Doctorate. R. Prasanna: Assistant Professor at SRM Institute of Science and Technology, India
Dr. R. Prasanna is a dynamic academician, researcher, and mentor with over 12 years of rich teaching experience in the field of Electronics and Communication Engineering. His expertise lies in Antenna and RF–Microwave Engineering with a special focus on biomedical and IoT-based applications. He is a recognized Ph.D. Research Supervisor under Anna University (Ref No: 4140101) and has guided numerous students in both academic and professional capacities. With a passion for innovation, he has contributed significantly to cutting-edge areas such as UWB antennas for healthcare, 5G antenna design, and smart sensor systems. In addition to his academic contributions, he is a strong advocate of skill-based education and has launched a YouTube channel titled “Passionate Professor” to support learning through technology and motivation.
Online Profiles
Dr. Prasanna actively engages with the academic and professional community through various digital platforms. He shares educational content and placement preparation resources on his YouTube channel, which features over 140 videos. His LinkedIn profile reflects his extensive professional activities, while his ORCID tracks his research contributions. He can be contacted via email at professorprasannaece@gmail.com and is available for academic collaborations, guest lectures, and mentoring.
Education
Dr. Prasanna holds a Ph.D. in Antenna and RF–Microwave Engineering from Anna University (2016–2022), where his thesis focused on the implementation of Ultra-Wide Band (UWB) antennas for blood Prothrombin Time detection—a novel solution for medical diagnostics. He earned his M.E. in Communication Systems (2009–2011) from GKM College of Engineering and Technology, Perungalathur, graduating with First Class. He completed his B.E. in Electronics and Communication Engineering (2004–2008) from VRS College of Engineering and Technology, Villupuram, also with First Class. His academic journey laid the foundation for a multidisciplinary approach that bridges electronics, healthcare, and embedded systems.
Research Focus
Dr. Prasanna’s research primarily revolves around Ultra-Wide Band (UWB) communication, RF and microwave systems, and wireless sensor networks. A significant portion of his work applies these technologies to the healthcare sector, notably in non-invasive patient monitoring, cardiovascular diagnostics, and touchless sensing using flexible antenna substrates. He also explores Cognitive Radio, MIMO systems, wearable electronics, and reconfigurable antenna design. His multidisciplinary collaborations and innovative methods have resulted in several high-impact publications, patents, and real-time system prototypes aimed at solving practical problems in medical, vehicular, and smart city domains.
Experience
Currently serving as an Assistant Professor at SRM Institute of Science and Technology (since June 2023), Dr. Prasanna brings rich experience from his previous roles at Sri Sai Ram Institute of Technology and Mailam Engineering College. He has taught core subjects such as RF and Microwave Engineering, Communication Theory, Wireless Networks, and IoT for over a decade. Beyond teaching, he has held critical roles including NBA Coordinator, Placement Training Coordinator, and NAAC Chapter Leader. His excellence in mentorship, curriculum design, and placement training has positively impacted hundreds of students in securing positions in top-tier MNCs and core industries.
Research Timeline
Dr. Prasanna’s research journey began with a focus on mobile ad hoc networks and cryptographic security, but evolved rapidly into the application of RF and UWB systems in biomedical engineering. His doctoral work introduced a UWB antenna for blood coagulation monitoring, positioning his research at the intersection of healthcare and wireless technologies. From 2019 onward, he expanded into touchless monitoring, smart wearable devices, and pandemic-specific applications like asymptomatic COVID-19 detection. He has published 7 SCI-indexed and 2 Scopus-indexed journal papers, filed 5 patents, and presented over 15 papers in international and national conferences, earning accolades and best paper awards.
Awards & Honors
Dr. Prasanna has received numerous awards in recognition of his academic excellence and student-centered contributions. These include the “Best Teacher Award” (2017–2018), “Best Student Motivator Award” (2020–2021), and a special Recognition Award from Anna University for publishing in Q1 journals. His research received the “Best Presentation Award” at IIT Kharagpur (CESSGS 2020) and the “Best Paper Award” at ICCEBS 2023. As a placement coordinator, he consistently achieved the highest number of student placements and was honored with multiple awards for excellence in student training and MNC hiring support.
Top-Noted Publication
Among his highly cited works, the article titled “Early Detection of Acute Coronary Syndrome Through Prothrombin Time Measurement Using Flexible UWB Antenna for Cardiac Patient”, published in Biomedical Signal Processing and Control (2022), stands out. With an impact factor of 5.076, this SCI and Scopus-indexed paper bridges RF antenna engineering with medical diagnostics, providing a novel UWB-based method for real-time PT measurement. This paper is a hallmark of Dr. Prasanna’s interdisciplinary vision and is widely referenced in ongoing research in wearable and non-invasive healthcare technologies.
1. Enhanced Blood Prothrombin Time Detection Deploying Flexible Substrate UWB Antenna from Artifacts Removed Pure Plasma Through Statistical Multiple Regression Modelling
Journal: Computers and Electrical Engineering
Date: March 2025
DOI: 10.1016/j.compeleceng.2024.109963
Contributors: R. Prasanna, M. Jenath, M. Vinoth, J. Joseph Ignatious, M. S. Maharajan, P. Banu Priya
Summary: This work demonstrates an advanced method for detecting prothrombin time (PT) levels, a crucial parameter for assessing blood clotting, through a flexible substrate UWB antenna. By leveraging statistical multiple regression models, this paper offers a breakthrough in improving accuracy and eliminating interference caused by artifacts, achieving more reliable PT detection. This method paves the way for non-invasive, portable medical diagnostic systems for better patient care.
2. Effective Biomedical System for Detecting, Tracking, and Preventing Asymptomatic COVID-19 Patients Non-Invasively Using IoT and Mixed Reality
Journal: International Journal for Multiscale Computational Engineering
Date: 2024
DOI: 10.1615/INTJMULTCOMPENG.2023050009
WOSUID: WOS:001267929500001
Contributors: R. Prasanna, T. Ragupathi, N. Ganesh Kumar, B. Prathaban, S. Aswath, R. Rajesh Kanna
Summary: This innovative biomedical system aims to detect and track asymptomatic COVID-19 cases through non-invasive methods using IoT devices and mixed reality. The system integrates real-time health data monitoring, contact tracing, and predictive modeling to provide a comprehensive early warning system. It offers a promising approach to mitigating the spread of the virus by enabling real-time diagnostics and preventive measures, especially in resource-limited environments.
3. An Early Warning System for Driver Fatigue Detection Using Viola-Jones Over HOG Algorithm
Conference: Intelligent Computing and Control for Engineering and Business Systems (ICCEBS)
Date: 2023
DOI: 10.1109/ICCEBS58601.2023.10448968
WOSUID: INSPEC:24868850
Contributors: B.P. Prathaban, R. Subash, D.F.D. Shahila, M. Jenath, R. Prasanna
Summary: This paper presents an intelligent system for detecting driver fatigue using a combination of the Viola-Jones algorithm and Histogram of Oriented Gradients (HOG). The system uses facial recognition techniques to monitor driver alertness and issue real-time warnings when fatigue is detected. It enhances road safety by providing a proactive solution for preventing accidents due to driver drowsiness.
4. Automated Driving Licensing System Based on Wireless Sensor Networks and IoT
Conference: Intelligent Computing and Control for Engineering and Business Systems (ICCEBS)
Date: 2023
DOI: 10.1109/ICCEBS58601.2023.10448936
WOSUID: INSPEC:24868949
Contributors: R. Prasanna, M. Jenath, B.P. Prathaban, M.H. Masood, H.U. Habiba, R.R. Kanna
Summary: This research introduces an automated driving licensing system that utilizes wireless sensor networks (WSNs) and IoT technologies to assess a driver’s abilities and road safety knowledge. The system measures driver response times, vehicle control skills, and environmental awareness. It has the potential to revolutionize the process of issuing driving licenses, ensuring only qualified drivers are authorized to drive on roads.
5. Automatic Traffic Sign Board Detection from Camera Images Using Deep Learning and Binarization Search Algorithm
Conference: International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI)
Date: 2023
DOI: 10.1109/RAEEUCCI57140.2023.10134376
WOSUID: INSPEC:23202273
Contributors: A. Ashwini, K.E. Purushothaman, B.P. Prathaban, M. Jenath, R. Prasanna
Summary: This paper focuses on automating the detection of traffic signs using deep learning algorithms. The model is trained to recognize and classify traffic sign boards from camera images and uses a binarization search algorithm to enhance detection accuracy under various lighting conditions. The proposed system could improve driver assistance systems, enabling real-time traffic sign recognition and enhancing road safety.