ORCID Profile
0000-0002-3200-2903
Current Organisations
University of Western Australia
,
Edith Cowan University
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Publisher: Springer Science and Business Media LLC
Date: 21-10-2015
Publisher: Elsevier BV
Date: 04-2022
Publisher: MDPI AG
Date: 12-08-2020
DOI: 10.3390/ELECTRONICS9081295
Abstract: The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions.
Publisher: IEEE
Date: 13-12-2022
Publisher: Walter de Gruyter GmbH
Date: 2022
DOI: 10.2478/AOJ-2022-031
Abstract: Introduction: The aim was to assess the relationship between airway morphology and surgical intervention in a cohort of patients presenting with increased body mass index (BMI) and a confirmed diagnosis of obstructive sleep apnoea (OSA). A secondary aim was to revisit the relationship between morphology and OSA severity. Methods: A retrospective analysis was conducted of pre-operative maxillofacial 3D-CT scans of thirty-two patients with a confirmed diagnosis of OSA who received treatment from an ear nose and throat specialist (ENT). Lateral cephalograms were imported into Quick Ceph Studio (Quick Ceph Systems Inc, San Diego, CA, USA) after which linear and angular measurements of selected hard and soft tissues were obtained. 3D-CT images were loaded into the software program 3dMDVultus (3dMD) which permitted 3D visualisation of the airway. Measurements were repeated 3 times on the images of six patients after an interval of two weeks to establish the intraclass correlation coefficient (ICC) for intra-examiner accuracy and reliability. Logistic regression was applied to determine the relationships between morphology, OSA and surgical treatments. Results: A positive correlation was found between age and the apnoea-hypopnea index (AHI). Morphological measurements of the airway did not exhibit a positive relationship with OSA severity. Posterior airway space at the level of the uvula and tongue, the length of the soft palate and position of the hyoid bone were significantly associated with BMI. No variables were found to be correlated with uvulopalatopharyngoplasty (UPPP) surgery. Notwithstanding, airway length and posterior airway space at the level of the uvula tip were significantly associated with tongue channelling. Conclusions: Radiographic airway assessment is an invaluable and opportunistic tool for screening OSA but requires judicial use in its prescription and interpretation. There is little correlation between OSA severity and airway morphology and between surgical intervention and morphology. Additional factors need to be considered before a treatment modality is considered and is best managed in a multidisciplinary setting.
Publisher: IEEE
Date: 29-11-2020
Publisher: Association for Computing Machinery (ACM)
Date: 06-2012
Abstract: Biometric-based human recognition is rapidly gaining popularity due to breaches of traditional security systems and the lowering cost of sensors. The current research trend is to use 3D data and to combine multiple traits to improve accuracy and robustness. This article comprehensively reviews unimodal and multimodal recognition using 3D ear and face data. It covers associated data collection, detection, representation, and matching techniques and focuses on the challenging problem of expression variations. All the approaches are classified according to their methodologies. Through the analysis of the scope and limitations of these techniques, it is concluded that further research should investigate fast and fully automatic ear-face multimodal systems robust to occlusions and deformations.
Publisher: IEEE
Date: 05-2019
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 12-2018
Publisher: Elsevier BV
Date: 03-2013
Publisher: IEEE
Date: 12-2018
Publisher: Wiley
Date: 20-07-2023
Abstract: Predicting future frames through image‐to‐image translation and using these synthetically generated frames for high‐speed ground vehicle teleoperation is a new concept to address latency and enhance operational performance. In the immediate previous work, the image quality of the predicted frames was low and a lot of scene detail was lost. To preserve the structural details of objects and improve overall image quality in the predicted frames, several novel ideas are proposed herein. A filter has been designed to remove noise from dense optical flow components resulting from frame rate inconsistencies. The Pix2Pix base network has been modified and a structure‐aware SSIM‐based perpetual loss function has been implemented. A new dataset of 20 000 training input images and 2000 test input images with a 500 ms delay between the target and input frames has been created. Without any additional video transformation steps, the proposed improved model achieved PSNR of 23.1 SSIM of 0.65 and MS‐SSIM of 0.80, a substantial improvement over our previous work. A Fleiss’ kappa score of .40 (0.48 for the modified network and 0.46 for the perpetual loss function) proves the reliability of the model.
Publisher: Springer International Publishing
Date: 16-10-2015
Publisher: Springer Science and Business Media LLC
Date: 10-2015
DOI: 10.1007/S11517-015-1399-Z
Abstract: Repetitive brief episodes of soft-tissue collapse within the upper airway during sleep characterize obstructive sleep apnea (OSA), an extremely common and disabling disorder. Failure to maintain the patency of the upper airway is caused by the combination of sleep-related loss of compensatory dilator muscle activity and aerodynamic forces promoting closure. The prediction of soft-tissue movement in patient-specific airway 3D mechanical models is emerging as a useful contribution to clinical understanding and decision making. Such modeling requires reliable estimations of the pharyngeal wall pressure forces. While nasal obstruction has been recognized as a risk factor for OSA, the need to include the nasal cavity in upper-airway models for OSA studies requires consideration, as it is most often omitted because of its complex shape. A quantitative analysis of the flow conditions generated by the nasal cavity and the sinuses during inspiration upstream of the pharynx is presented. Results show that adequate velocity boundary conditions and simple artificial extensions of the flow domain can reproduce the essential effects of the nasal cavity on the pharyngeal flow field. Therefore, the overall complexity and computational cost of accurate flow predictions can be reduced.
Publisher: IEEE
Date: 11-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 29-04-2021
DOI: 10.1007/S00521-021-06044-0
Abstract: Deep learning has provided numerous breakthroughs in natural imaging tasks. However, its successful application to medical images is severely handicapped with the limited amount of annotated training data. Transfer learning is commonly adopted for the medical imaging tasks. However, a large covariant shift between the source domain of natural images and target domain of medical images results in poor transfer learning. Moreover, scarcity of annotated data for the medical imaging tasks causes further problems for effective transfer learning. To address these problems, we develop an augmented ensemble transfer learning technique that leads to significant performance gain over the conventional transfer learning. Our technique uses an ensemble of deep learning models, where the architecture of each network is modified with extra layers to account for dimensionality change between the images of source and target data domains. Moreover, the model is hierarchically tuned to the target domain with augmented training data. Along with the network ensemble, we also utilize an ensemble of dictionaries that are based on features extracted from the augmented models. The dictionary ensemble provides an additional performance boost to our method. We first establish the effectiveness of our technique with the challenging ChestXray-14 radiography data set. Our experimental results show more than 50% reduction in the error rate with our method as compared to the baseline transfer learning technique. We then apply our technique to a recent COVID-19 data set for binary and multi-class classification tasks. Our technique achieves 99.49% accuracy for the binary classification, and 99.24% for multi-class classification.
Publisher: IEEE
Date: 10-10-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 12-2019
Publisher: IEEE
Date: 12-2019
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Elsevier BV
Date: 12-2022
Publisher: IEEE
Date: 09-2019
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2010
Publisher: IEEE
Date: 06-2015
Publisher: Elsevier BV
Date: 09-2023
Publisher: Springer Science and Business Media LLC
Date: 10-2022
DOI: 10.1007/S10846-022-01749-3
Abstract: Long-distance, high latency teleoperation tasks are difficult, highly stressful for teleoperators, and prone to over-corrections, which can lead to loss of control. At higher latencies, or when teleoperating at higher vehicle speed, the situation becomes progressively worse. To explore potential solutions, this research work investigates two 2D visual feedback-based assistive interfaces (sliding-only and sliding-and-zooming windows) that apply simple but effective video transformations to enhance teleoperation. A teleoperation simulator that can replicate teleoperation scenarios affected by high and adjustable latency has been developed to explore the effectiveness of the proposed assistive interfaces. Three image comparison metrics have been used to fine-tune and optimise the proposed interfaces. An operator survey was conducted to evaluate and compare performance with and without the assistance. The survey has shown that a 900ms latency increases task completion time by up to 205% for an on-road and 147% for an off-road driving track. Further, the overcorrection-induced oscillations increase by up to 718% with this level of latency. The survey has shown the sliding-only video transformation reduces the task completion time by up to 25.53%, and the sliding-and-zooming transformation reduces the task completion time by up to 21.82%. The sliding-only interface reduces the oscillation count by up to 66.28%, and the sliding-and-zooming interface reduces it by up to 75.58%. The qualitative feedback from the participants also shows that both types of assistive interfaces offer better visual situational awareness, comfort, and controllability, and significantly reduce the impact of latency and intermittency on the teleoperation task.
Publisher: Elsevier BV
Date: 12-2021
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE
Date: 11-2021
Publisher: MDPI AG
Date: 24-06-2020
DOI: 10.3390/S20123578
Abstract: Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications.
Publisher: Bangladesh Journals Online (JOL)
Date: 17-02-2014
Abstract: Abstract not available DOI: 0.3329/updcj.v3i2.17990 Update Dent. Coll. j: 2013 3 (2): 01-02
Publisher: Springer International Publishing
Date: 2017
Publisher: Bangladesh Journals Online (JOL)
Date: 17-02-2014
Abstract: Abstract not available DOI: 0.3329/updcj.v3i1.17976 Update Dent. Coll. j: 2013 3 (1): 01-02
Publisher: IEEE
Date: 2008
Publisher: IEEE
Date: 2009
Publisher: Wiley
Date: 19-12-2023
DOI: 10.1002/ROB.22135
Abstract: High latency in teleoperation has a significant negative impact on operator performance. While deep learning has revolutionized many domains recently, it has not previously been applied to teleoperation enhancement. We propose a novel approach to predict video frames deep into the future using neural networks informed by synthetically generated optical flow information. This can be employed in teleoperated robotic systems that rely on video feeds for operator situational awareness. We have used the image‐to‐image translation technique as a basis for the prediction of future frames. The Pix2Pix conditional generative adversarial network (cGAN) has been selected as a base network. Optical flow components reflecting real‐time control inputs are added to the standard RGB channels of the input image. We have experimented with three data sets of 20,000 input images each that were generated using our custom‐designed teleoperation simulator with a 500‐ms delay added between the input and target frames. Structural Similarity Index Measures (SSIMs) of 0.60 and Multi‐SSIMs of 0.68 were achieved when training the cGAN with three‐channel RGB image data. With the five‐channel input data (incorporating optical flow) these values improved to 0.67 and 0.74, respectively. Applying Fleiss' κ gave a score of 0.40 for three‐channel RGB data, and 0.55 for five‐channel optical flow‐added data. We are confident the predicted synthetic frames are of sufficient quality and reliability to be presented to teleoperators as a video feed that will enhance teleoperation. To the best of our knowledge, we are the first to attempt to reduce the impacts of latency through future frame prediction using deep neural networks.
Publisher: IEEE
Date: 06-09-2021
Publisher: Springer International Publishing
Date: 21-10-2018
Publisher: Springer International Publishing
Date: 2020
Publisher: MDPI AG
Date: 2019
DOI: 10.3390/BDCC3010003
Abstract: Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage.
Publisher: IEEE
Date: 12-2018
Publisher: Springer Netherlands
Date: 2008
Publisher: IEEE
Date: 11-2017
Publisher: IEEE
Date: 11-2021
Publisher: IEEE
Date: 09-12-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 19-09-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Publisher: ACM
Date: 23-08-2017
Publisher: Springer International Publishing
Date: 2020
Publisher: Elsevier BV
Date: 08-2023
Publisher: Springer Science and Business Media LLC
Date: 04-2011
Publisher: IEEE
Date: 12-2018
Publisher: IEEE
Date: 12-2013
DOI: 10.1109/WCSE.2013.55
Publisher: IEEE
Date: 29-11-2020
Publisher: IEEE
Date: 12-2018
Publisher: Springer International Publishing
Date: 2014
Publisher: CRC Press
Date: 27-01-2020
Location: Saudi Arabia
Location: Bangladesh
Start Date: 2016
End Date: 2018
Funder: Edith Cowan University
View Funded ActivityStart Date: 2017
End Date: 2018
Funder: Edith Cowan University
View Funded ActivityStart Date: 2017
End Date: 2017
Funder: Edith Cowan University
View Funded Activity