Beam Pattern Optimization Method for Subarray-Based Hybrid Beamforming Systems

Massive multiple-input multiple-output (MIMO) systems operating at millimeter-wave (mmWave) frequencies promise to satisfy the demand for higher data rates in mobile communication networks. A practical challenge that arises is the calibration in amplitude and phase of these massive MIMO systems, as the antenna elements are too densely packed to provide a separate calibration branch for measuring them independently. Over-the-air (OTA) calibration methods are viable solutions to this problem. In contrast to previous works, the here presented OTA calibration method is investigated and optimized for subarray-based hybrid beamforming (SBHB) systems. SBHB systems represent an efficient architectural solution to realize massive MIMO systems. Moreover, based on OTA scattering parameter measurements, the ambiguities of the phase shifters are exploited and two criteria to optimize the beam pattern are formulated. Finally, the optimization criteria are examined in measurements utilizing a novel SBHB receiver system operating at 27.8 GHz

On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT Setting

Computed tomography (CT) is routinely used for three-dimensional non-invasive imaging. Numerous data-driven image denoising algorithms were proposed to restore image quality in low-dose acquisitions. However, considerably less research investigates methods already intervening in the raw detector data due to limited access to suitable projection data or correct reconstruction algorithms. In this work, we present an end-to-end trainable CT reconstruction pipeline that contains denoising operators in both the projection and the image domain and that are optimized simultaneously without requiring ground-truth high-dose CT data. Our experiments demonstrate that including an additional projection denoising operator improved the overall denoising performance by 82.4-94.1%/12.5-41.7% (PSNR/SSIM) on abdomen CT and 1.5-2.9%/0.4-0.5% (PSNR/SSIM) on XRM data relative to the low-dose baseline. We make our entire helical CT reconstruction framework publicly available that contains a raw projection rebinning step to render helical projection data suitable for differentiable fan-beam reconstruction operators and end-to-end learning.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Noise2Contrast: Multi-Contrast Fusion Enables Self-Supervised Tomographic Image Denoising

Self-supervised image denoising techniques emerged as convenient methods that allow training denoising models without requiring ground-truth noise-free data. Existing methods usually optimize loss metrics that are calculated from multiple noisy realizations of similar images, e.g., from neighboring tomographic slices. However, those approaches fail to utilize the multiple contrasts that are routinely acquired in medical imaging modalities like MRI or dual-energy CT. In this work, we propose the new self-supervised training scheme Noise2Contrast that combines information from multiple measured image contrasts to train a denoising model. We stack denoising with domain-transfer operators to utilize the independent noise realizations of different image contrasts to derive a self-supervised loss. The trained denoising operator achieves convincing quantitative and qualitative results, outperforming state-of-the-art self-supervised methods by 4.7-11.0%/4.8-7.3% (PSNR/SSIM) on brain MRI data and by 43.6-50.5%/57.1-77.1% (PSNR/SSIM) on dual-energy CT X-ray microscopy data with respect to the noisy baseline. Our experiments on different real measured data sets indicate that Noise2Contrast training generalizes to other multi-contrast imaging modalities

Lower-thermosphere–ionosphere (LTI) quantities: current status of measuring techniques and models

The lower-thermosphere-ionosphere (LTI) system consists of the upper atmosphere and the lower part of the ionosphere and as such comprises a complex system coupled to both the atmosphere below and space above. The atmospheric part of the LTI is dominated by laws of continuum fluid dynamics and chemistry, while the ionosphere is a plasma system controlled by electromagnetic forces driven by the magnetosphere, the solar wind, as well as the wind dynamo. The LTI is hence a domain controlled by many different physical processes. However, systematic in situ measurements within this region are severely lacking, although the LTI is located only 80 to 200 km above the surface of our planet. This paper reviews the current state of the art in measuring the LTI, either in situ or by several different remote-sensing methods. We begin by outlining the open questions within the LTI requiring high-quality in situ measurements, before reviewing directly observable parameters and their most important derivatives. The motivation for this review has arisen from the recent retention of the Daedalus mission as one among three competing mission candidates within the European Space Agency (ESA) Earth Explorer 10 Programme. However, this paper intends to cover the LTI parameters such that it can be used as a background scientific reference for any mission targeting in situ observations of the LTI.Peer reviewe

Annual (2023) taxonomic update of RNA-directed RNA polymerase-encoding negative-sense RNA viruses (realm Riboviria: kingdom Orthornavirae: phylum Negarnaviricota)

55 Pág.In April 2023, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by one new family, 14 new genera, and 140 new species. Two genera and 538 species were renamed. One species was moved, and four were abolished. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV.This work was supported in part through the Laulima Government Solutions, LLC, prime contract with the U.S. National Institute of Allergy and Infec tious Diseases (NIAID) under Contract No. HHSN272201800013C. J.H.K. performed this work as an employee of Tunnell Government Services (TGS), a subcontractor of Laulima Government Solutions, LLC, under Contract No. HHSN272201800013C. U.J.B. was supported by the Division of Intramural Resarch, NIAID. This work was also funded in part by Contract No. HSHQDC15-C-00064 awarded by DHS S and T for the management and operation of The National Biodefense Analysis and Countermeasures Centre, a federally funded research and development centre operated by the Battelle National Biodefense Institute (V.W.); and NIH contract HHSN272201000040I/HHSN27200004/D04 and grant R24AI120942 (N.V., R.B.T.). S.S. acknowl edges support from the Mississippi Agricultural and Forestry Experiment Station (MAFES), USDA-ARS project 58-6066-9-033 and the National Institute of Food and Agriculture, U.S. Department of Agriculture, Hatch Project, under Accession Number 1021494. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of the Army, the U.S. Department of Defence, the U.S. Department of Health and Human Services, including the Centres for Disease Control and Prevention, the U.S. Department of Homeland Security (DHS) Science and Technology Directorate (S and T), or of the institutions and companies affiliated with the authors. In no event shall any of these entities have any responsibility or liability for any use, misuse, inability to use, or reliance upon the information contained herein. The U.S. departments do not endorse any products or commercial services mentioned in this publication. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S.Government retains a non-exclusive, paid up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. Government purposes.Peer reviewe

2020 taxonomic update for phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales.

In March 2020, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. At the genus rank, 20 new genera were added, two were deleted, one was moved, and three were renamed. At the species rank, 160 species were added, four were deleted, ten were moved and renamed, and 30 species were renamed. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV