84,095 research outputs found
Investigating the Kinematics of Coronal Mass Ejections with the Automated CORIMP Catalog
Studying coronal mass ejections (CMEs) in coronagraph data can be challenging
due to their diffuse structure and transient nature, compounded by the
variations in their dynamics, morphology, and frequency of occurrence. The
large amounts of data available from missions like the Solar and Heliospheric
Observatory (SOHO) make manual cataloging of CMEs tedious and prone to human
error, and so a robust method of detection and analysis is required and often
preferred. A new coronal image processing catalog called CORIMP has been
developed in an effort to achieve this, through the implementation of a dynamic
background separation technique and multiscale edge detection. These algorithms
together isolate and characterise CME structure in the field-of-view of the
Large Angle Spectrometric Coronagraph (LASCO) onboard SOHO. CORIMP also applies
a Savitzky-Golay filter, along with quadratic and linear fits, to the
height-time measurements for better revealing the true CME speed and
acceleration profiles across the plane-of-sky. Here we present a sample of new
results from the CORIMP CME catalog, and directly compare them with the other
automated catalogs of Computer Aided CME Tracking (CACTus) and Solar Eruptive
Events Detection System (SEEDS), as well as the manual CME catalog at the
Coordinated Data Analysis Workshop (CDAW) Data Center and a previously
published study of the sample events. We further investigate a form of
unsupervised machine learning by using a k-means clustering algorithm to
distinguish detections of multiple CMEs that occur close together in space and
time. While challenges still exist, this investigation and comparison of
results demonstrates the reliability and robustness of the CORIMP catalog,
proving its effectiveness at detecting and tracking CMEs throughout the LASCO
dataset.Comment: 23 pages, 11 figures, 1 tabl
An end-to-end software solution for the analysis of high-throughput single-cell migration data
The systematic study of single-cell migration requires the availability of software for assisting data inspection, quality control and analysis. This is especially important for high-throughput experiments, where multiple biological conditions are tested in parallel. Although the field of cell migration can count on different computational tools for cell segmentation and tracking, downstream data visualization, parameter extraction and statistical analysis are still left to the user and are currently not possible within a single tool. This article presents a completely new module for the open-source, cross-platform CellMissy software for cell migration data management. This module is the first tool to focus specifically on single-cell migration data downstream of image processing. It allows fast comparison across all tested conditions, providing automated data visualization, assisted data filtering and quality control, extraction of various commonly used cell migration parameters, and non-parametric statistical analysis. Importantly, the module enables parameters computation both at the trajectory-and at the step-level. Moreover, this single-cell analysis module is complemented by a new data import module that accommodates multiwell plate data obtained from high-throughput experiments, and is easily extensible through a plugin architecture. In conclusion, the end-to-end software solution presented here tackles a key bioinformatics challenge in the cell migration field, assisting researchers in their highthroughput data processing
Service Knowledge Capture and Reuse
The keynote will start with the need for service knowledge capture and reuse for industrial product-service systems. A novel approach to capture the service damage knowledge about individual component will be presented with experimental results. The technique uses active thermography and image processing approaches for the assessment. The paper will also give an overview of other non-destructive inspection techniques for service damage assessment. A robotic system will be described to automate the damage image capture. The keynote will then propose ways to reuse the knowledge to predict remaining life of the component and feedback to design and manufacturing
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