeneities. It is based on the fact that the collected Multi-Static
Response (MSR) matrix can be modeled via a rigorous asymptotic expansion
formula of the scattering amplitude due to the presence of such
inhomogeneities. In order to show the effectiveness, we compare the proposed
algorithm with traditional MUltiple SIgnal Classification (MUSIC) algorithm and
Kirchhoff migration. Various numerical results demonstrate that the proposed
algorithm is robust with respect to random noise and yields more accurate
location than the MUSIC algorithm and Kirchhoff migration.Comment: 21 pages, 25 figure
Power Spectrum of Cosmic Momentum Field Measured from the SFI Galaxy Sample
We have measured the cosmic momentum power spectrum from the peculiar
velocities of galaxies in the SFI sample. The SFI catalog contains field spiral
galaxies with radial peculiar velocities derived from the I-band Tully-Fisher
relation. As a natural measure of the large-scale peculiar velocity field, we
use the cosmic momentum field that is defined as the peculiar velocity field
weighted by local number of galaxies. We have shown that the momentum power
spectrum can be derived from the density power spectrum for the constant linear
biasing of galaxy formation, which makes it possible to estimate \beta_S =
\Omega_m^{0.6} / b_S parameter precisely where \Omega_m is the matter density
parameter and b_S is the bias factor for optical spiral galaxies. At each
wavenumber k we estimate \beta_S(k) as the ratio of the measured to the derived
momentum power over a wide range of scales (0.026 h^{-1}Mpc <~ k <~ 0.157
h^{-1}Mpc) that spans the linear to the quasi-linear regimes. The estimated
\beta_S(k)'s have stable values around 0.5, which demonstrates the constancy of
\beta_S parameter at scales down to 40 h^{-1}Mpc. We have obtained
\beta_S=0.49_{-0.05}^{+0.08} or \Omega_m = 0.30_{-0.05}^{+0.09} b_S^{5/3}, and
the amplitude of mass fluctuation as
\sigma_8\Omega_m^{0.6}=0.56_{-0.21}^{+0.27}. The 68% confidence limits include
the cosmic variance. We have also estimated the mass density power spectrum.
For example, at k=0.1047 h Mpc^{-1} (\lambda=60 h^{-1}Mpc) we measure
\Omega_m^{1.2} P_{\delta}(k)=(2.51_{-0.94}^{+0.91})\times 10^3 (h^{-1}Mpc)^3,
which is lower compared to the high-amplitude power spectra found from the
previous maximum likelihood analyses of peculiar velocity samples like Mark
III, SFI, and ENEAR.Comment: 12 pages, 9 figures, accepted for publication in Ap
Simulation of Cosmic Microwave Background Polarization Fields for AMiBA Experiment
We have made a topological study of cosmic microwave background (CMB)
polarization maps by simulating the AMiBA experiment results. A CDM
CMB sky is adopted to make mock interferometric observations designed for the
AMiBA experiment. CMB polarization fields are reconstructed from the AMiBA mock
visibility data using the maximum entropy method. We have also considered
effects of Galactic foregrounds on the CMB polarization fields. The genus
statistic is calculated from the simulated and polarization maps, where
and are Stokes parameters. Our study shows that the Galactic foreground
emission, even at low Galactic latitude, is expected to have small effects on
the CMB polarization field. Increasing survey area and integration time is
essential to detect non-Gaussian signals of cosmological origin through genus
measurement.Comment: 7 pages, 4 figures, Accepted version for publication in the Journal
of the Korean Astronomical Societ
- âŠ