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Dr MD Moniruzzaman

Lecturer

Staff Member Details
Mobile: 0416 432 649
Email: m.moniruzzaman@ecu.edu.au
Campus: Joondalup  
ORCID iD: https://orcid.org/0000-0003-1130-7078

Dr MD Moniruzzaman is a Lecturer in ECU School of Engineering.

He completed his PhD in Artificial Intelligence, Deep Learning, and Robotics at Edith Cowan University in 2023. Prior to that, he obtained a Master of Science in Computer Science from ECU in 2019 and a Bachelor of Science in Electrical & Electronic Engineering from Khulna University of Engineering & Technology (KUET) in 2010.

His research interests lie in artificial intelligence, deep learning, and image and signal processing in the fields of robotics, electrical engineering, computer vision, and medical diagnostics. He has made contributions to robotics, teleoperation enhancement, and healthcare automation, AI-driven diagnostic tools for cardiovascular diseases with multiple high-impact journal publications.

Dr Moniruzzaman has published in leading journals and conferences, with his research receiving increasing recognition in the scientific community. His publications have been cited extensively, reflecting the impact of his work in artificial intelligence and healthcare.

He is actively involved in academic and professional service, serving as a reviewer for top-tier journals such as IEEE Transactions on Intelligent Vehicles, Robotics & Automation Letters, and Sensors. He is a member of IEEE, IEEE Robotics and Automation Society, IEEE Signal Processing Society, and the IEEE Computer Society.

Research Areas and Interests

  • Deep learning in robotics, electrical engineering and computer vision
  • AI-driven medical diagnostics, signal processing, and sensor-based data analysis
  • Edge computing, automation, and intelligent systems in healthcare and engineering

Qualifications

  • Doctor of Philosophy, Edith Cowan University, 2023.
  • Master of Science (Computer Science), Edith Cowan University, 2019.
  • Bachelor of Science in Electrical & Electronic Engineering, Bangladesh, 2010.

Research Outputs

2024

Conference Publications

  • Islam, KT., Islam, S., Moniruzzaman, M., Ihdayhid, A. (2024). A Hybrid Transformer-Deep Learning Model for Improved Cardiac MRI Left Ventricle Segmentation. Proceedings - 2024 25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024 (435-441). IEEE. https://doi.org/10.1109/DICTA63115.2024.00070.

2023

Journal Articles

  • Moniruzzaman, M., Rassau, A., Chai, D., Islam, S. (2023). Structure-Aware Image Translation-Based Long Future Prediction for Enhancement of Ground Robotic Vehicle Teleoperation. Advanced Intelligent Systems, 5(10), article number 2200439. https://doi.org/10.1002/aisy.202200439.
  • Moniruzzaman, M., Rassau, A., Chai, D., Islam, S. (2023). Long Future Frame Prediction using Optical Flow informed Deep Neural Networks for Enhancement of Robotic Teleoperation in High Latency Environments. Journal of Field Robotics, 40(2), 393-425. https://doi.org/10.1002/rob.22135.
  • Moniruzzaman, M., Rassau, A., Chai, D., Islam, S. (2023). Structure‐Aware Image Translation‐Based Long Future Prediction for Enhancement of Ground Robotic Vehicle Teleoperation. Advanced Intelligent Systems, 5(2023), 2200439. https://doi.org/https://doi.org/10.1002/aisy.202200439.

2022

Journal Articles

  • Moniruzzaman, M., Rassau, A., Chai, D., Islam, S. (2022). High Latency Unmanned Ground Vehicle Teleoperation Enhancement by Presentation of Estimated Future through Video Transformation. Journal of Intelligent and Robotic Systems: theory and applications, 106(2), article number 48. https://doi.org/10.1007/s10846-022-01749-3.
  • Moniruzzaman, M., Rassau, A., Chai, D., Islam, S. (2022). Robotic teleoperation methods and enhancement techniques: A comprehensive survey. Robotics and Autonomous Systems, 150(2022), article number 103973. https://doi.org/10.1016/j.robot.2021.103973.

2019

Conference Publications

  • Moniruzzaman, M., Islam, S. (2019). Evaluation of Different Features and Classifiers for Classification of Rays from Underwater Digital Images. Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2018 (83-90). IEEE. https://doi.org/10.1109/iCMLDE.2018.00025.
  • Moniruzzaman, M., Islam, S., Lavery, P., Bennamoun, M. (2019). Faster R-CNN Based Deep Learning for Seagrass Detection from Underwater Digital Images. Proceedings of Digital Image Computing: Techniques and Application (Article number 8946048). IEEE. https://ro.ecu.edu.au/ecuworkspost2013/7020.

2017

Conference Publications

  • Islam, S., Moniruzzaman, M. (2017). Automatic Ear Detection from Ear and Non-Ear Environment using Deep Machine Learning Technique. Proceedings of the 1st International Conference on Machine Learning and Data Engineering (iCMLDE2017) (108-114).
  • Islam, S., Raza, S., Moniruzzaman, M., Janjua, N., Lavery, P., Al-Jumaily, A. (2017). Automatic Seagrass Detection: A Survey. 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA) (527-532). IEEE. https://doi.org/10.1109/ICECTA.2017.8252036.
  • Moniruzzaman, M., Islam, S., Bennamoun, M., Lavery, P. (2017). Deep Learning on Underwater Marine Object Detection: A Survey. Advanced Concepts for Intelligent Vision Systems 18th International Conference, ACIVS 2017, Antwerp, Belgium, September 18-21, 2017, Proceedings (150-160). Springer. https://doi.org/10.1007/978-3-319-70353-4_13.
  • Moniruzzaman, M., Islam, S. (2017). Automatic Ear Detection from Ear and Non-Ear Environment using DML Technique. Automatic Ear Detection from Ear and Non-Ear Environment using DML Technique (1-6). iCMLDE.
  • Islam, S., K Raza, S., Moniruzzaman, M., Janjua, N., Lavery, P., Al-Jumaily, A. (2017). Automatic seagrass detection: A survey. Automatic seagrass detection: A survey (1-5). IEEE. https://doi.org/10.1109/ICECTA.2017.8252036.

Research Projects

  • Preparing Joondalup City for AVs: Initialising the Necessary Data Capture for Routine Routes, iMOVE Australia Limited, iMOVE CRC, 2025 ‑ 2026, $200,000.
  • Artificial intelligent enhanced stethoscope to improve the diagnosis of Heart Valve Disease (HVD), Department of Health WA, Future Health Research & Innovation Fund - Innovation Fellowship, 2023 ‑ 2024, $147,192.
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