Lakshmikanthan P | Engineering | Best Researcher Award

Published on by Innovation Technologist

Dr. Lakshmikanthan P | Engineering | Best Researcher Award

Senior Scientist/Assistant Professor at Council for Scientific and Industrial Research – Fourth Paradigm Institute | India

Lakshmikanthan P is a Senior Scientist at CSIR-4PI, Bangalore, and an Assistant Professor at the Academy of Scientific and Innovative Research, recognized for his expertise in environmental engineering, climate change assessment, urban health risk, environmental forecasting, solid waste management, sustainability engineering, and ocean modeling. He has an extensive track record in research and academia, contributing through multiple roles as a scientist, assistant professor, and postdoctoral researcher. His work focuses on the characterization and management of municipal solid waste, landfill fires, gas emissions, leachate treatment, landfill mining, and sustainable waste solutions. He has published numerous journal articles, conference papers, book chapters, and technical articles, reflecting his dedication to advancing knowledge in environmental protection and sustainable urban management. He has guided and co-guided several Ph.D. and master’s students, mentoring the next generation of researchers. His research has significant recognition, with 488 citations overall (395 since 2020), an h-index of 12 (11 since 2020), and an i10-index of 15 (14 since 2020), demonstrating the impact and reach of his contributions. He is actively involved in professional organizations, including the American Society of Civil Engineers and the International Water Association, and has been honored with awards for research excellence. With expertise in diverse engineering and environmental software, computational modeling, and statistical analysis, his work bridges theoretical research and practical solutions. His contributions have shaped urban environmental management practices, improved landfill safety and sustainability, and enhanced scientific understanding of waste behavior, establishing him as a leading researcher in environmental engineering, sustainable waste management, and urban health risk assessment.

Profile: Google Scholar

Featured Publications

Lakshmikanthan, P., Babu, G. L. S., & Santhosh, L. G. (2015). Shear strength characteristics of mechanically biologically treated municipal solid waste (MBT-MSW) from Bangalore. Waste Management, 39, 63–70.
Cited by: 57

Chavan, D., Lakshmikanthan, P., Mondal, P., Kumar, S., & Kumar, R. (2019). Determination of ignition temperature of municipal solid waste for understanding surface and sub-surface landfill fire. Waste Management, 97, 123–130.
Cited by: 48

Babu, G. L. S., Lakshmikanthan, P., & Santhosh, L. G. (2014). Life cycle analysis of municipal solid waste (MSW) land disposal options in Bangalore City. In ICSI 2014: Creating Infrastructure for a Sustainable World (pp. 795–806). American Society of Civil Engineers (ASCE).
Cited by: 47

Manjunatha, G. S., Chavan, D., Lakshmikanthan, P., Singh, L., Kumar, S., & others. (2020). Specific heat and thermal conductivity of municipal solid waste and its effect on landfill fires. Waste Management, 116, 120–130.
Cited by: 45

Sivakumar Babu, G. L., & Lakshmikanthan, P. (2015). Estimation of the components of municipal solid waste settlement. Waste Management & Research, 33(1), 30–38.
Cited by: 43

Manas Ranjan Sethi | ECE | Best Researcher Award

Published on by Innovation Technologist

Mr. Manas Ranjan Sethi | ECE | Best Researcher Award

Research Scholar at NIT Silchar, India

Manas Ranjan Sethi is a dedicated academic professional currently pursuing a Ph.D. in Electronics and Instrumentation Engineering at NIT Silchar, with a strong focus on machine learning applications in fault diagnosis and energy systems. He holds an M.Tech in Electronics & Telecommunication from BPUT, Odisha and has over 12 years of teaching experience, having worked as an Assistant Professor at Gandhi Institute for Technology (GIFT) and as a Lecturer at Koustuv Institute of Self Domain, Bhubaneswar. His research interests include machine learning, signal processing, and sustainable energy systems, particularly in wind turbine diagnostics and emotion recognition using EEG signals. Manas has contributed to numerous journals, conferences, and book chapters, and he has earned distinctions such as qualifying CBSE-UGC NET and GATE. He is also skilled in technical tools, enjoys singing, and has a passion for reading.

Publication Profile : 

Scopus

 

🎓 Educational Background :

Manas Ranjan Sethi is currently pursuing a Ph.D. in Electronics and Instrumentation Engineering at NIT Silchar (since March 2020). He holds an M.Tech in Electronics & Telecommunication (Specialization in Communication Engineering) from BPUT, Odisha (2012), with a CGPA of 8.20. He completed his B.E. in Electronics & Telecommunication from BPUT, Odisha, in 2006, securing a 62.19%. Additionally, he completed his +2 Science and Matriculation from MP Board, Madhya Pradesh.

💼 Professional Experience :

Manas has a rich academic career spanning over 12 years. He served as an Assistant Professor in the Electronics & Communication Engineering Department at Gandhi Institute for Technology (GIFT), Bhubaneswar, from November 2013 to March 2020. Prior to that, he worked as a Lecturer in the Electronics & Telecommunication Engineering Department at Koustuv Institute of Self Domain, Bhubaneswar, from July 2007 to November 2013. He has a strong foundation in teaching and mentoring students in the field of Electronics and Communication Engineering.

📚 Research Interests : 

Manas’s research interests lie in the domains of Machine Learning, Signal Processing, and Fault Diagnosis. His work focuses on vibration signal-based diagnostics and energy extraction using wind turbines. He is passionate about leveraging machine learning techniques for predictive maintenance and condition monitoring. His recent research includes the application of meta-classifiers for diagnosing wind turbine blade faults and exploring emotion recognition through EEG signals.

🏆Achievements & Certifications :

Manas has earned several academic distinctions, including qualifying the CBSE-UGC NET (Electronic Science) in July 2018, and securing GATE scores of 260 (2016) and 218 (2011) in Electronics and Communication. He has also attended and contributed to various seminars, workshops, and short-term courses in fields such as VLSI Design, Microwave Filters, and Adaptive Signal Processing.

📝 Publication Top Notes :

  • Sethi, M. R., Subba, A. B., Faisal, M., Sahoo, S., & Koteswara Raju, D. (2024). Fault diagnosis of wind turbine blades with continuous wavelet transform based deep learning model using vibration signal. Engineering Applications of Artificial Intelligence, 138, 109372.
  • Sethi, M. R., Sahoo, S., Dhanraj, J. A., & Sugumaran, V. (2023). Vibration Signal-Based Diagnosis of Wind Turbine Blade Conditions for Improving Energy Extraction Using Machine Learning Approach. Smart and Sustainable Manufacturing Systems, 7(1), 14–40.
  • Chatterjee, S., Sethi, M. R., & Asad, M. W. A. (2016). Production phase and ultimate pit limit design under commodity price uncertainty. European Journal of Operational Research, 248(2), 658–667.
  • Sethi, M. R., Parhi, S. S., Sahoo, S., Sugumaran, V., & Mohanty, S. R. (2023). Fault Diagnosis of Wind Turbine Blades Through Vibration Signal Using Filtered Cultivation Data: A Comparative Study. Proceedings of the 2023 IEEE Region 10 Symposium, TENSYMP 2023.
  • Kar, P., Hazarika, J., & Sethi, M. R. (2023). A Comparative Study between Supervised and Unsupervised Techniques for Two Class Emotion Recognition using EEG. Proceedings of the 2023 IEEE 8th International Conference for Convergence in Technology, I2CT 2023.
  • Banala, H. S., Sahoo, S., Sethi, M. R., & Sharma, A. K. (2023). Fault Diagnosis in Wind Turbine Blades Using Machine Learning Techniques. In R. Doriya, B. Soni, A. Shukla, & X. Z. Gao (Eds.), Machine Learning, Image Processing, Network Security and Data Sciences (Lecture Notes in Electrical Engineering, Vol. 946), 401–411. Springer, Singapore.
  • Sethi, M. R., Sahoo, S., Kanoongo, S., & Hemasudheer, B. (2022). A Comparative Study on Diagnosing Wind Turbine Blade Fault Conditions using Rule Classifier. Proceedings of the 2022 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET 2022), 1–6. doi: 10.1109/ICEFEET51821.2022.9848401.
  • Sethi, M. R., Hemasudheer, B., Sahoo, S., & Kanoongo, S. (2022). A Comparative Study on Diagnosing Wind Turbine Blade Fault Conditions using Vibration Data through META Classifiers. Proceedings of the 2022 4th International Conference on Energy, Power, and Environment (ICEPE 2022), 1–5. doi: 10.1109/ICEPE55035.2022.9798026.