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Fabry-Prot based narrow band imager for solar filament observations

research in Astronomy and Astrophysics 2016年第1期16卷 79-88页

作者： Sajal kumar Dhara Belur ravindra r.vinder.kumar banyal Indian Institute of Astrophysics Bangalore-560034 India

We have recently developed a narrow band imager（NBI） using an air gap based Fabry-P′erot（FP） interferometer at the Indian Institute of Astrophysics, Bangalore. Narrow band imaging is achieved by using an FP interferometer working in combination with an order sorting pre-filter. The NBI can be tuned to a different wavelength position on the line profile by changing the plate separation of the FP. The interferometer has a 50 mm clear aperture with a bandpass of ～247.8 m ？A and a free spectral range of～5.3 ？A at λ = 656.3 nm. The developed NBI is used to observe the solar filament in the Hα *** instrument is being used to image the Sun at chromospheric height and it is also able to scan the Hα spectral line profile at different wavelength positions. We have also made Doppler velocity maps at chromospheric height by taking the blue and red wing images at ±176 m ？A wavelength positions separately away from the line center of the spectral line. In this paper, we present a description of the NBI including lab test results of individual components and some initial observations carried out with this instrument.

关键词： instrumentation: interferometers -- instrumentation: spectrographs -- methods: observational-- Sun: chromosphere -- Sun: filaments/prominences

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Measurements of atmospheric turbulence parameters at Vainu Bappu Observatory using short-exposure CCD images

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research in Astronomy and Astrophysics 2019年第5期19卷 123-132页

作者： Sreekanth reddy V. r.vinder.kumar banyal Sridharan r. Aishwarya Selvaraj Indian Institute of Astrophysics

We report atmospheric turbulence parameters, namely atmospheric seeing, tilt-anisoplanatic angle(θ_0) and coherence time(Τ_0), measured under various sky conditions, at Vainu Bappu Observatory in Kavalur. Bursts of short exposure images of selected stars were recorded with a high-speed, frame-transfer CCD mounted on the Cassegrain focus of a newly commissioned 1.3 m telescope. The estimated median seeing is ≈ 1.85 " at wavelength of ～ 600 nm, the image motion correlation between different pairs of stars is ～44% for θ0≈ 36" and mean Τ_0 is ≈ 2.4 ms. This work was motivated by the design considerations and expected performance of an adaptive optics system that is currently being planned for the telescope.

关键词： atmospheric effects turbulence instrumentation:adaptive optics catalogs methods:observational

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Development of image motion compensation system for 1.3 m telescope at Vainu Bappu Observatory

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research in Astronomy and Astrophysics 2020年第1期20卷 93-106页

作者： Sreekanth reddy Vallapureddy r.vinder.kumar banyal Sridharan rengaswamy P Umesh Kamath Aishwarya Selvaraj Indian Institute of Astrophysics

We developed a tip-tilt system to compensate the turbulence induced image motion for the 1.3 m telescope at Vainu Bappu Observatory,at *** instrument is designed to operate at the visible wavelength band(480-700 nm)with a field of view of 1′×1′.The tilt corrected images have demonstrated up to ≈57% improvement in image resolution and a corresponding peak intensity increase by a factor of ≈2.8.A closed-loop correction bandwidth of ≈26 Hz has been achieved with on-sky tests and the root mean square motion of the star image has been reduced by a factor of ~*** results are consistent with theoretical and numerical predictions of wave-front aberrations caused by atmospheric turbulence and image quality improvement expected from a real-time control *** this paper,we present details of the instrument design,laboratory calibration studies and quantify its performance on the telescope.

关键词： adaptive optics atmospheric effects high angular resolution

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Technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

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Computers, Materials & Continua 2021年第5期67卷 1679-1696页

作者： Siddhant banyal rinky Dwivedi Koyel Datta Gupta Deepak kumar Sharma Fadi Al-Turjman Leonardo Mostarda Department of Instrumentation and Control Netaji Subhas University of TechnologyNew Delhi110078India Department of Computer Science&Engineering Maharaja Surajmal Institute of TechnologyNew Delhi110058India Department of Information Technology Netaji Subhas University of TechnologyNew Delhi110078India research Center for AI and IoT Near East UniversityNicosiaMersin10Turkey Computer Science Department Camerino UniversityCamerino62032Italy

The COVID-19 outbreak initiated from the Chinese city of Wuhanand eventually affected almost every nation around the globe. From China,the disease started spreading to the rest of the world. After China, Italybecame the next epicentre of the virus and witnessed a very high death *** nations like the USA became severely hit by SArS-CoV-2 virus. TheWorld Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the worldhas instituted various policies like physical distancing, isolation of infectedpopulation and researching on the potential vaccine of SArS-CoV-2. Toidentify the impact of various policies implemented by the affected countrieson the pandemic spread, a myriad of AI-based models have been presented toanalyse and predict the epidemiological trends of COVID-19. In this work, theauthors present a detailed study of different articial intelligence frameworksapplied for predictive analysis of COVID-19 patient record. The forecastingmodels acquire information from records to detect the pandemic spreadingand thus enabling an opportunity to take immediate actions to reduce thespread of the virus. This paper addresses the research issues and correspondingsolutions associated with the prediction and detection of infectious diseaseslike COVID-19. It further focuses on the study of vaccinations to cope withthe pandemic. Finally, the research challenges in terms of data availability,reliability, the accuracy of the existing prediction models and other open issuesare discussed to outline the future course of this study.

关键词： COVID-19 diagnosis deep learning forecasting models machine learning metaheuristics prediction big data pandemic

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