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Aravinda Rao

I am a Senior Research Fellow at Griffith University and a Research Fellow at the Department of Electrical and Electronic Engineering at The University of Melbourne. I also serve as the Theme Coordinator of Internet of Things Sensors (Construction Tech) at Building 4.0 CRC. As a Senior Member of IEEE, I have focused my research on the fields of Machine Learning, Computer Vision, Deep Learning, and Artificial Intelligence, with a particular emphasis on real-world applications in several key industries.

At Griffith University, my research focuses on applying AI, machine learning, and pattern recognition techniques to blockchain technology and smart contracts, with broader applications in defence, healthcare, smart cities, and manufacturing. This work aims to enhance security, efficiency, and automation in distributed systems through advanced computational intelligence.

At the University of Melbourne, through Building 4.0 CRC projects, I develop computer vision and video analytics solutions for monitoring construction sites to identify safety gaps and protect personnel. This research leverages deep learning techniques to create intelligent monitoring systems that can detect hazards, track compliance with safety protocols, and provide real-time alerts to prevent accidents.

Theme Coordinator - Building 4.0 CRC

Internet of Things Sensors (Construction Tech)

Leading digital innovation initiatives to transform the building industry through IoT sensors and smart technologies.

Digital Innovation IoT Sensors Construction Tech

IEEE Senior Member

Recognized for significant contributions to the field of electrical and electronic engineering through research and professional service.

Professional Recognition Research Excellence Service Contributions

Research Focus

  • Blockchain and Smart Contracts: Applying machine learning and pattern recognition techniques to enhance blockchain systems and smart contracts, focusing on security, verification, and optimization for enterprise applications.
  • Building and Construction Industry: Structural health monitoring using vision-based deep learning techniques, including automated crack detection in concrete and asphalt surfaces using convolutional neural networks and vision transformers.
  • Healthcare and Rehabilitation: Development of wearable technologies for monitoring post-stroke motor recovery and rehabilitation, using accelerometer data and machine learning algorithms.
  • Industry 4.0 and Smart Manufacturing: Network resource allocation and quality of experience optimization for Industry 4.0 applications.
  • Urban Planning and Public Safety: Crowd behavior analysis through video analytics, including crowd density estimation and anomaly detection.