M. Anitha, Anitha Manisekaran, Anitha M

About

I received my B.E. in Electronics and Communication Engineering from Anjalai Ammal Mahalingam Engineering College in 2016 and my M.E. in Applied Electronics from the College of Engineering, Guindy, Anna University, in 2018. I also completed my M.A. in Yoga for Human Excellence from the University of Madras in 2018. I completed my Ph.D. in Electronics and Communication Engineering from the National Institute of Technology Puducherry in January 2026.

My research interests include remote sensing, atmospheric science, aerosol characterization, air pollution monitoring, and the application of machine learning and deep learning techniques to environmental and climate-related studies. My work focuses on the validation, classification, and bias correction of satellite-derived aerosol products using ground-based observations and data-driven approaches.

I actively engage in scientific publishing, peer review, conference presentations, and interdisciplinary research collaborations. My goal is to develop accurate and reliable environmental monitoring solutions that contribute to atmospheric and climate research.

Employment

• National Institute of Technology Puducherry Research Scholar
  2019 - Present

Education

• National Institute of Technology, Puducherry Ph.D.
  2019 - 2026
• CEG Campus, Anna University M.E (Applied Electronics)
  2016 - 2018
• University of Madras M.A
  2016 - 2018
• Anjalai Ammal Mahalingam Engineering College, Kovilvenni B.E
  2012 - 2016

Projects & Funding

Projects & funding information is unavailable.

Publications (14)

• Investigations on the Aerosol Type Classification using Machine Learning Techniques over Multiple AERONET Sites in India
  Advances in Space Research 2026 DOI: 10.1016/j.asr.2025.12.045
  +Save
• An Emerging Trend in Substrate Integrated Waveguide Filtering Antenna: Design Strategies, Performance Enhancement, and Applications - A Review
  Archives of Computational Methods in Engineering 2026 DOI: 10.1007/s11831-026-10622-7
  +Save
• Bias correction of MODIS retrieved Aerosol Optical Depth using Machine Learning and Deep Learning Techniques
  International Journal of Remote Sensing 2025 DOI: 10.1080/01431161.2025.2571235
  +Save
• Compact Ultra-Wide-Stopband Microwave Bandpass Filter with Multiple Transmission Zeros by Integrating an L-Shaped Bandpass Filter and a Stepped Impedance Lowpass Filter
  IEEE Latin America Transactions 2025 DOI: 10.1109/TLA.2025.10974366
  +Save
• Performance Assessment of MODIS Dark Target and Deep Blue Aerosol Retrieval Algorithms Using AERONET Observations: A Case Study over Jaipur, Karunya University, and Broader Indian Regions
  Environmental Monitoring and Assessment 2025 DOI: 10.1007/s10661-025-14653-8
  +Save
• An Analysis of Atmospheric Aerosol Characteristics Using Remote Sensing Data in the Indian Region
  Pure and Applied Geophysics 2024 DOI: 10.1007/s00024-023-03415-7
  +Save
• Composition and source based aerosol classification using machine learning algorithms
  Advances in Space Research 2024 DOI: 10.1016/j.asr.2023.09.068
  +Save
• Compact Triple-Band Bandpass Filter Using Coupled Resonators With Transmission Zeros
  2024 IEEE Microwaves Antennas and Propagation Conference Mapcon 2024 2024 DOI: 10.1109/MAPCON61407.2024.10923220
  +Save
• IoT Based Vehicle Theft Detection and Accident Detection using Arduino
  2023 Second International Conference on Advances in Computational Intelligence and Communication (ICACIC) 2023 DOI: 10.1109/icacic59454.2023.10435328
  +Save
• Tracking of NO2 and SO2 trace gases emission from Thermal Power Plants in Tamil Nadu using Sentinel 5P Tropomi Satellite with observations from CPCB CAAQM station
  2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT) 2023 DOI: 10.1109/iconscept57958.2023.10170014
  +Save