A web tool to detect and track Solar features from SDO images

Coronal bright points (CBPs) are useful features that can be used to calculate solar rotation even when no active regions are present. Unlike active regions, CBPs are dis-tributed at all latitudes on the solar disk and its lifetime varies from less than an hour to a few days. Identifying and tracking CBPs are the main keys to successfully calculate the Solar corona rotation profile for different latitudes. Over the last years this topic has been an area of research in solar astronomy and some effective methods have been developed. The purpose of this dissertation was to design a web tool that retrieves, prepro-cesses, detects and tracks CBPs on solar images and that allows search and visualization of CBPs and solar information from a database, helping astrophysicists on their solar analysis. The detection uses a gradient based segmentation algorithm that has proved to provide accurate data about CBPs’ dynamics. It was developed a website to visualize the results, hosted by SPINLab. The track-ing from 480 images confirmed to be consistent within the expected when comparing with other authors’ work. This topic was motivated by the astrophysicists need for a near to real-time tool that allows the most recent data, as well as archive with historical data, concerning the Solar corona rotation to be processed just a few minutes after the image being captured by Nasa’s Atmospheric Imaging Assembly on board of the Solar Dynamic Observatory

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp