Dr Yogesh Thakare, Ramdeobaba University, Nagpur, India
Dr. Yogesh Thakare 🎓 is an accomplished researcher and educator in Electronics and Communication Engineering. He earned his Ph.D. (2020) from SGB Amravati University, specializing in DRAM design using submicron technology 💾. Currently an Assistant Professor at Shri Ramdeobaba College of Engineering & Management, Nagpur 👨🏫, he has published in SCIE and Scopus-indexed journals 📑. His research spans FPGA architectures, AI, IoT, and biomedical systems 🤖. A GATE qualifier (94.92%), he has led government-funded projects 💰 and organized AI & IoT workshops 🏗️. Passionate about innovation, he contributes to cutting-edge electronics and computing technologies ⚡.
Publication Profile
Google Scholar
Academic Excellence
Dr. Yogesh Thakare earned his Ph.D. in Electronics Engineering from SGB Amravati University in 2020, focusing on Dynamic Random Access Memory (DRAM) design using submicron technology ⚡🔬. His academic journey reflects excellence, having completed his M.Tech with Distinction (85.10%) 🎓🏆 and his B.E. with First-Class (72.62%) 📚✨. With a strong foundation in electronics and a passion for advanced semiconductor technologies, Dr. Thakare has made significant contributions to memory design and innovation. His expertise in microelectronics and circuit design continues to drive advancements in the field, shaping the future of high-performance computing and digital storage solutions 💡🔍.
Funded Research & Grants
Dr. Yogesh Thakare has demonstrated exceptional research leadership by securing ₹24.6 Lakhs from CSIR for developing an automated water distribution system 💧🔬. His innovative approach aims to enhance water management efficiency through automation, contributing to sustainable resource utilization 🌱💡. This significant funding underscores his expertise in engineering solutions that address real-world challenges 🏗️⚙️. With a strong commitment to technological advancement, Dr. Thakare continues to drive impactful research that promotes water conservation and smart distribution systems 🌍📊. His work not only fosters scientific progress but also supports community welfare by ensuring efficient and equitable water access 🚰✅.
Experience
Dr. Yogesh Thakare is an experienced educator with over 14 years of teaching in top engineering institutes 🏫, including Shri Ramdeobaba College of Engineering and Management, Nagpur. As an Assistant Professor, he has played a key role in shaping technical education 📚. His passion for emerging technologies has led him to organize numerous workshops on Artificial Intelligence 🤖, the Internet of Things 🌐, and Machine Learning 📊, empowering students with cutting-edge knowledge. Through his dedication to academic excellence and innovation, Dr. Thakare continues to inspire the next generation of engineers and researchers 🚀.
Research Focus
Dr. Yogesh Thakare’s research spans electronics, artificial intelligence, IoT, and machine learning 🤖📡. His work includes DRAM memory design 🏗️💾, FPGA-based cryptography 🔐, and deepfake detection using neural networks 🕵️♂️🎭. He has contributed to environmental intelligence systems 🌱📊, weather prediction for agriculture 🌦️🚜, and smart monitoring technologies 📡🏠. Additionally, he has explored cortisol detection for stress monitoring 🧪⚕️ and crime reporting frameworks 🚔📜. His interdisciplinary research integrates hardware and AI-driven solutions, making impactful advancements in computing, security, and human well-being 🔬💡. His innovative approach bridges technology and real-world applications, enhancing automation, safety, and intelligence. 🚀
Publication Top Notes
Intelligent Life Saver System for People Living in Earthquake Zone.
An Effect of Process Variation on 3T-1D DRAM
Analysis of power dissipation in design of capacitorless embedded DRAM
IoT-Enabled Environmental Intelligence: A Smart Monitoring System
Detection of Deepfake Video Using Residual Neural Network and Long Short-Term Memory.
A Read-out Scheme of 1T-1D DRAM Design with Transistor Assisted Decoupled Sensing Amplifier in 7 nm Technology
Enhancing weather prediction and forecasting for agricultural applications using machine learning
FPGA Implementation of Compact Architecture for Lightweight Hash Algorithm for Resource Constrained Devices
Crafting visual art from text: A generative approach
Cortisol Detection Methods for Stress Monitoring: Current Insight and Future Prospect: A Review
An Ensemble Learning with Deep Feature Extraction Approach for Recognition of Traffic Signs in Advanced Driving Assistance Systems
Development and design approach of an sEMG-based Eye movement control system for paralyzed individuals