Ashwani Kumar Aggarwal

About

Ashwani Kumar did Ph.D. in computer vision from Information and Communication Engineering department at the University of Tokyo, Japan. He did master of technology in instrumentation from Indian Institute of Science, Bangalore, India and Bachelor of Engineering in Electronics and Instrumentation Engineering from College of Engineering and Technology, Bathinda, India. He is recipient of QIP and MEXT scholarships. His research interests are computer vision and artificial intelligence.

Employment

• Sant Longowal Institute of Engineering and Technology Professor
  1996 - Present

Education

• The University of Tokyo Ph.D.
  2009 - 2014
• Indian Institute of Science Master of Technology
  2002 - 2002
• College of Engineering and Technology Bachelor of Engineering
  1992 - 1996

Projects & Funding

Projects & funding information is unavailable.

Publications (92)

• Comparative Analysis of Non-Local Means and Hybrid Median Bilateral Filtering for Mobile Image Denoising
  Smart Wearable Technology 2026 DOI: 10.47852/bonviewSWT62028015
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• Position Estimation Enhancement Using GNSS and LiDAR Sensor Data
  2025 DOI: 10.1007/978-3-031-92602-0_41
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• AI and CV based 2D-CNN algorithm: botanical authentication of Indian honey
  Sustainable Food Technology 2024 DOI: 10.1039/D3FB00170A
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• Detection of sugar syrup adulteration in unifloral honey using deep learning framework: An effective quality analysis technique
  Food and Humanity 2024 DOI: 10.1016/J.FOOHUM.2023.11.017
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• Determinants of well-being factors mediated by teacher commitment
  Journal of Education and e-Learning Research 2024 DOI: 10.20448/jeelr.v11i2.5566
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• Chemical and functional characteristics to detect sugar syrup adulteration in honey from different botanical origins
  International Journal of Food Properties 2023 DOI: 10.1080/10942912.2023.2218066
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• Investigative analysis of different mutation on diversity-driven multi-parent evolutionary algorithm and its application in area coverage optimization of WSN
  Soft Computing 2023 DOI: 10.1007/S00500-023-08090-3
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• Enhancing assessment of corn growth performance using unmanned aerial vehicles (UAVs) and deep learning
  Measurement 2023 DOI: 10.1016/J.MEASUREMENT.2023.112764
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• Deep Learning-Based Spatiotemporal Fusion of Unmanned Aerial Vehicle and Satellite Reflectance Images for Crop Monitoring
  IEEE Access 2023 DOI: 10.1109/ACCESS.2023.3297513
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• A review of remote sensing image spatiotemporal fusion: Challenges, applications and recent trends
  Remote Sensing Applications: Society and Environment 2023 DOI: 10.1016/J.RSASE.2023.101005
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