533 research outputs found
Evolution of Relative Magnetic Helicity: New Boundary Conditions for the Vector Potential
We recently proposed a method to calculate the relative magnetic helicity in
a finite volume for a given magnetic field which however required the flux to
be balanced separately on all the sides of the considered volume. In order to
allow finite magnetic fluxes through the boundaries, a Coulomb gauge is
constructed that allows for global magnetic flux balance. We tested and
verified our method in a theoretical fore-free magnetic field model. We apply
the new method to the former calculation data and found a difference of less
than 1.2\%. We also applied our method to the magnetic field above active
region NOAA 11429 obtained by a new photospheric-data-driven MHD model code
GOEMHD3. We analyzed the magnetic helicity evolution in the solar corona using
our new method. It was found that the normalized magnetic helicityis equal to
-0.038 when fast magnetic reconnection is triggered. This value is comparable
to the previous value (-0.029) in the MHD simulations when magnetic
reconnection happened and the observed normalized magnetic helicity (-0.036)
from the eruption of newly emerging active regions. We found that only 8\% of
the accumulated magnetic helicity is dissipated after it is injected through
the bottom boundary. This is in accordance with the Woltjer conjecture. Only
2\% of magnetic helicity injected from the bottom boundary escapes through the
corona. This is consistent with the observation of magnetic clouds, which could
take away magnetic helicity into the interplanetary space, in the case
considered here, several halo CMEs and two X-class solar flares origin from
this active region.Comment: Accepted to be pulished on A&
Compression via Compressive Sensing : A Low-Power Framework for the Telemonitoring of Multi-Channel Physiological Signals
Telehealth and wearable equipment can deliver personal healthcare and
necessary treatment remotely. One major challenge is transmitting large amount
of biosignals through wireless networks. The limited battery life calls for
low-power data compressors. Compressive Sensing (CS) has proved to be a
low-power compressor. In this study, we apply CS on the compression of
multichannel biosignals. We firstly develop an efficient CS algorithm from the
Block Sparse Bayesian Learning (BSBL) framework. It is based on a combination
of the block sparse model and multiple measurement vector model. Experiments on
real-life Fetal ECGs showed that the proposed algorithm has high fidelity and
efficiency. Implemented in hardware, the proposed algorithm was compared to a
Discrete Wavelet Transform (DWT) based algorithm, verifying the proposed one
has low power consumption and occupies less computational resources.Comment: 2013 International Workshop on Biomedical and Health Informatic
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