Dr Muhammad Khalid

Dr Muhammad Khalid

Lecturer/Assistant Professor

Faculty and Department

  • Faculty of Science and Engineering
  • School of Computer Science

Qualifications

  • PhD / DPhil (Northumbria University)
  • FHEA (University of Hull)

Summary

I am actively looking for potential PhD students, please get in touch m.khalid@hull.ac.uk

Muhammad Khalid is currently working as a Lecturer(Assistant Professor) in the school of computer science, University of Hull. Before this, he worked Post-Doctoral Research Fellow at the University of Lincoln, Lincoln. He was responsible for assuring robot autonomy toward safety aspects in the project called MeSAPro. In this project, he ensured robotic safety through human-robot interaction for the robots used in the strawberry collection. Khalid has completed his PhD from Northumbria University in 2020 on Autonomous parking in intelligent smart cities, where he was awarded a fully funded PhD studentship in 2017 under research development framework.

Recent outputs

View more outputs

Journal Article

An Adaptive Flooding Detection Framework with Blockchain Mitigation for Satellite Communications †

Arshad, M., Liu, |., Khalid, |. M., Liu, Y., Wang, P., & Arshad, M. (online). An Adaptive Flooding Detection Framework with Blockchain Mitigation for Satellite Communications †. International Journal of Satellite Communications and Networking, https://doi.org/10.1002/sat.1560

Combined Oriented Data Augmentation Method for Brain MRI Images

Farhan, A. S., Khalid, M., & Manzoor, U. (2025). Combined Oriented Data Augmentation Method for Brain MRI Images. IEEE Access, 13, 9981-9994. https://doi.org/10.1109/ACCESS.2025.3526684

mmFruit: A Contactless and Non-Destructive Approach for Fine-Grained Fruit Moisture Sensing Using Millimeter-Wave Technology

Niaz, F., Zhang, J., Khalid, M., Younas, M., & Niaz, A. (online). mmFruit: A Contactless and Non-Destructive Approach for Fine-Grained Fruit Moisture Sensing Using Millimeter-Wave Technology. IEEE Transactions on Mobile Computing (TMC), https://doi.org/10.1109/TMC.2024.3520914

mm-CUR: A Novel Ubiquitous, Contact-free, and Location-aware Counterfeit Currency Detection in Bundles Using Millimeter-Wave Sensor

Niaz, F., Zhang, J., Zheng, Y., Khalid, M., & Niaz, A. (online). mm-CUR: A Novel Ubiquitous, Contact-free, and Location-aware Counterfeit Currency Detection in Bundles Using Millimeter-Wave Sensor. ACM Transactions on Sensor Networks, https://doi.org/10.1145/3694970

Presentation / Conference Contribution

LLM Based Cross Modality Retrieval to Improve Recommendation Performance

Anwaar, F., Khan, A. M., & Khalid, M. (2024, August). LLM Based Cross Modality Retrieval to Improve Recommendation Performance. Presented at 2024 29th International Conference on Automation and Computing (ICAC), Sunderland, UK

Research interests

Autonomous/Electric vehicles

Autonomous Parking in Future Smart Cities

Applied AI in Autonomy

IoT in Future Smart Cities

AI-based Decision Making in IoT devices

Autonomous Transportation for Emergency Medical Services

Machine Learning in Wireless Networks

Machine Learning in Digital Health

Wireless communication

Robotics

Postgraduate supervision

PhD Students:

1. Ahmeed Farhan, Tumor detection using deep learning.

2. Haylay Hatton, Efficient 3D printing processes.

3. Franky George, GANs using DRL for improved autonomous transportation.

4. Waqas Ahmad RL for emergency autonomous transportation system.

5. Muneeb Anjum, Long term robot autonomy in adverse conditions.

6. Yingxiu Chang, Drone Navigation in GPS denied areas.

External examiner role

PhD Examiner - Lancaster University

2022

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