Publications

Autonomous Ephemeris Prediction using Navigation Receivers

Published in , 1900

The prediction of ephemeris has been a long term problem being worked upon in the scientific community using the traditional methods of statistics and signal processing. In this paper, we present our machine learning-based approach for the autonomous prediction of ephemeris using navigation receivers. We have used the data provided by the Indian Space Research Organization (ISRO) for the duration of 30 days in the RINEX navigation file format and formalized the prediction of single day ephemerides using the previous 25 days’ navigation data (ephemerides). This approach includes handling inconsistent timelines in the data. We have used the ‘Forecasting at Scale - Prophet’ model for time series prediction. Using this method we devised a solution to reduce the Time to First Fix (TTFF) effectively. Read more

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Smart Business Continuity Application

Published in International Journal of Innovative Research in Science, Engineering and Technology, 2019

In the current scenario, the Business Continuity Plans (BCPs) and Disaster Recovery Plans (DRs) in organizations are written using typical word processing tools. The monitoring of projects and triggering of these plans must be done manually. Here, we propose a Smart Business Continuity Application which shall allow an end user to store the BCPs for various projects of an organization in an Information Technology Service Management (ITSM) server. As soon as an asset or a service goes down due to a disaster, its backup, as mentioned in the project’s BCP, takes over its working. The admin of the organization is automatically notified of such incidents and he can check status of impacted projects at a glance. There is a provision to trigger BCPs for severely impacted projects by a single click. Read more

Recommended citation: Ashish Kshirsagar, You. (2019). "Smart Business Continuity Application" International Journal of Innovative Research in Science, Engineering and Technology http://Ask149.github.io/files/paper1.pdf