Time delays in quasi-periodic pulsations observed during the X2.2 solar flare on 2011 February 15

We report observations of quasi-periodic pulsations (QPPs) during the X2.2 flare of 2011 February 15, observed simultaneously in several wavebands. We focus on fluctuations on time scale 1-30 s and find different time lags between different wavebands. During the impulsive phase, the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) channels in the range 25-100 keV lead all the other channels. They are followed by the Nobeyama RadioPolarimeters at 9 and 17 GHz and the Extreme Ultra-Violet (EUV) channels of the Euv SpectroPhotometer (ESP) onboard the Solar Dynamic Observatory (SDO). The Zirconium and Aluminum filter channels of the Large Yield Radiometer (LYRA) onboard the Project for On-Board Autonomy (PROBA2) satellite and the SXR channel of ESP follow. The largest lags occur in observations from the Geostationary Operational Environmental Satellite (GOES), where the channel at 1-8 {\AA} leads the 0.5-4 {\AA} channel by several seconds. The time lags between the first and last channels is up to 9 s. We identified at least two distinct time intervals during the flare impulsive phase, during which the QPPs were associated with two different sources in the Nobeyama RadioHeliograph at 17 GHz. The radio as well as the hard X-ray channels showed different lags during these two intervals. To our knowledge, this is the first time that time lags are reported between EUV and SXR fluctuations on these time scales. We discuss possible emission mechanisms and interpretations, including flare electron trapping

The SWAP EUV Imaging Telescope Part I: Instrument Overview and Pre-Flight Testing

The Sun Watcher with Active Pixels and Image Processing (SWAP) is an EUV solar telescope on board ESA's Project for Onboard Autonomy 2 (PROBA2) mission launched on 2 November 2009. SWAP has a spectral bandpass centered on 17.4 nm and provides images of the low solar corona over a 54x54 arcmin field-of-view with 3.2 arcsec pixels and an imaging cadence of about two minutes. SWAP is designed to monitor all space-weather-relevant events and features in the low solar corona. Given the limited resources of the PROBA2 microsatellite, the SWAP telescope is designed with various innovative technologies, including an off-axis optical design and a CMOS-APS detector. This article provides reference documentation for users of the SWAP image data.Comment: 26 pages, 9 figures, 1 movi

The SWAP Filter: A Simple Azimuthally Varying Radial Filter for Wide-Field EUV Solar Images

We present the SWAP Filter: an azimuthally varying, radial normalizing filter specifically developed for EUV images of the solar corona, named for the Sun Watcher with Active Pixels and Image Processing (SWAP) instrument on the Project for On-Board Autonomy 2 spacecraft. We discuss the origins of our technique, its implementation and key user-configurable parameters, and highlight its effects on data via a series of examples. We discuss the filter's strengths in a data environment in which wide field-of-view observations that specifically target the low signal-to-noise middle corona are newly available and expected to grow in the coming years.Comment: Contact D. B. Seaton for animations referenced in figure caption

Robustness evaluation of deep neural networks for endoscopic image analysis:Insights and strategies

Computer-aided detection and diagnosis systems (CADe/CADx) in endoscopy are commonly trained using high-quality imagery, which is not representative for the heterogeneous input typically encountered in clinical practice. In endoscopy, the image quality heavily relies on both the skills and experience of the endoscopist and the specifications of the system used for screening. Factors such as poor illumination, motion blur, and specific post-processing settings can significantly alter the quality and general appearance of these images. This so-called domain gap between the data used for developing the system and the data it encounters after deployment, and the impact it has on the performance of deep neural networks (DNNs) supportive endoscopic CAD systems remains largely unexplored. As many of such systems, for e.g. polyp detection, are already being rolled out in clinical practice, this poses severe patient risks in particularly community hospitals, where both the imaging equipment and experience are subject to considerable variation. Therefore, this study aims to evaluate the impact of this domain gap on the clinical performance of CADe/CADx for various endoscopic applications. For this, we leverage two publicly available data sets (KVASIR-SEG and GIANA) and two in-house data sets. We investigate the performance of commonly-used DNN architectures under synthetic, clinically calibrated image degradations and on a prospectively collected dataset including 342 endoscopic images of lower subjective quality. Additionally, we assess the influence of DNN architecture and complexity, data augmentation, and pretraining techniques for improved robustness. The results reveal a considerable decline in performance of 11.6% (1.5) as compared to the reference, within the clinically calibrated boundaries of image degradations. Nevertheless, employing more advanced DNN architectures and self-supervised in-domain pre-training effectively mitigate this drop to 7.7% (2.03). Additionally, these enhancements yield the highest performance on the manually collected test set including images with lower subjective quality. By comprehensively assessing the robustness of popular DNN architectures and training strategies across multiple datasets, this study provides valuable insights into their performance and limitations for endoscopic applications. The findings highlight the importance of including robustness evaluation when developing DNNs for endoscopy applications and propose strategies to mitigate performance loss.Computer-aided detection and diagnosis systems (CADe/CADx) in endoscopy are commonly trained using high-quality imagery, which is not representative for the heterogeneous input typically encountered in clinical practice. In endoscopy, the image quality heavily relies on both the skills and experience of the endoscopist and the specifications of the system used for screening. Factors such as poor illumination, motion blur, and specific post-processing settings can significantly alter the quality and general appearance of these images. This so-called domain gap between the data used for developing the system and the data it encounters after deployment, and the impact it has on the performance of deep neural networks (DNNs) supportive endoscopic CAD systems remains largely unexplored. As many of such systems, for e.g. polyp detection, are already being rolled out in clinical practice, this poses severe patient risks in particularly community hospitals, where both the imaging equipment and experience are subject to considerable variation. Therefore, this study aims to evaluate the impact of this domain gap on the clinical performance of CADe/CADx for various endoscopic applications. For this, we leverage two publicly available data sets (KVASIR-SEG and GIANA) and two in-house data sets. We investigate the performance of commonly-used DNN architectures under synthetic, clinically calibrated image degradations and on a prospectively collected dataset including 342 endoscopic images of lower subjective quality. Additionally, we assess the influence of DNN architecture and complexity, data augmentation, and pretraining techniques for improved robustness. The results reveal a considerable decline in performance of 11.6% (1.5) as compared to the reference, within the clinically calibrated boundaries of image degradations. Nevertheless, employing more advanced DNN architectures and self-supervised in-domain pre-training effectively mitigate this drop to 7.7% (2.03). Additionally, these enhancements yield the highest performance on the manually collected test set including images with lower subjective quality. By comprehensively assessing the robustness of popular DNN architectures and training strategies across multiple datasets, this study provides valuable insights into their performance and limitations for endoscopic applications. The findings highlight the importance of including robustness evaluation when developing DNNs for endoscopy applications and propose strategies to mitigate performance los

Barrett's lesion detection using a minimal integer-based neural network for embedded systems integration

Embedded processing architectures are often integrated into devices to develop novel functions in a cost-effective medical system. In order to integrate neural networks in medical equipment, these models require specialized optimizations for preparing their integration in a high-efficiency and power-constrained environment. In this paper, we research the feasibility of quantized networks with limited memory for the detection of Barrett’s neoplasia. An Efficientnet-lite1+Deeplabv3 architecture is proposed, which is trained using a quantization-aware training scheme, in order to achieve an 8-bit integer-based model. The performance of the quantized model is comparable with float32 precision models. We show that the quantized model with only 5-MB memory is capable of reaching the same performance scores with 95% Area Under the Curve (AUC), compared to a fullprecision U-Net architecture, which is 10× larger. We have also optimized the segmentation head for efficiency and reduced the output to a resolution of 32×32 pixels. The results show that this resolution captures sufficient segmentation detail to reach a DICE score of 66.51%, which is comparable to the full floating-point model. The proposed lightweight approach also makes the model quite energy-efficient, since it can be real-time executed on a 2-Watt Coral Edge TPU. The obtained low power consumption of the lightweight Barrett’s esophagus neoplasia detection and segmentation system enables the direct integration into standard endoscopic equipment

Dynamics of Coronal Bright Points as seen by Sun Watcher using Active Pixel System detector and Image Processing (SWAP), Atmospheric Imaging Assembly AIA), and Helioseismic and Magnetic Imager (HMI)

The \textit{Sun Watcher using Active Pixel system detector and Image Processing}(SWAP) on board the \textit{PRoject for OnBoard Autonomy\todash 2} (PROBA\todash 2) spacecraft provides images of the solar corona in EUV channel centered at 174 \AA. These data, together with \textit{Atmospheric Imaging Assembly} (AIA) and the \textit{Helioseismic and Magnetic Imager} (HMI) on board \textit{Solar Dynamics Observatory} (SDO), are used to study the dynamics of coronal bright points. The evolution of the magnetic polarities and associated changes in morphology are studied using magnetograms and multi-wavelength imaging. The morphology of the bright points seen in low-resolution SWAP images and high-resolution AIA images show different structures, whereas the intensity variations with time show similar trends in both SWAP 174 and AIA 171 channels. We observe that bright points are seen in EUV channels corresponding to a magnetic-flux of the order of $10^{18}$ Mx. We find that there exists a good correlation between total emission from the bright point in several UV\todash EUV channels and total unsigned photospheric magnetic flux above certain thresholds. The bright points also show periodic brightenings and we have attempted to find the oscillation periods in bright points and their connection to magnetic flux changes. The observed periods are generally long (10\todash 25 minutes) and there is an indication that the intensity oscillations may be generated by repeated magnetic reconnection

Excitation of standing kink oscillations in coronal loops

In this work we review the efforts that have been done to study the excitation of the standing fast kink body mode in coronal loops. We mainly focus on the time-dependent problem, which is appropriate to describe flare or CME induced kink oscillations. The analytical and numerical studies in slab and cylindrical loop geometries are reviewed. We discuss the results from very simple one-dimensional models to more realistic (but still simple) loop configurations. We emphasise how the results of the initial value problem complement the eigenmode calculations. The possible damping mechanisms of the kink oscillations are also discussed

Resonant Absorption of Axisymmetric Modes in Twisted Magnetic Flux Tubes

It has been shown recently that magnetic twist and axisymmetric MHD modes are ubiquitous in the solar atmosphere, and therefore the study of resonant absorption for these modes has become a pressing issue because it can have important consequences for heating magnetic flux tubes in the solar atmosphere and the observed damping. In this investigation, for the first time, we calculate the damping rate for axisymmetric MHD waves in weakly twisted magnetic flux tubes. Our aim is to investigate the impact of resonant damping of these modes for solar atmospheric conditions. This analytical study is based on an idealized configuration of a straight magnetic flux tube with a weak magnetic twist inside as well as outside the tube. By implementing the conservation laws derived by Sakurai et al. and the analytic solutions for weakly twisted flux tubes obtained recently by Giagkiozis et al. we derive a dispersion relation for resonantly damped axisymmetric modes in the spectrum of the Alfvén continuum. We also obtain an insightful analytical expression for the damping rate in the long wavelength limit. Furthermore, it is shown that both the longitudinal magnetic field and the density, which are allowed to vary continuously in the inhomogeneous layer, have a significant impact on the damping time. Given the conditions in the solar atmosphere, resonantly damped axisymmetric modes are highly likely to be ubiquitous and play an important role in energy dissipation. We also suggest that, given the character of these waves, it is likely that they have already been observed in the guise of Alfvén waves

On Solving the Coronal Heating Problem

This article assesses the current state of understanding of coronal heating, outlines the key elements of a comprehensive strategy for solving the problem, and warns of obstacles that must be overcome along the way.Comment: Accepted by Solar Physics; Published by Solar Physic