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Dr Hongbo Xie
Associate Investigator
Queensland University of Technology
Hong-Bo Xie is a Vice-Chancellor's Senior Research Fellow/Lecturer at QUT and an Associate Investigator at ACEMS, QUT. He received his Ph.D. degree in Biomedical Engineering from Shanghai Jiao Tong University, Shanghai, China. He has published over 80 research papers including book chapters, peer reviewed journal and conference papers. His research interests include statistical/Bayesian inference, machine learning and deep learning, biomedical signal/image processing, matrix and tensor analysis for signal and image recovery, and array signal processing.
Research Interests:
Bayesian inference
Big data analytics
Biomedical signal/image processing
Machine learning/deep learning
Matrix/Tensor decomposition for signal recovery
Statistical/Bayesian signal processing
Qualifications:
PhD Biomedical Engineering (Shanghai Jiao Tong University)
Projects
Publications
Book Chapters
Xie, H., & Guo T.
(2017). Computational Tools and Techniques for Biomedical Signal Processing.
(Singh, B., Ed.).Computational Tools and Techniques for Biomedical Signal Processing. 100-122. doi: 10.4018/978-1-5225-0660-7.ch005
Invited talks, refereed proceedings and other conference outputs
Turner, I., Xie H.,.Pearcy M., .Grote R., McGrath M., &.Colditz P.
(2018). Can Three-Dimensional Motion Analysis and Fuzzy entropy detect movement differences in General Movement Assessment Categories in the normative infant population?.
World Congress of Biomechanics.
Ji, Y., & Xie H.
(2017). Stationary wavelet and two-directional 2dpca for pattern recognition of electromyographic signal.
Proceedings of the 2017 International Conference on Wavelet Analysis and Pattern Recognition. doi: 10.1109/ICWAPR.2017.8076668
Journal Articles
Ji, Y., & Xie H.
(2017). Generalized Multivariate Singular Spectrum Analysis for Nonlinear Time Series De-Noising and Prediction.
Chinese Physics Letters. 34(12), doi: 10.1088/0256-307X/34/12/120501
Ji, Y., & Xie H.
(2017). Stationary wavelet-based two-directional two-dimensional principal component analysis for EMG signal classification.
Measurement Science Review. 17(3), doi: 10.1515/msr-2017-0015
Mendez-Rebolledo, G., Gatica-Rojas V., Martinez-Valdes E., & Xie H.
(2016). The recruitment order of scapular muscles depends on the characteristics of the postural task.
Journal of Electromyography and Kinesiology. 31, 40-47. doi: 10.1016/j.jelekin.2016.09.001
Jiang, J., & Xie H.
(2016). Denoising nonlinear time series using singular spectrum analysis and fuzzy entropy.
Chinese Physics Letters. 33(10), doi: 10.1088/0256-307X/33/10/100501
Xie, H., Zhou P., Guo T., Sivakumar B., Zhang X., & Dokos S.
(2016). Multiscale two-directional two-dimensional principal component analysis and its application to high-dimensional biomedical signal classification.
IEEE Transactions on Biomedical Engineering. 63(7), 1416-1425. doi: 10.1109/TBME.2015.2436375
Xie, H., Dokos S., Sivakumar B., & Mengersen KL.
(2016). Symplectic geometry spectrum regression for prediction of noisy time series.
Physical Review E. 93(5), doi: 10.1103/PhysRevE.93.052217
Xie, H., Wu J., & Liu L.
(2016). Pattern Classification of High-Dimensional Myoelectric Signals Using Wavelet Two-Directional Two-Dimensional Principal Component Analysis.
2016 8th International Conference on Machine Learning and Computing. doi: 10.18178/ijmlc.2016.6.1.574
