Dr. B. Harichandana | Internet of Things | Best Researcher Award

Posted on by ScienceFather

Dr. B. Harichandana | Internet of Things | Best Researcher Award

Dr. B. Harichandana, Srinivasa Ramanujan Institute of Technology, India 

Dr. B. Hari Chandana is an Associate Professor in the Department of Computer Science and Engineering at Srinivasa Ramanujan Institute of Technology (Autonomous), Anantapur, Andhra Pradesh, India. With over 18 years of teaching experience, she has held academic positions at reputed institutions including Sir C.V. Raman Institute of Technology and Sciences and Nalanda Degree College. She earned her Ph.D. in Computer Science and Technology from Sri Krishnadevaraya University, where her research focused on image processing, specifically the recognition of Indian currency based on texture classification using SVM classifiers. She also holds an M.Tech in Information Technology from Karnataka State Open University and an M.Sc. in Computer Science from Sri Krishnadevaraya University. Dr. Chandana has qualified both the UGC-NET and the AP-SET in Computer Science and Applications, reflecting her strong academic proficiency. Passionate about research and emerging technologies, she continues to contribute to the fields of computer science and image processing through teaching, mentoring, and scholarly work.

Professional Profile:

SCOPUS

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award

Dr. B. Hari Chandana, an accomplished academic with over 18 years of teaching and research experience, is a strong and deserving candidate for the Best Researcher Award. Her career reflects a consistent commitment to advancing the fields of Computer Science, Image Processing, and Artificial Intelligence, as well as mentoring the next generation of researchers and professionals.

🎓 Education Qualifications

  • 🧠 Ph.D. in Computer Science & Technology
    📅 2016–2020
    🏫 Sri Krishnadevaraya University, Anantapur, Andhra Pradesh
    📌 Research Area: Image Processing
    📜 Thesis: Security Features of Indian Currency Recognition Based on Texture Classification Using SVM Classifier

  • 💻 M.Tech in Information Technology
    📅 2012–2014
    🏫 Karnataka State Open University, Mysore
    🏅 Division: Distinction (84.12%)

  • 🧑‍💻 M.Sc. in Computer Science
    📅 2004–2006
    🏫 Sri Krishnadevaraya University, Anantapur
    🏅 Division: Distinction (82.4%)

💼 Professional Experience

  • 👩‍🏫 Associate Professor, Dept. of Computer Science & Engineering
    🏫 Srinivasa Ramanujan Institute of Technology (Autonomous), Anantapur, Andhra Pradesh
    📅 2021 – Present

  • 👩‍🏫 Assistant Professor, Dept. of CSE
    🏫 Sir C.V. Raman Institute of Technology and Sciences
    📅 June 2013 – March 2016

  • 👩‍🏫 Lecturer, Dept. of Computer Science
    🏫 Nalanda Degree College, Anantapur
    📅 June 2006 – May 2013

  • 📚 PG Classes Instructor (During Ph.D.)
    🏫 Sri Krishnadevaraya University
    📅 2016–2020

🕰️ Total Teaching Experience: 18+ Years

🏆 Achievements & Qualifications

  • ✅ Qualified NET (National Eligibility Test)
    📅 June 2020
    🖥️ Subject: Computer Science & Applications

  • ✅ Qualified AP-SET (Andhra Pradesh State Eligibility Test)
    📅 2019
    🖥️ Subject: Computer Science & Applications

Publication Top Notes:

CITED:9
CITED:4
CITED:2
CITED:1
CITED:1
CITED:1

 

Mr. Joel Adams | Automation | Best Researcher Award

Posted on by ScienceFather

Mr. Joel Adams | Automation | Best Researcher Award 

Mr. Joel Adams, Florida International University, United States

Joel Adams is a robotics researcher and Ph.D. candidate in Mechanical Engineering at Florida International University, specializing in autonomous mobile and manipulator systems. With extensive experience in radiological surveillance, autonomous mission planning, and multi-robot coordination, he has developed innovative solutions integrating sensor technologies such as LiDAR, depth cameras, and IMUs. His expertise includes robotics middleware (ROS1, ROS2), simulation tools (Gazebo, PyBullet), and advanced programming in C++, Python, and MATLAB. As a Research Assistant at the Applied Research Center since 2019, he has contributed to cutting-edge projects in autonomous system development, multi-robot collaboration, and real-world testing of robotic platforms.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award

Joel Adams appears to be a strong candidate for the Best Researcher Award, particularly if the award recognizes contributions in robotics, autonomous systems, and applied research in radiological surveillance. His work aligns well with advanced robotics, AI-driven mission planning, and real-world applications in nuclear site monitoring.

🎓 Education

  • Florida International University
    • Ph.D. in Mechanical Engineering (Expected Summer 2025) 🎯 (GPA: 3.87)
    • Master of Science in Mechanical Engineering (Summer 2024) 🛠️ (GPA: 3.87)
    • Bachelor of Science in Mechanical Engineering (Honors College) (Fall 2019) 🏅 (GPA: 3.72)
  • Miami Dade College
    • Associate in Arts Degree (Highest Honors) (Summer 2015) 🏆 (GPA: 3.95)

💼 Work Experience

  • Applied Research Center, Florida International University (March 2019 – Present)
    Research Assistant
    • 🚀 Developed autonomous systems for radiological surveillance in nuclear sites, integrating LiDAR, depth cameras, and IMUs.
    • 🧠 Designed multi-robot mission planning solutions using network bridges and behavior-tree-based task allocation.
    • 🛠️ Conducted testing in simulation (Gazebo, PyBullet) and real-world robotic platforms for validation.

🏆 Achievements, Awards & Honors

  • 🎖️ Highest Honors Graduate – Miami Dade College
  • 🏅 Honors College Graduate – Florida International University
  • 🤖 Developed autonomous systems for radiological surveillance, enhancing safety in nuclear environments
  • 🏆 Contributed to multi-robot coordination research, advancing mission planning strategies in robotics
  • 🏅 Published research contributions in robotics intelligence and autonomous system optimization

Publication Top Notes:

A Behavioral Robotics Approach to Radiation Mapping Using Adaptive Sampling