Dr Jasmin Martin
Lecturer in Mechatronic Engineering
School of Mechanical and Mining Engineering
+61 7 336 54673
Researcher biography
My research interests are in change detection for low signal to noise ratio applications in various domains.
I joined the School of Mechanical & Mining Engineering at UQ as a Lecturer in 2023.
I received my PhD from QUT in Robotics and Control in 2019.
Journal Articles
James, Jasmin, Ford, Jason J. and Molloy, Timothy L. (2024). A Framework for Bayesian Quickest Change Detection in General Dependent Stochastic Processes. IEEE Control Systems Letters, 8, 1-1. doi: 10.1109/lcsys.2024.3403918
Ford, Jason J., James, Jasmin and Molloy, Timothy L. (2023). Exactly optimal Bayesian quickest change detection for hidden Markov models. Automatica, 157 111232, 1-5. doi: 10.1016/j.automatica.2023.111232
Ford, Jason J., Kennedy, Justin M., Tompkins, Caitlin, James, Jasmin and Mcfadyen, Aaron (2023). Exactly optimal quickest change detection of Markov chains. IEEE Control Systems Letters, 7, 2749-2754. doi: 10.1109/LCSYS.2023.3288933
Ford, Jason J., James, Jasmin and Molloy, Timothy L. (2020). On the informativeness of measurements in Shiryaev's Bayesian quickest change detection. Automatica, 111, 1-5. doi: 10.1016/j.automatica.2019.108645
James, Jasmin, Ford, Jason J. and Molloy, Timothy L. (2019). Quickest detection of intermittent signals with application to vision-based aircraft detection. IEEE Transactions on Control Systems Technology, 27 (6) 8490112, 2703-2710. doi: 10.1109/tcst.2018.2872468
James, Jasmin, Ford, Jason J. and Molloy, Timothy L. (2018). Learning to detect aircraft for long-range vision-based sense-and-avoid systems. IEEE Robotics and Automation Letters, 3 (4), 4383-4390. doi: 10.1109/lra.2018.2867237
James, Jasmin, Ford, Jason J. and Molloy, Timothy L. (2017). Change detection for undermodelled processes using mismatched hidden Markov model test filters. IEEE Control Systems Letters, 1 (2), 238-243. doi: 10.1109/lcsys.2017.2713825
Conference Papers
Martin, J., Riseley, J. and Ford, J. J. (2022). A dataset of stationary, fixed-wing aircraft on a collision course for vision-based sense and avoid. International Conference on Unmanned Aircraft Systems (ICUAS), Dubrovnik, Croatia, 21-24 June 2022. New York, NY USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/icuas54217.2022.9836180
Hussaini, Somayeh, James, Jasmin and Ford, Jason J. (2020). Vision-based aircraft detection using deep learning with synthetic data. Australasian Conference on Robotics and Automation (ACRA) 2020, Brisbane, QLD Australia, 8-10 December 2020. Canberra, ACT Australia: Australasian Robotics and Automation Association.
James, Jasmin, Ford, Jason J. and Molloy, Timothy L. (2020). A novel technique for rejecting non-aircraft artefacts in above horizon vision-based aircraft detection. International Conference on Unmanned Aircraft Systems (ICUAS), Athens, Greece, 1-4 September 2020. New York, NY USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/icuas48674.2020.9213938
James, Jasmin, Ford, Jason J. and Molloy, Timothy L. (2019). Below horizon aircraft detection using deep learning for vision-based sense and avoid. International Conference on Unmanned Aircraft Systems (ICUAS), Atlanta, GA USA, 11-14 June 2019. New York, NY USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/icuas.2019.8798096
James, Jasmin, Ford, Jason J. and Molloy, Timothy L. (2018). Quickest detection of intermittent signals with estimated anomaly times. 11th Asian Control Conference (ASCC), Gold Coast, QLD Australia, 17-20 December 2017. New York, NY USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/ASCC.2017.8287493
James, Jasmin, Ross, Patrick and Ball, David (2015). Comparison of elastic configurations for energy efficient legged locomotion. Australasian Conference on Robotics and Automation 2015, Canberra, ACT Australia, 2-4 December 2015. Canberra, ACT Australia: Australasian Robotics and Automation Association.