Bannari Amman Institute of Technology | India
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Early Academic Pursuits
Dr. Harikumar Rajaguru began his academic journey in electronics and communication engineering. He completed his Bachelor of Engineering in Electronics and Communication Engineering from Regional Engineering College, Trichy, affiliated with Bharathidasan University, in 1988. He then pursued a Master of Engineering in Applied Electronics at the College of Engineering, Guindy, under Anna University Chennai, graduating in 1990. His passion for biomedical signal processing led him to obtain a Ph.D. in Information and Communication Engineering, with a specialization in Bio Signal Processing, from Thiagarajar College of Engineering, Madurai, under Anna University Chennai in 2009. His Ph.D. thesis focused on the use of soft computing techniques and non-linear models for the performance analysis and classification of epilepsy risk levels from EEG signals.
Professional Endeavors
Dr. Rajaguru's professional career spans over 33 years in academia. He began as a lecturer and senior lecturer at PSNA College of Engineering and Technology, Dindigul, where he served for ten years. He then worked as an Assistant Professor at PSG College of Technology, Coimbatore, for one year. His journey continued at Amrita Institute of Technology, Coimbatore, as a Senior Lecturer and Assistant Professor for over two years. Dr. Rajaguru also spent time as a research scholar at Thiagarajar College of Engineering, Madurai. Since 2006, he has been a professor at Bannari Amman Institute of Technology, Sathyamangalam, where he has contributed significantly to the field of biomedical signal processing and soft computing.
Contributions and Research Focus
Dr. Rajaguru has made substantial contributions to biomedical signal processing, particularly in the classification of epilepsy risk levels from EEG signals. He has been involved in several funded projects, including the development of an ASIC fuzzy processor for diabetic epilepsy risk level classification, wavelet networks for epilepsy risk levels classification from EEG signals, and a non-invasive photo plethysmographic-based glucometer for mass diabetes screening. His research primarily focuses on applying soft computing techniques and developing non-linear models for analyzing and classifying biomedical signals.
Accolades and Recognition
Throughout his career, Dr. Rajaguru has received numerous accolades and recognition for his contributions to engineering and biomedical signal processing. He has published multiple papers in SCI-indexed journals and conferences, showcasing his research findings and innovations. His work has been recognized with patents for various biomedical devices, including an electro gastrogram system for detecting gastric disorders, a sensor for stress measurement using photo plethysmography, and a device for detecting ventricular tachycardia. These patents highlight his innovative approach to solving complex biomedical problems.
Impact and Influence
Dr. Rajaguru's work has significantly impacted biomedical signal processing, particularly in the analysis and classification of epilepsy risk levels and the development of non-invasive diagnostic tools. His research has provided new insights into the use of soft computing and wavelet networks in biomedical applications. His contributions have influenced both academic research and practical applications in the medical field, improving diagnostic techniques and patient outcomes.
Legacy and Future Contributions
Dr. Rajaguru's legacy lies in his dedication to advancing biomedical signal processing through innovative research and teaching. He has guided numerous students in their research projects, fostering the next generation of engineers and researchers. As he continues his work at Bannari Amman Institute of Technology, he is poised to make further contributions to the field, particularly in developing new diagnostic tools and techniques for medical applications. His commitment to research and education ensures that his influence will be felt for years to come, both in academia and the broader field of biomedical engineering.
Notable Publications
Wavelet feature extraction and bio-inspired feature selection for the prognosis of lung cancer − A statistical framework analysis 2024
Processing of digital mammogram images using optimized ELM with deep transfer learning for breast cancer diagnosis 2023 (5)
Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images 2023 (6)
Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance 2023 (1)
Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene—A Paradigm Shift 2023 (6)