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Dr MD Moniruzzaman

Adjunct Lecturer

Staff Member Details
Email: m.moniruzzaman@ecu.edu.au
ORCID iD: https://orcid.org/0000-0003-1130-7078

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(2023), 2200439. https://doi.org/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(10), article number 2200439. 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

  • 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.
  • 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.
  • 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).

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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