55,499 research outputs found
The Tukey algorithm for enhancing MST radar data
One of the most troublesome features in MSR (mesosphere stratosphere troposphere) velocity measurements is the determination of unwanted scatterer whose velocity is different from that of the surrounding atmosphere. Aircraft seen in the sidelobes of the antenna are the principal problem. Because coherent integration essentially eliminates echoes with line of sight velocities greater than 10 or 20 m/s, aircraft are seen only when their flight path is almost perpendicular to the line of sight. Then, they give large returns whose velocities may be positive or negative, and certainly different from that of the surrounding air. The glitches in the minute by minute velocity records are quite troublesome in that they may distort the statistics of the velocity. An objective way is therefore needed to remove sporadic points of this kind. For this purpose, the Tukey algorithm is appropriate and has some advantages over averaging. The Tukey algorithm, applied to a data array, uses for each data point the median of it and the two points surrounding it. If the three points form a monotonically increasing or decreasing sequence, the original point is copied with change. However, if the central point is remote from the other two, it is replaced by whichever of the two surrounding points is closest in value. The greatest effect of the Tukey algorithm is on data where the successive points are uncorrelated. Examples are given
Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI
Parallel MRI is a fast imaging technique that enables the acquisition of
highly resolved images in space or/and in time. The performance of parallel
imaging strongly depends on the reconstruction algorithm, which can proceed
either in the original k-space (GRAPPA, SMASH) or in the image domain
(SENSE-like methods). To improve the performance of the widely used SENSE
algorithm, 2D- or slice-specific regularization in the wavelet domain has been
deeply investigated. In this paper, we extend this approach using 3D-wavelet
representations in order to handle all slices together and address
reconstruction artifacts which propagate across adjacent slices. The gain
induced by such extension (3D-Unconstrained Wavelet Regularized -SENSE:
3D-UWR-SENSE) is validated on anatomical image reconstruction where no temporal
acquisition is considered. Another important extension accounts for temporal
correlations that exist between successive scans in functional MRI (fMRI). In
addition to the case of 2D+t acquisition schemes addressed by some other
methods like kt-FOCUSS, our approach allows us to deal with 3D+t acquisition
schemes which are widely used in neuroimaging. The resulting 3D-UWR-SENSE and
4D-UWR-SENSE reconstruction schemes are fully unsupervised in the sense that
all regularization parameters are estimated in the maximum likelihood sense on
a reference scan. The gain induced by such extensions is illustrated on both
anatomical and functional image reconstruction, and also measured in terms of
statistical sensitivity for the 4D-UWR-SENSE approach during a fast
event-related fMRI protocol. Our 4D-UWR-SENSE algorithm outperforms the SENSE
reconstruction at the subject and group levels (15 subjects) for different
contrasts of interest (eg, motor or computation tasks) and using different
parallel acceleration factors (R=2 and R=4) on 2x2x3mm3 EPI images.Comment: arXiv admin note: substantial text overlap with arXiv:1103.353
The Modified Direct Method: an Approach for Smoothing Planar and Surface Meshes
The Modified Direct Method (MDM) is an iterative mesh smoothing method for
smoothing planar and surface meshes, which is developed from the non-iterative
smoothing method originated by Balendran [1]. When smooth planar meshes, the
performance of the MDM is effectively identical to that of Laplacian smoothing,
for triangular and quadrilateral meshes; however, the MDM outperforms Laplacian
smoothing for tri-quad meshes. When smooth surface meshes, for trian-gular,
quadrilateral and quad-dominant mixed meshes, the mean quality(MQ) of all mesh
elements always increases and the mean square error (MSE) decreases during
smoothing; For tri-dominant mixed mesh, the quality of triangles always
descends while that of quads ascends. Test examples show that the MDM is
convergent for both planar and surface triangular, quadrilateral and tri-quad
meshes.Comment: 18 page
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
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