ORCID Profile
0000-0002-3076-0540
Current Organisation
University of Wollongong
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Artificial Intelligence and Image Processing | Pattern Recognition and Data Mining | Computer Vision | Image Processing | Signal Processing | Image Processing | Machine learning not elsewhere classified | Data security and protection | Cybersecurity and privacy | Pattern Recognition |
ANZSRC Socio-Economic Objective (SEO)
Information Processing Services (incl. Data Entry and Capture) | Expanding Knowledge in the Information and Computing Sciences | Combined operations | Data, image and text equipment | Industrial instrumentation |
Related Links
Publications
Variational Bayesian Compressive Multipolarization Indoor Radar Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Susceptibility artifact correction for sub-millimeter fMRI using inverse phase encoding registration and T1 weighted regularization.
Publisher: Elsevier BV
Date: 04-2020
Pedestrian Lane Detection for Assistive Navigation of Vision-Impaired People: Survey and Experimental Evaluation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Signal Classification for Ground Penetrating Radar Using Sparse Kernel Feature Selection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2014
Stochastic variational hierarchical mixture of sparse Gaussian processes for regression
Publisher: Springer Science and Business Media LLC
Date: 06-07-2018
Dynamics of Inter-Molecular Interactions Between Single Aβ42 Oligomeric and Aggregate Species by High-Speed Atomic Force Microscopy
Publisher: Elsevier BV
Date: 07-2019
DOI: 10.1016/J.JMB.2019.04.044
Abstract: Within the amyloid hypothesis in Alzheimer's disease, current focus has shifted to earlier stages of amyloid beta (Aβ) peptide assembly, involving soluble oligomers and smaller aggregates, which are more toxic to cells compared to their morphological distinct fibril forms. Critical to the Aβ field is unlocking the molecular-level kinetic pathways of oligomerization, leading to the culprit subset or specific species of Aβ oligomer populations responsible for the disease etiology. Here, we apply high-speed atomic force microscopy to enable direct visualization of dynamic interactions between single Aβ
A Scalable Hierarchical Gaussian Process Classifier
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Visual descriptors for scene categorization: experimental evaluation
Publisher: Springer Science and Business Media LLC
Date: 17-11-2015
Compressive Radar Imaging of Stationary Indoor Targets With Low-Rank Plus Jointly Sparse and Total Variation Regularizations
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
D-Net: A Generalised and Optimised Deep Network for Monocular Depth Estimation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Object segmentation and classification using 3-D range camera
Publisher: Elsevier BV
Date: 2014
Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning Approach
Publisher: MDPI AG
Date: 26-03-2021
DOI: 10.3390/S21072314
Abstract: Echo planar imaging (EPI), a fast magnetic resonance imaging technique, is a powerful tool in functional neuroimaging studies. However, susceptibility artifacts, which cause misinterpretations of brain functions, are unavoidable distortions in EPI. This paper proposes an end-to-end deep learning framework, named TS-Net, for susceptibility artifact correction (SAC) in a pair of 3D EPI images with reversed phase-encoding directions. The proposed TS-Net comprises a deep convolutional network to predict a displacement field in three dimensions to overcome the limitation of existing methods, which only estimate the displacement field along the dominant-distortion direction. In the training phase, anatomical T1-weighted images are leveraged to regularize the correction, but they are not required during the inference phase to make TS-Net more flexible for general use. The experimental results show that TS-Net achieves favorable accuracy and speed trade-off when compared with the state-of-the-art SAC methods, i.e., TOPUP, TISAC, and S-Net. The fast inference speed (less than a second) of TS-Net makes real-time SAC during EPI image acquisition feasible and accelerates the medical image-processing pipelines.
Hybrid Deep Learning-Gaussian Process Network for Pedestrian Lane Detection in Unstructured Scenes
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Integrating the Projective Transform with Particle Filtering for Visual Tracking
Publisher: Springer Science and Business Media LLC
Date: 2011
DOI: 10.1155/2011/839412
A Pyramidal Neural Network For Visual Pattern Recognition
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2007
CompactNet: A Light-Weight Deep Learning Framework for Smart Intrusive Load Monitoring
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-11-2021
Efficient cost aggregation for feature-vector-based wide-baseline stereo matching
Publisher: Springer Science and Business Media LLC
Date: 11-04-2018
An Unsupervised Deep Learning Technique for Susceptibility Artifact Correction in Reversed Phase-encoding EPI Images
Publisher: Elsevier BV
Date: 09-2020
A Fast and Compact Deep Gabor Network for Micro-Doppler Signal Processing and Human Motion Classification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-10-2021
A Super Descriptor Tensor Decomposition for Dynamic Scene Recognition
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Performance evaluation of non-GPS based localization techniques under shadowing effects
Publisher: MDPI AG
Date: 10-06-2019
DOI: 10.3390/S19112633
Abstract: Non-GPS localization has gained much interest from researchers and industries recently because GPS might fail to meet the accuracy requirements in shadowing environments. The two most common range-based non-GPS localization methods, namely Received Signal Strength Indicator (RSSI) and Angle-of-Arrival (AOA), have been intensively mentioned in the literature over the last decade. However, an in-depth analysis of the weighted combination methods of AOA and RSSI in shadowing environments is still missing in the state-of-the-art. This paper proposes several weighted combinations of the two RSSI and AOA components in the form of pAOA + qRSSI, devises the mathematical model for analyzing shadowing effects, and evaluates these weighted combination localization methods from both accuracy and precision perspectives. Our simulations show that increasing the number of anchors does not necessarily improve the precision and accuracy, that the AOA component is less susceptible to shadowing than the RSSI one, and that increasing the weight of the AOA component and reducing that of the RSSI component help improve the accuracy and precision at high Signal-to-Noise Ratios (SNRs). This observation suggests that some power control algorithm could be used to increase automatically the transmitted power when the channel experiences large shadowing to maintain a high SNR, thus guaranteeing both accuracy and precision of the weighted combination localization techniques.
Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks
Publisher: Elsevier BV
Date: 12-2017
Performance Analysis of Uplink NOMA Systems with Hardware Impairments and Delay Constraints over Composite Fading Channels
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2021
Classification of micro‐Doppler signatures of human motions using log‐Gabor filters
Publisher: Institution of Engineering and Technology (IET)
Date: 12-2015
AdaptorNAS: A New Perturbation-based Neural Architecture Search for Hyperspectral Image Segmentation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks.
Publisher: MDPI AG
Date: 09-07-2020
DOI: 10.3390/S20143838
Abstract: Source positioning using hybrid angle-of-arrival (AOA) estimation and received signal strength indicator (RSSI) is attractive because no synchronization is required among unknown nodes and anchors. Conventionally, hybrid AOA/RSSI localization combines the same number of these measurements to estimate the agents’ locations. However, since AOA estimation requires anchors to be equipped with large antenna arrays and complicated signal processing, this conventional combination makes the wireless sensor network (WSN) complicated. This paper proposes an unbalanced integration of the two measurements, called 1AOA/nRSSI, to simplify the WSN. Instead of using many anchors with large antenna arrays, the proposed method only requires one master anchor to provide one AOA estimation, while other anchors are simple single-antenna transceivers. By simply transforming the 1AOA/1RSSI information into two corresponding virtual anchors, the problem of integrating one AOA and N RSSI measurements is solved using the least square and subspace methods. The solutions are then evaluated to characterize the impact of angular and distance measurement errors. Simulation results show that the proposed network achieves the same level of precision as in a fully hybrid nAOA/nRSSI network with a slightly higher number of simple anchors.
Two-stage through-the-wall radar image formation using compressive sensing
Publisher: SPIE-Intl Soc Optical Eng
Date: 25-01-2013
Pedestrian lane detection in unstructured scenes for assistive navigation
Publisher: Elsevier BV
Date: 08-2016
Sparse Representation of GPR Traces With Application to Signal Classification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2013
Automatic classification of ground-penetrating-radar signals for railway-ballast assessment
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2011
Skin segmentation using color pixel classification: analysis and comparison
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2005
Bayesian Gabor Network With Uncertainty Estimation for Pedestrian Lane Detection in Assistive Navigation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Multipolarization Through-Wall Radar Imaging Using Low-Rank and Jointly-Sparse Representations
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
Probabilistic Fuzzy Image Fusion Approach for Radar Through Wall Sensing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2013
Adaptive hierarchical architecture for visual recognition
Publisher: Optica Publishing Group
Date: 28-01-2010
DOI: 10.1364/AO.49.0000B1
Two-Stage Fuzzy Fusion With Applications to Through-the-Wall Radar Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2013
A Sparse Bayesian Learning Approach for Through-Wall Radar Imaging of Stationary Targets
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Related Organisations
Related Funding Activities
Dynamic Visual Scene Gist Recognition Using A Probabilistic Inference Framework
Start Date: 2014
End Date: 2016
Funder: Australian Research Council
View Funded ActivityAdvanced Processing For Through-the-wall Radar Imaging
Start Date: 2007
End Date: 2011
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP0773879
Start Date: 2007
End Date: 12-2012
Amount: $585,437.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP190100607
Start Date: 2019
End Date: 12-2022
Amount: $389,326.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP140101833
Start Date: 2014
End Date: 12-2017
Amount: $386,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Infrastructure, Equipment And Facilities - Grant ID: LE230100039
Start Date: 2023
End Date: 12-2023
Amount: $440,145.00
Funder: Australian Research Council
View Funded Activity