11,727 research outputs found
Detecting and quantifying stellar magnetic fields -- Sparse Stokes profile approximation using orthogonal matching pursuit
In the recent years, we have seen a rapidly growing number of stellar
magnetic field detections for various types of stars. Many of these magnetic
fields are estimated from spectropolarimetric observations (Stokes V) by using
the so-called center-of-gravity (COG) method. Unfortunately, the accuracy of
this method rapidly deteriorates with increasing noise and thus calls for a
more robust procedure that combines signal detection and field estimation. We
introduce an estimation method that provides not only the effective or mean
longitudinal magnetic field from an observed Stokes V profile but also uses the
net absolute polarization of the profile to obtain an estimate of the apparent
(i.e., velocity resolved) absolute longitudinal magnetic field. By combining
the COG method with an orthogonal-matching-pursuit (OMP) approach, we were able
to decompose observed Stokes profiles with an overcomplete dictionary of
wavelet-basis functions to reliably reconstruct the observed Stokes profiles in
the presence of noise. The elementary wave functions of the sparse
reconstruction process were utilized to estimate the effective longitudinal
magnetic field and the apparent absolute longitudinal magnetic field. A
multiresolution analysis complements the OMP algorithm to provide a robust
detection and estimation method. An extensive Monte-Carlo simulation confirms
the reliability and accuracy of the magnetic OMP approach.Comment: A&A, in press, 15 pages, 14 figure
Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device
There is a significant high fall risk population, where individuals are susceptible to frequent falls and obtaining significant injury, where quick medical response and fall information are critical to providing efficient aid. This article presents an evaluation of compressive sensing techniques in an accelerometer-based intelligent fall detection system modelled on a wearable Shimmer biomedical embedded computing device with Matlab. The presented fall detection system utilises a database of fall and activities of daily living signals evaluated with discrete wavelet transforms and principal component analysis to obtain binary tree classifiers for fall evaluation. 14 test subjects undertook various fall and activities of daily living experiments with a Shimmer device to generate data for principal component analysis-based fall classifiers and evaluate the proposed fall analysis system. The presented system obtains highly accurate fall detection results, demonstrating significant advantages in comparison with the thresholding method presented. Additionally, the presented approach offers advantageous fall diagnostic information. Furthermore, transmitted data accounts for over 80% battery current usage of the Shimmer device, hence it is critical the acceleration data is reduced to increase transmission efficiency and in-turn improve battery usage performance. Various Matching pursuit-based compressive sensing techniques have been utilised to significantly reduce acceleration information required for transmission.Scopu
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