Automatic Burst Detection in Solar Radio Spectrograms Using Deep Learning: deARCE Method

We present in detail an automatic radio-burst detection system, based on the AlexNet con- volutional neural network, for use with any kind of solar spectrogram. A full methodology for model training, performance evaluation, and feedback to the model generator has been developed with special emphasis on i) robustness tests against stochastic and overfitting ef- fects, ii) specific metrics adapted to the unbalanced nature of the solar-burst scenario, iii) tunable parameters for probability-threshold optimization, and iv) burst-coincidence cross match among e-Callisto stations and with external observatories (NOAA-SWPC). The re- sulting neural network configuration has been designed to accept data from observatories other than e-Callisto, either ground- or spacecraft-based. Typical False Negative and False Positive Scores in single-observatory mode are, respectively, in the 10 ? 16% and 6 ? 8% ranges, which improve further in cross-match mode. This mode includes new services ( deARCE , Xmatch ) allowing the end-user to check at a glance if a solar radio burst has taken place with a high level of confidence.Junta de Comunidades de Castilla La Mancha; European Unio

Implementation of Frequency Drift for Identification of Solar Radio Burst Type II

Sun is constantly produced mass and radiation during its natural activities, which will interact with ionosphere and affect the earth weather. In radio astronomer community, CALLISTO is used to capture the radio signal comes from solar activities such as solar burst. Solar flares and Coronal Mass Ejections (CMEs) were closely associated with the production of solar radio burst Type II and III. However, the determination of solar burst existence is done manually using spectrograph which appears for every 15 minutes.  In order to assist the solar radio researcher to speed up the process of solar burst identification and detection, this work presents a new algorithm to auto classify solar radio burst Type II and III. The value of frequency drift was used as the main idea in this auto classify algorithm because it can easily implemented using MATLAB. There are three main steps involved named as pre-processing, identification and classification. Auto calculation of frequency drift burst on spectra was obtained from two parts which are frequency axis (df) and time axis (dt). The results of the frequency drift implementation in classification algorithm show that the algorithm developed gave almost similar determination as in manual detection. However, there are always have rooms for improvement for better detection system in future which may include specific characterization of bursts and improved noise elimination

Automated solar radio burst detection on radio spectrum: a review of techniques in image processing

The information of solar atmosphere was obtained after investigating the recording radiation of the space mission. With technology growing recently, a lot of solar radio receiver was introduced to monitor the solar radio activity on the ground with high efficiency. It is recorded in every second for 24 hours per day. A massive of solar radio spectra data produced every day that makes it impossible to identify, whether the data contain burst or not. By doing manual detection, human effort and error become the issues when the solar astronomer needs the fast and accurate result. Recently, the success of various techniques in image processing to identify solar radio burst automatically was presented. This paper reviews previous technique in image processing. This discussion will help the solar astronomer to find the best technique in pre-processing before moving into the next stage for detection of solar radio burst.Keywords: monitoring solar activity; automated solar radio burst detection; image processing; techniqu

Progetto di tirocinio laurea triennale in Fisica: Rilevamento automatico di scariche atmosferiche registrate negli spettrogrammi ‘CALLISTO’ tramite tecniche di machine-learning/deep-learning

Un solar radio burst (SRB) è un’ intensa emissione radio solare spesso correlata a un brillamento solare. Il rilevamento di SRBs puo’ essere effettuato tramite radiometri e spettrometri terrestri. CALLISTO e' uno spettrometro a basso costo progettato presso il Politecnico di Zurigo. Gli strumenti CALLISTO attivi in questo momento sono 67, distribuiti in tutto il mondo e permettono il monitoraggio delle emissioni radio solari 24h/7 tramite la rete e-Callisto http://www.e-callisto.org/. La Stazione Osservativa di Basovizza di INAF – Osservatorio Astronomico di Trieste e’ equipaggiata con tre spettrometri tipo CALLISTO operanti rispettivamente nelle bande VHF, UHF, L. E’ attivo anche un sistema sperimentale di rilevamento automatico di SRBs. I radiospettri acquisiti dagli spettrometri sono purtroppo affetti da disturbi ed interferenze a radio frequenza (RFI) che a volte vengono interpretati dal sistema come burst/falsi positivi. Tra questi le scariche atmosferiche, rilevate dagli spettrometri come emissioni lineari su tutto lo spettro. Il loro riconoscimento ed eliminazione puo’ contribuire in maniera importante a diminuire il numero di falsi positivi rilevati. Tra i vari possibili metodi di riconoscimento, tenuto anche conto del numero di eventi a disposizione per costruire un training set di numerosità adeguata, si e’ pensato di usare un sistema basato sul deep learning. Il lavoro effettuato nell’ambito del tirocinio, dopo l’acquisizione delle necessarie conoscenze di base (solar radio burst, documentazione CALLISTO, reti neurali, deep-learning) ha consentito la costruzione di training/test set di eventi ed una sperimentazione pilota su dati limitati con sviluppo ed esecuzione di modelli Keras su una virtual machine Virtualbox Ubuntu64 18.04 LTS, precedentemente installata ed attrezzata con librerie TensorFlow/Keras, propedeutica ad una sperimentazione completa su una Deep-Learning Machine fisica dedicata (CPU Xeon + GPU NVIDIA Quadro P4000)

Physics-Informed Computer Vision: A Review and Perspectives

Incorporation of physical information in machine learning frameworks are opening and transforming many application domains. Here the learning process is augmented through the induction of fundamental knowledge and governing physical laws. In this work we explore their utility for computer vision tasks in interpreting and understanding visual data. We present a systematic literature review of formulation and approaches to computer vision tasks guided by physical laws. We begin by decomposing the popular computer vision pipeline into a taxonomy of stages and investigate approaches to incorporate governing physical equations in each stage. Existing approaches in each task are analyzed with regard to what governing physical processes are modeled, formulated and how they are incorporated, i.e. modify data (observation bias), modify networks (inductive bias), and modify losses (learning bias). The taxonomy offers a unified view of the application of the physics-informed capability, highlighting where physics-informed learning has been conducted and where the gaps and opportunities are. Finally, we highlight open problems and challenges to inform future research. While still in its early days, the study of physics-informed computer vision has the promise to develop better computer vision models that can improve physical plausibility, accuracy, data efficiency and generalization in increasingly realistic applications

The Bird's Ear View: Audification for the Spectral Analysis of Heliospheric Time Series Data.

The sciences are inundated with a tremendous volume of data, and the analysis of rapidly expanding data archives presents a persistent challenge. Previous research in the field of data sonification suggests that auditory display may serve a valuable function in the analysis of complex data sets. This dissertation uses the heliospheric sciences as a case study to empirically evaluate the use of audification (a specific form of sonification) for the spectral analysis of large time series. Three primary research questions guide this investigation, the first of which addresses the comparative capabilities of auditory and visual analysis methods in applied analysis tasks. A number of controlled within-subject studies revealed a strong correlation between auditory and visual observations, and demonstrated that auditory analysis provided a heightened sensitivity and accuracy in the detection of spectral features. The second research question addresses the capability of audification methods to reveal features that may be overlooked through visual analysis of spectrograms. A number of open-ended analysis tasks quantitatively demonstrated that participants using audification regularly discovered a greater percentage of embedded phenomena such as low-frequency wave storms. In addition, four case studies document collaborative research initiatives in which audification contributed to the acquisition of new domain-specific knowledge. The final question explores the potential benefits of audification when introduced into the workflow of a research scientist. A case study is presented in which a heliophysicist incorporated audification into their working practice, and the “Think-Aloud” protocol is applied to gain a sense for how audification augmented the researcher’s analytical abilities. Auditory observations are demonstrated to make significant contributions to ongoing research, including the detection of previously unidentified equipment-induced artifacts. This dissertation provides three primary contributions to the field: 1) an increased understanding of the comparative capabilities of auditory and visual analysis methods, 2) a methodological framework for conducting audification that may be transferred across scientific domains, and 3) a set of well-documented cases in which audification was applied to extract new knowledge from existing data archives. Collectively, this work presents a “bird’s ear view” afforded by audification methods—a macro understanding of time series data that preserves micro-level detail.PhDDesign ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111561/1/rlalexan_1.pd

Academic Year 2019-2020 Faculty Excellence Showcase, AFIT Graduate School of Engineering & Management

An excerpt from the Dean\u27s Message: There is no place like the Air Force Institute of Technology (AFIT). There is no academic group like AFIT’s Graduate School of Engineering and Management. Although we run an educational institution similar to many other institutions of higher learning, we are different and unique because of our defense-focused graduate-research-based academic programs. Our programs are designed to be relevant and responsive to national defense needs. Our programs are aligned with the prevailing priorities of the US Air Force and the US Department of Defense. Our faculty team has the requisite critical mass of service-tested faculty members. The unique composition of pure civilian faculty, military faculty, and service-retired civilian faculty makes AFIT truly unique, unlike any other academic institution anywhere