Mr. SaiTeja Chopparapu | ECE | Best Paper Award

Assistant Professor at St. PETER’S Engineering College, India

Summary:

Mr. SaiTeja Chopparapu is an enthusiastic researcher and educator with expertise in electronics and communication engineering. With a solid academic background and hands-on experience in IoT, sensor systems, and image processing, he is committed to fostering innovation and critical thinking in the field. Known for his dedication and leadership skills, he aims to contribute to technological advancements and create an engaging learning environment for his students.

Professional Profile:

Google Scholar Profile

👩‍🎓Education:

Mr. SaiTeja Chopparapu is a dedicated researcher in Electronics and Communication Engineering, currently in the final stages of his Ph.D. at GITAM University, Visakhapatnam, where he submitted his thesis in October 2023. He earned an M.Tech in Sensor System Technology from Vellore Institute of Technology (VIT), Vellore, in 2019 with a CGPA of 8.49, building upon a B.Tech in Electronics and Communication Engineering from Dhanekula Institute of Engineering and Technology, JNTUK, in 2017. His foundational education includes an Intermediate in MPC from Sri Chaitanya Junior College, Kakinada, where he scored 88.4%, and an SSC from Ratnam High School, Dargamitta, with an aggregate of 84.67%.

🏢 Professional Experience:

Mr. Chopparapu is currently an Assistant Professor at St. Peters Engineering College, JNTUH, where he has been teaching since February 2024. His courses include Digital Electronics, IoT Architecture and Protocols, and Image Processing. With a passion for hands-on education, he has served as a lab in-charge for B.Tech students, mentoring them in programming fundamentals and facilitating discussions to deepen their understanding. His research experience includes a 9-month internship at the Research Centre Imarat (RCI), DRDO, where he developed a GUI for a capacitance-to-voltage converter for capacitive-based sensors. He also completed a 30-day internship at Effectronics Pvt. Ltd., where he was involved in testing and analyzing signaling systems. Over his career, Mr. Chopparapu has participated in more than 40 faculty development programs, covering a variety of topics such as MATLAB, Python, IoT, and technical skill enhancement.

Research Interests:

Mr. Chopparapu’s research interests are centered on sensor systems, IoT, and embedded systems, with an emphasis on IoT architecture and image processing. His dedication to advancing electronics and communication engineering is evident in his academic pursuits, professional roles, and active involvement in technical conferences. He has also expressed a keen interest in developing automation technologies and vehicle control systems.

Top Noted Publication:

“A Hybrid Learning Framework for Multi-Modal Facial Prediction and Recognition Using Improvised Non-Linear SVM Classifier”

Authors: C. SaiTeja, J.B. Seventline
Journal: AIP Advances, Vol. 13, Issue 2, 2023
Citations: 9
Abstract: This study presents a hybrid framework that integrates multi-modal inputs to enhance the accuracy of facial prediction and recognition systems. The framework leverages an improvised non-linear Support Vector Machine (SVM) classifier, optimizing recognition rates through a synergistic use of varied input sources.

“An Efficient Multi-Modal Facial Gesture-Based Ensemble Classification and Reaction to Sound Framework for Large Video Sequences”

Authors: S.T. Chopparapu, J.B. Seventline
Journal: Engineering, Technology & Applied Science Research, Vol. 13, Issue 4, Pages 11263-11270, 2023
Citations: 5
Abstract: This paper introduces a multi-modal ensemble classification framework that combines facial gesture recognition with sound response mechanisms, specifically designed for extensive video sequences. The framework enhances real-time interactions, addressing performance demands in complex multimedia environments.

“Object Detection Using MATLAB, Scilab, and Python”

Authors: S. Chopparapu, B. Seventline
Journal: Technology, Vol. 11, Issue 6, Pages 101-108, 2020
Citations: 5
Abstract: This publication compares the efficacy of object detection across three platforms: MATLAB, Scilab, and Python. By analyzing detection rates, computational efficiency, and implementation complexity, the study provides insights into the adaptability of these platforms for real-time applications.

“GUI for Object Detection Using Voila Method in MATLAB”

Authors: S.T. Chopparapu, D.B. Seventline J
Journal: International Journal of Electrical Engineering and Technology, Vol. 11, Issue 4, 2020
Citations: 4
Abstract: This work introduces a graphical user interface (GUI) for object detection utilizing the Viola-Jones method in MATLAB. The study highlights the GUI’s design features, ease of use, and effectiveness for beginner-level applications in image processing.

“Enhancing Visual Perception in Real-Time: A Deep Reinforcement Learning Approach to Image Quality Improvement”

Authors: S.T. Chopparapu, G. Chopparapu, D. Vasagiri
Journal: Engineering, Technology & Applied Science Research, Vol. 14, Issue 3, Pages 14725-14731, 2024
Citations: 2
Abstract: This paper explores the use of deep reinforcement learning techniques for enhancing real-time image quality. The proposed approach aims to improve visual perception in dynamic environments, with potential applications in surveillance, autonomous vehicles, and other fields requiring high-quality image processing.

Conclusion:

Overall, Mr. SaiTeja Chopparapu presents a strong case for the Best Paper Award. His impactful publications, coupled with his practical experience in IoT and image processing, are aligned with the award’s objective to recognize innovative contributions in ECE. Addressing the suggested areas for improvement could help maximize the visibility and impact of his work in the field. With his impressive background and dedication, he is well-positioned as a competitive candidate for this award.

SaiTeja Chopparapu | ECE | Best Paper Award