SaiTeja Chopparapu | ECE | Best Paper Award

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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:

👩‍🎓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.

 

 

Xiaoxiao Huo | Electric Engineering | Best Researcher Award

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Ms. Xiaoxiao Huo | Electric Engineering | Best Researcher Award

Xiaoxiao Huo at Xiamen University, China

Summary:

Ms. Xiaoxiao Huo is a promising researcher in the field of electrical engineering, currently pursuing her master’s degree at Xiamen University, China. With a strong foundation in power electronics and control theory, she has engaged in various research projects focused on optimizing energy systems and improving power conversion techniques. Ms. Huo is an award-winning student, recognized for her academic excellence and contributions to both research and engineering competitions. She has published several papers on advanced control strategies and holds patents in image recognition technologies for meter reading. Her diverse academic and practical experiences position her as a future leader in energy system optimization and power management.

Professional Profile:

👩‍🎓Education:

Ms. Xiaoxiao Huo is currently pursuing her M.Eng. in Electrical Engineering at Xiamen University (2022-2025, expected), where she maintains a GPA of 3.6/4.0. She is under the supervision of Dr. Po Li (Xiamen University) and Dr. Zhengmao Li (Aalto University). Her primary coursework includes modern control theory, advanced power electronics, and numerical analysis. She completed her B.Eng. in Electrical Engineering and Automation from South China Agricultural University (SCAU) in 2022 with an average score of 90.91/100. Her undergraduate studies focused on power electronics technology, power system analysis, and electrical machinery theory.

🏢 Professional Experience:

Throughout her academic journey, Ms. Huo has engaged in various research and professional roles. Currently, she is conducting research on multi-energy system planning and operation at Aalto University, where she is developing a planning and operation model for integrated energy systems. In addition, at Xiamen University, she is working on power conversion and energy management strategies for power systems, focusing on model predictive control and its applications. In 2022, Ms. Huo gained hands-on experience as an intern and research assistant at the Guangzhou Institute of Energy Testing, where she designed instrument verification methods using computer vision. She has also been an active volunteer with the Chinese Association of Automation (CAA) since 2021, contributing to academic promotion and conference organization. Her participation in robotics competitions, including the Robomaster visual team, further demonstrates her interest in AI and automation.

Research Interests:

Ms. Huo’s research interests lie in the areas of power electronics, control engineering, energy management, and hybrid energy storage systems. Her work focuses on model predictive control, model-free control, observer design, and hardware-in-the-loop (HIL) simulations. She has also contributed to projects involving grid-tied inverters, multi-energy systems, and the optimization of electric power conversion.

Author Metrics:

Ms. Huo has contributed to several significant publications in the fields of energy systems and power electronics. Some of her key works include:

  • Huo, X., & Li, P. (2024). Power Management and Control Strategy Based on Model-Free Control for Hybrid Energy Storage System. Accepted by 2024 9th International Conference on Power and Renewable Energy (ICPRE 2024).
  • Huo, X., & Li, P. Parameter-Free Ultralocal Model-Based Predictive Current Control for Three-Phase Four-Leg Inverters. Submitted to Electric Power Systems Research.
  • Zhang, Y., Huo, X., et al. (2023). Design of an Image Recognition Device for Electronic Water Meter Readings Based on Improved Threading Method. IEEE Chinese Automation Congress (CAC 2023).
  • She is also the co-author of two Chinese patents on image recognition methods for gas and water meter readings.

Top Noted Publication:

Simplified Finite Control Set Model Predictive Control for Single-Phase Grid-Tied Inverters with Twisted Parameters

  • Journal: Electric Power Systems Research
  • Publication Date: January 2025
  • DOI: 10.1016/j.epsr.2024.111063
  • Contributors: Po Li, Xiaoxiao Huo
  • Details: The paper discusses an advanced control method for single-phase grid-tied inverters with twisted parameters, focusing on reducing total harmonic distortion (THD).

Design of an Image Recognition Device for Electronic Water Meter Readings Based on Improved Threading Method

  • Conference: 2023 China Automation Congress (CAC)
  • Publication Date: November 17, 2023
  • DOI: 10.1109/cac59555.2023.10451216
  • Contributors: Yuanming Zhang, Qilun Lu, Xiaoxiao Huo, Yong Wan, Peng Tian
  • Details: This paper focuses on the design of a device utilizing image recognition technology for reading electronic water meters, improving accuracy through an enhanced threading method.

Total Harmonic Distortion Reduction Method of Improved Finite Control Set Model Predictive Control for Single-Phase Inverter with Twisted Parameter

  • Conference: 2023 5th International Conference on Power and Energy Technology (ICPET)
  • Publication Date: July 27, 2023
  • DOI: 10.1109/icpet59380.2023.10367520
  • Contributors: Po Li, Xiaoxiao Huo, Feng Guo
  • Details: This paper presents an improved control method for reducing THD in single-phase inverters with twisted parameters.

Multi-Sampling Rate Finite Control Set Model Predictive Control and Adaptive Method of Single-Phase Inverter

  • Journal: Electronics
  • Publication Date: June 27, 2023
  • DOI: 10.3390/electronics12132848
  • Contributors: Yunfeng She, Xiaoxiao Huo, Xiaoshan Tong, Chunjie Wang, Kunkun Fu
  • Details: The paper discusses multi-sampling rate model predictive control methods for single-phase inverters, including adaptive control to enhance performance.

Research on Quality Control Application of Whole Process Intelligent Manufacturing in Steel Industry 4.0 Based on Big Data Analysis

  • Journal: Journal of Network Intelligence
  • Publication Year: 2022
  • EID: 2-s2.0-85136270844
  • ISSN: 2414-8105
  • Contributors: Zhao, F., Yin, C., Huo, X., Xu, Y.
  • Details: This paper examines the application of big data analysis for quality control in Industry 4.0, focusing on intelligent manufacturing in the steel industry.

E.Laxmi Lydia | Computer Science | Best Researcher Award

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Prof. E.Laxmi Lydia, Computer Science, Best Researcher Award

Professor at Velagapudi Ramakrishna Siddhartha Engineering College, Siddhartha Academy of Higher Education (SAHE), India

Summary:

Dr. E. Laxmi Lydia is a seasoned educator and researcher with over 20 years of experience in teaching, training, and research. Currently serving as a Professor and Dean of R&D at Vignan’s Institute of Information Technology, she has significantly contributed to the academic and research community. Dr. Lydia is recognized for her exceptional interpersonal skills, commitment to student development, and active involvement in curriculum design and institutional accreditation processes. Her extensive expertise and numerous accolades, including the Best Researcher Award for five consecutive years, highlight her dedication to advancing knowledge and fostering innovation in the field of computer science and engineering.

Professional Profile:

👩‍🎓Education:

Ph.D. in Computer Science and Engineering (Year not specified)

Master of Computer Applications (MCA) (Year not specified)

Bachelor of Science (B.Sc.) (Year not specified)

🏢 Professional Experience:

Professor & Dean of R&D Vignan’s Institute of Information Technology (A), July 2019 – Present
Dr. E. Laxmi Lydia leads the Research and Development department, overseeing various research initiatives and fostering collaborations both within and outside the institution. She plays an integral role in curriculum development and significantly contributes to the institution’s growth and academic excellence.

Associate Professor Vignan’s Institute of Information Technology (A), 2015 – 2019
In her tenure as an Associate Professor, Dr. Lydia taught both undergraduate and postgraduate courses, guided numerous research projects, and actively participated in various academic committees. Her efforts were pivotal in advancing the research capabilities and academic standards of the institution.

Associate Professor Raghu Engineering College, 2011 – 2015
At Raghu Engineering College, Dr. Lydia conducted lectures, supervised research activities, and contributed significantly to the academic community through her publications and participation in conferences. Her work helped in elevating the research profile and educational quality of the college.

Assistant Professor Ravindra and Rajendra PG College for MCA, 2003 – 2009
During her tenure as an Assistant Professor, Dr. Lydia was responsible for teaching MCA students, developing comprehensive course materials, and mentoring students. Her dedication to teaching and mentorship helped shape the careers of many students in the field of computer science and engineering.

Honors & Certifications:

  • Oracle Certified (2009)
  • Microsoft Certified Solution Developer (MCSD)
  • Outcome-Based Education (OBE) Certified
  • Engineering Education Certification
  • EPICS-Engineering Projects in Community Services Certified
  • Reviewer for JEET Scopus Journal
  • Best Researcher Award (2018, 2019, 2020, 2021, 2022) at Vignan’s Institute of Information Technology
  • Distinguished Faculty in CSE-2021 by Ambitions, an educational entity

Professional Affiliations:

  • Member of the Board of Studies (BOS) at Vignan’s Institute of Information Technology
  • Member of the International Accreditation Council of Quality Education & Research (IACQER)
  • Member of the Computer Society of India

Research Interests:

Dr. Lydia’s research interests encompass a wide range of topics within computer science and engineering, including but not limited to:

  • Artificial Intelligence and Machine Learning
  • Data Science and Big Data Analytics
  • Cybersecurity and Information Assurance
  • Software Engineering and Development
  • Internet of Things (IoT) and Smart Systems

Top Noted Publication:

An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products

  • Authors: M Sivaram, EL Lydia, IV Pustokhina, DA Pustokhin, M Elhoseny, GP Joshi
  • Journal: IEEE Access
  • Volume: 8
  • Pages: 120321-120330
  • Year: 2020
  • Citations: 97

Concept of Electronic Document Management System (EDMS) as an Efficient Tool for Storing Document

  • Authors: ATR Rosa, IV Pustokhina, EL Lydia, K Shankar, M Huda
  • Journal: Journal of Critical Reviews
  • Volume: 6
  • Issue: 5
  • Pages: 85-90
  • Year: 2019
  • Citations: 91

Synergic Deep Learning Model–Based Automated Detection and Classification of Brain Intracranial Hemorrhage Images in Wearable Networks

  • Author: EL Lydia
  • Journal: Personal and Ubiquitous Computing
  • Year: 2022
  • Citations: 90

Optimal Deep Learning Based Image Compression Technique for Data Transmission on Industrial Internet of Things Applications

  • Authors: B Sujitha, VS Parvathy, EL Lydia, P Rani, Z Polkowski, K Shankar
  • Journal: Transactions on Emerging Telecommunications Technologies
  • Volume: 32
  • Issue: 7
  • Article: e3976
  • Year: 2021
  • Citations: 84

Data Encryption for Internet of Things Applications Based on Catalan Objects and Two Combinatorial Structures

  • Authors: MH Saračević, SZ Adamović, VA Miškovic, M Elhoseny, ND Maček, EL Lydia
  • Journal: IEEE Transactions on Reliability
  • Volume: 70
  • Issue: 2
  • Pages: 819-830
  • Year: 2020
  • Citations: 83

 

Zakria Qadir | Computer Engineering

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Dr. Zakria Qadir: Leading Researcher in Computer Engineering

🎉 Congratulations Dr. Zakria Qadir on Winning the Most Reader’s Article Award! 🏆 Your dedication to research, mentorship, and collaboration with international teams is truly commendable. This award is a testament to your outstanding work and the impact it has on the broader community.

Professional Profile:

🔬 Research Focus: Enthusiastic PostDoc Research Associate at UNSW Artificial Intelligence Institute, dedicated to pushing boundaries in DIGITECH. Research spans Artificial Intelligence, Machine Learning, Wireless Communication, IoT, and Cybersecurity. Highly cited Young STEM Researcher.

🎓 Education:

  • Ph.D. in Electrical and Computer Engineering (Western Sydney University).
    • Research: Smart UAVs for disaster relief, AI, ML, IoT applications.
  • Master’s in Sustainable Environment and Energy System (METU).
    • Thesis: Neural Network-Based Prediction Algorithms for Hybrid PV-Wind System.
  • Bachelor of Science in Electronic Engineering (UET Taxila).
    • Gold Medal Award for securing First Position.

🏆 Achievements:

  • Google Scholar: Citations 1000+, H-Index 17, Total Papers 40, Cumulative Impact Factor >100+.
  • Keynote Speaker at Core A conferences.
  • Fully Funded ARC Research Discovery Scholarship for Ph.D.
  • Various scholarships and awards for academic excellence.

👨‍💻 Professional Experience:

  • Post-Doc Research Associate at UNSW, collaborating with the Department of Defence Australia.
  • Research: Drones-aided AI Algorithms for Battlefield Scenarios.
  • Research Assistant at UNSW, collaborating with Cisco, focusing on Intelligent Transportation Systems.
  • Sessional Lecturer at Victoria University, teaching Data Science, AI, Computer Science, Business Analysis, Networking.
  • Casual Lecturer at Western Sydney University (WSU) and Melbourne Institute of Technology (MIT).
  • Lecturer at National University of Technology, teaching IoT, AI, and Machine Learning.
  • Senior Research Scientist at Imam Abdulrahman Bin Faisal University.
  • Graduate Teaching Assistant at Middle East Technical University (METU).
  • Lab Engineer at National University of Science and Technology (NUST).

Publication Top Noted:

  • Towards 6G Internet of Things: Recent advances, use cases, and open challenges
  • A Hybrid Deep Learning Approach for Bottleneck Detection in IoT
  • A strong construction of S-box using Mandelbrot set an image encryption scheme
  • Resource optimization in UAV-assisted wireless networks—A comprehensive survey
  • Autonomous UAV Path-Planning Optimization Using Metaheuristic Approach for Predisaster Assessment

📚 Skills:

  • Programming Languages: MATLAB, Python, C++.
  • Metaheuristic Algorithms: PSO, ACO, DGBCO, GWO.
  • Machine Learning (AI): Deep learning, Feature Extraction, CNN, FRNN, YOLO.
  • Understanding of Arduino, Raspberry Pi, Proteus, Lucid Chart, VOSViewer, LaTeX.

🎓 Teaching Experience:

  • Lectured and supervised students at various universities.
  • Lesson planning, preparation, and research in diverse areas.

🏅 Honors and Awards:

  • Graduate Teaching Assistant Scholarship at METU.
  • Gold Medal Award for securing First Position in Bachelors.
  • Best Engineering Project Award at UET Taxila.

🏆 Funding and Recognition:

  • Awarded ARC Research Discovery Scholarship, Research Candidate Support Funding, Teaching Assistant Scholarships, Travel Grants, and Research Grants.
  • Recognition from Core A conferences and Australia’s Natural Hazard Research.

 

The paper “A Prototype of an Energy-Efficient MAGLEV Train: A Step Towards Cleaner Train Transport” focuses on the development and evaluation of a prototype Magnetic Levitation (MAGLEV) train with an emphasis on energy efficiency. Below are some key points and important content from the paper:

Abstract:

  • Focus: Development and assessment of an energy-efficient MAGLEV train prototype.
  • Goal: Contributing to cleaner and more sustainable train transportation.

Introduction:

  • Motivation: Addressing the need for environmentally friendly and energy-efficient transportation solutions.
  • Importance of MAGLEV: Highlighting the advantages of MAGLEV technology, such as reduced friction and energy consumption.

Key Features of the MAGLEV Prototype:

  • Energy Efficiency Measures: Description of features and technologies incorporated to enhance energy efficiency.
  • Magnetic Levitation System: Explanation of the MAGLEV technology used in the prototype.
  • Propulsion System: Details about the propulsion mechanism and its role in energy savings.

Performance Evaluation:

  • Energy Consumption Analysis: Quantitative assessment of energy consumption compared to traditional train systems.
  • Environmental Impact: Discussion on the potential reduction in carbon footprint and environmental benefits.

Results and Findings:

  • Energy Savings Percentage: Presentation of the achieved energy savings compared to conventional trains.
  • Operational Stability: Evaluation of the MAGLEV prototype’s stability during operations.

Conclusion:

  • Significance: Emphasizes the significance of developing energy-efficient transportation solutions.
  • Future Implications: Discusses the potential widespread adoption of MAGLEV technology for cleaner and sustainable train transport.

Impact and Citations:

  • Citation Count: Indicates the paper’s impact and recognition within the research community.
  • Reader’s Count: Reflects the broader readership and interest in the paper’s findings.

Innovation and Contribution:

  • Novelty: Highlights any novel approaches, technologies, or methodologies introduced in the MAGLEV prototype.
  • Contribution to the Field: Describes how the research contributes to advancements in cleaner and energy-efficient transportation.

This summary provides a glimpse into the essential content of the paper, focusing on its goals, methodology, findings, and impact on the field of transportation and energy efficiency.