Doctorate of Philosophy (PhD)
Remote Sensing
UAV,Ballastic Missile, Natural Disasters
Professional Member
MIEEE(California) | MIENG(HongKong) | MATSC(USA) | MIFERP(India) | MIRED(USA) | MINSC(India)
2022
Automotive/UAV Embedded Systems Developer
Bare-Metal | Firmware/RTOS/Linux Device Driver Developer
QT/Visual Studio/Visual Code | C/C++ | Eagle/Altium/KiCAD
Best Researcher Award 2022
SHEN Awards
2022
Research Excellent award 2021
InSc
2021
Skills and Specialization
👨💻 Programming languages
Research and Development
Best Researcher Award
Honored with "Best Researcher Award" by International Research Awards on Science, Health and Engineering (SHEN Awards) in 2022.
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Terrain Aware Cellular Network Blind Spot Recovery Algorithm Using AeriaBTS
The efficiency of cellular communication systems is hampered by cyclones. Network operator authenticated network blind spots, generated after cellular BTS destruction, were successfully recovered using AerialBTS systems by selecting Nisarga cyclone as a representative natural calamity in this case using the stated algorithm in this paper. The geographical location and parameter reconfiguration of AerialBTS have been precisely described to provide maximum coverage of the cellular network. Using the corresponding algorithm, network deployment successfully expanded network coverage by 22.31% with the Digital Elevation Model.
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Ballistics Algorithm For Airborne Remote Sensor Position In Catastrophe Zones
Sensor payload calibration and FOV are still the most essential parameters in Airborn Remote Sensors for devel- oping quality terrain images in the Remote Sensing field. UAVs are core devices in Airborn Remote Sensing by carrying camera payload for large terrain objects to achieve desired image quality with geographical position provided for different available flight planning algorithms. This paper proposes the algorithm to find the exact geo- coordinates for UAV navigation algorithm viewpoint identi- fication for implementing Airborn Remote Sensors (ARS)to study Landslide Susceptibility Zone as well as application for catastrophe analysis. Terrain Slope and Aspect was considered using Remote Sensing products from ISRO CartoSat and NASA ASTER satellite available in the Digital Elevation Model (DEM) construction to maintain the calibrated angle of vision, field of vision, and land surface image quality. This algorithm will enhance the functioning of various avail- able UAV Flight Planning Algorithms to improve the quality of 3D topography images for 3D Modeling as well as Landslide and Flood Prediction and Analysis, as well as provide the position of camera payload and tilting angle for enhanced terrain images.
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Airborne UAV Remote Sensor Position Accuracy Algorithm in Catastrophes Zones
Sensor payload calibration and FOV are still the most essential parameters in Airborne Remote Sensors for developing quality terrain images in the Remote Sensing field. UAVs are core devices in Airborne Remote Sensing by carrying camera payload for large terrain objects to achieve desired image quality with geographical position provided for different available flight planning algorithms. This paper proposes the algorithm to find the exact Geo-coordinates for UAV navigation algorithm viewpoint identification for implementing Airborne Remote Sensing (ARS) to study Landslide Susceptibility Zone as well as application for catastrophe analysis. Terrain Slope and Aspect was considered using Remote Sensing products from ISRO CartoSat and NASA ASTER satellite available in the Digital Elevation Model (DEM) construction to maintain the calibrated angle of vision, field of vision, and land surface image quality. This algorithm will enhance the functioning of various available UAV Flight Planning Algorithms to improve the quality of 3D topography images for 3D Modeling as well as Landslide and Flood Prediction and Analysis, as well as provide the position of camera payload and tilting angle for enhanced terrain images.
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Research Excellence Award
Honored with "Research Excellent Award" by Institute of Science in 2020.
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VKz Surveying Robot (GPTrack Edition)
A natural disaster can happen anytime and you cant stop it. Living in such a natural calamity loses their lives, but human beings are awake to defend themselves against such a catastrophe. So that you succeed in saving your precious life. The team is very difficult to work at such places and also suffers from severe problems and also has time to go. But using the Surveying Robotic Vehicle, we can overcome all these problems. In modern technology, autonomous robotic system is an outstanding innovation that can help robots in a natural disaster. In which places the Rescue team cant reach, robots will be helped so that the team can save time. Robots can help man by doing a job that is impossible for humans to do. The propose system design to identify the victim inside the collapse building and other underground areas like mines, heavy duty pipe lines as well as tunnels and some other places where rescue operation needs proper information about people inside the affected area.
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Introduction-Wireless Local Area Sensor Network Protocol for Rescue Calling
The residential and industrial accidents frequently occurred on each day worldwide. As per the survey, millions of people lost their lives because they did not intimated by any way. Hence to make personal as well as professional life & property make more secure & safe, I have designed WLASN protocol follower Rescue caller & data Analyzer system.
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