| 1. |
- Frid, Henrik, et al.
(author)
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Determining Direction‐of‐Arrival Accuracy for Installed Antennas by Postprocessing of Far‐Field Data
- 2019
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In: Radio Science. - 0048-6604 .- 1944-799X. ; 54:12, s. 1204-1221
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Journal article (peer-reviewed)abstract
- Direction‐of‐arrival (DoA) estimation accuracy can be degraded due to installation effects, such as platform reflections, diffraction from metal edges, and reflections and refraction in the radome. To analyze these effects, this paper starts with a definition of the term installation error related to DoA estimation. Thereafter, we present a postprocessing method, which can be used to determine the DoA estimation accuracy for installed antennas. By computing synthetic signals from the installed far‐field data, it is possible to analyze the installation errors described above, in addition to analyzing array model errors. The method formulation is general, thus allowing generic array configurations, installation configurations, and direction‐finding algorithms to be studied. The use of the presented method is demonstrated by a case study of a wideband four‐quadrant array. In this case study, we investigate the installation errors due to a single‐shell radome. Thereafter, the effects of platform reflections are also analyzed, for an antenna placement in the tail of a fighter aircraft. Simulation results are presented for both the monopulse and the MUltiple SIgnal Classification direction‐finding algorithms.
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| 2. |
- Malmström, Johan, et al.
(author)
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Approximate methods to determine the isolation between antennas on vehicles
- 2016
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In: Antennas and Propagation (APSURSI), 2016 IEEE International Symposium on. - : IEEE conference proceedings. ; , s. 131-132
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Conference paper (peer-reviewed)abstract
- The isolation between antennas needs to be considered when integrating antennas on vehicles, since poor isolation between antennas can cause interference between radio systems on-board. The electrical size of vehicles at gigahertz frequencies often limits the usage of full-wave methods. This paper therefore evaluates two efficient methods to approximate the antenna isolation; the non-singular transmission integral (NSTI) method and the geometric theory of diffraction (GTD). We present the first evaluation of NSTI for antennas outside line-of-sight (non-LOS). It is shown to provide a 6.5 dB RMS accuracy in the early non-LOS zone for an antenna position sweep on an aircraft at 2 GHz, and for frequency sweeps on a cube and cylinder, the latter only to 5 GHz. The GTD implementation gives a 4.4 dB RMS accuracy for cylinders and simplified aircraft models. Both investigated methods give remarkably accurate results, considering memory requirements and runtime, which makes them interesting for further investigations.
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| 3. |
- Schirmer, M. D., et al.
(author)
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White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study
- 2019
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In: Neuroimage-Clinical. - : Elsevier BV. - 2213-1582. ; 23
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Journal article (peer-reviewed)abstract
- White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94-0.95; p < 0.01) and Pearson correlation of total brain volume (r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N = 2783) and identify a decrease in total brain volume of -2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited.
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