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Phung, Son Lam

Professor

  • Faculty of Engineering and Information Sciences
  • School of Electrical, Computer and Telecommunications Engineering
  • Acting Co-Director, Centre for Signal and Information Processing (CSIP)
  • School Research Committee, School Education Committee member
  • UOW Thesis Examination Committee member

Overview


I received B. Eng. with first-class honours and Ph.D. in computer engineering, from Edith Cowan University, Perth, Australia. I was awarded the University Medal in 2000 as the graduating student with the highest course average. 


I have been a Chief Investigator for 14 external  research grants and projects, including three ARC Discovery Projects, one Qatar National Research Fund grant, three Defence Innovation Network Pilot Projects, one Defence Innovation Network PhD Project, two Aus4Innovation projects (DFAT), one AI for Decision Making Initiative project (Office of National Intelligence), one Analytics Lab Program project (Australian Geospatial-Intelligence Organisation), and one NSW Space Research Network Pilot Project.

I have co-supervised 25 HDR students (17 PhD and 8 MPhil) to successful completion, and received Research Paper Awards at 5 international conferences (ICASSP-2016, DICTA-2014, EUVIP-2010, AVSS-2009, ANZIIS-2001). I also received the Highly Commended Supervisor Award at the 2012 Canon Extreme Imaging Competition and the OCTAL Faculty Early Career teaching award in 2010.

At UOW, I am currently serving as a Member of the UOW Thesis Examination Committee, SECTE School Research Committee, and SECTE School Education Committee. Previously, I served as a Member of the EIS Faculty Research Committee in 2013-2021.

Externally, I am serving as an Associate Editor of IEEE Access journal (impact factor: 3.367), and Section Editor (Sensing and Imaging Section) of Sensors journal (IF: 3.576).

Recent publications:

  1. H. T. Le, S. L. Phung, and A. Bouzerdoum, "Bayesian Gabor Network with Uncertainty Estimation for Pedestrian Lane Detection in Assistive Navigation," IEEE Transactions on Circuits and Systems for Video Technology, 2022. (IF = 4.685)
  2. M. H. Phan, T. A. Ta, S. L. Phung, L. T. Tran, A. Bouzerdoum, "Class Similarity Weighted Knowledge Distillation for Continual Semantic Segmentation," IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.
  3. T. D. Truong, Q. H. Bui, C. H. Duong, H. S. Seo, S. L. Phung, X. Li, K. Luu, "
    DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition," IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.
  4. V. H. Tang, A. Bouzerdoum, and S. L. Phung, "Variational Bayesian compressive multichannel indoor radar imaging," IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 9, pp. 7459-7474, 2021. (IF = 5.855)
  5. T. N. A. Nguyen, S. L. Phung, and A. Bouzerdoum, "Hybrid deep learning-Gaussian process network for pedestrian lane detection in unstructured scenes," IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 12, pp-5324-5338, 2020. (IF = 8.793)
  6. H. T. Le, S. L. Phung, and A. Bouzerdoum, "A fast and compact deep Gabor network for micro-Doppler signal processing and human motion classification," IEEE Sensors Journal, vol. 21, no. 20, pp. 23085-23097, 2021. (IF = 3.073)
  7. J. L. Thompson, S. L. Phung, and A. Bouzerdoum, "D-Net: A generalised and optimised deep network for monocular depth estimation," IEEE Access, 2021. (IF = 3.367)
  8. N. P. Le, L. C. Tran, X. Huang, E. Dutkiewicz, S. L. Phung, and A. Bouzerdoum, "Performance analysis of uplink NOMA systems with imperfect SIC detection and delay constraints over composite fading channels," IEEE Transactions on Vehicular Technology, vol. 70, no. 7, pp. 6881-6897, 2021. (IF = 5.379)
  9. M. H. Phan. Q. Nguyen, S. L. Phung, W. E. Zhang, T. D. Vo, Q. Z. Sheng, "CompactNet: A light-weight deep learning framework for smart intrusive load monitoring," IEEE Sensors Journal, vol. 21, no. 22, pp. 25181-25189, 2021. (IF = 3.073)
  10. N. P. Le, L. C. Tran, X. Huang, E. Dutkiewicz, C. Ritz, S. L. Phung, A. Bouzerdoum, D. Franklin, L. Hanzo, "Energy-harvesting aided unmanned aerial vehicles for reliable ground user localization under lognormal-Nakagami-m fading channels," IEEE Transactions on Vehicular Technology, vol. 70, no. 2, pp. 1632-1647, 2021.
  11.  S. T. M. Duong, S. L. Phung, A. Bouzerdoum, S. P. Ang, M. M. Schira, "Correcting susceptibility artifacts of MRI sensors in brain scanning: A 3D anatomy-guided deep learning approach," Sensors, vol. 21, no. 9, pp. 1-16, 2021. 
  12. T. D. Truong, C. N. Duong, N. Le, S. L. Phung, C. Rainwater, K. Luu, "BiMaL: Bijective maximum likelihood approach to domain adaptation in semantic scene segmentation," Proc. Inter. Conf. on Computer Vision (ICCV), 2021.

Top Publications


Research Overview


  • My research is in the areas of image processing, pattern recognition, signal processing, machine learning and artificial intelligence. I focus on developing new algorithms and systems to solve strategic and practical problems in defence, security, healthcare, and industry. 

    I have published over 130 Scopus-listed papers in international journals and conferences. Several publications appear in high-impact journals, including IEEE Trans. Neural Networks and Learning Systems, IEEE. Trans. Image Processing, Pattern Recognition, Artificial Intelligence ReviewIEEE. Trans. Signal Processing, IEEE Trans. Pattern Analysis and Machine Intelligence, IEEE Trans. Geoscience and Remote Sensing. My papers have received 2240+ Scopus citations (h-index: 21), and 3860+ Google Scholar citations (h-index: 27).

    Among several research projects, I and my team are currently developing algorithms and systems for assistive navigation of vision-impaired people, underwater mine detection sonar imaging, vessel classification in satellite images, big crowd analytics, drone image processing for agriculture, and through-wall radar imaging. I am mentoring a group of highly motivated, hard-working, and capable research students.

    I conduct research in the Centre for Signal and Information Processing (CSIP) at the Signals, Information and Communications Research Institute (SICOM). I have been a Conference Organizing Committee member for 5 international conferences, and a peer reviewer for numerous high-impact journals and conferences.

Available as Research Supervisor

Available for Collaborative Projects

Member Of


  • IEEE Senior Member 2014 -

Selected Publications


Investigator On


Other Research Activities


Impact Story


  • <p>Underwater mines are a cost-effective tool in asymmetric warfare and are commonly used to block shipping lanes and restrict naval operations. The aim of this research is develop an automatic system based on sonar imaging for locating naval mines and then recognizing their various types. It involves investigating the latest advances in deep learning and artificial intelligence for object detection, image segmentation, and image classification.<br /><br />This on-going research, supported by the <em>NSW Defence Innovation Network </em>(DIN), is conducted in collaboration with <em>Defence and Science Technology Group</em> (DSTG) and a defence software company. The research team includes Assoc. Prof. Son Lam Phung (<em>CI, Technical Lead, Principal Supervisor</em>), Mr Hoang Thanh Le (PhD Student), Mr Luca Russo (PhD student), Dr. Thi N. A. Nguyen (Associate Research Fellow), Senior Prof. Salim Bouzerdoum (CI), Prof. Christian Ritz (CI), and Dr. Le Chung Tran (CI).<br /><br />In this project, we have developed a deep learning-based tool for underwater mine detection. Further discussions are in progress to apply the project outcomes. </p><p><strong>Publication</strong>: H. T. Le, S. L. Phung, P. B. Chapple, A. Bouzerdoum, C. H. Ritz, L. C. Tran, "<a href="https://ieeexplore.ieee.org/document/9095329?source=authoralert" target="_blank" rel="noopener">Deep Gabor Neural Network for Automatic Detection of Mine-like Objects in Sonar Imagery</a>," <em>IEEE Access</em>, vol. 8, no. 1, pp. 94126-94139, 2020.</p>
  • <p>Over 250 million people worldwide and 380,000 people in Australia suffer from visual impairment. The aim of the project is to develop, based on deep learning and artificial intelligence technologies, a portable electronic tool that enables a vision-impaired user to perform micro-navigation tasks. The targeted tasks include locating the pedestrian path in crowded scenes, evading obstacles and hazards, and recognising relevant landmarks. The technologies developed in this project can be adopted for road safety, self-driving vehicles, and autonomous robots.<br /><br />This project is funded by an ARC Discovery Project DP190100607, titled "<em>Assistive Micro-Navigation for Vision Impaired People"</em> (2019-2021). It is one of five national projects highlighted in the Ministerial Media Release on 27 Nov 2018 <a href="https://www.arc.gov.au/news-publications/media/media-releases/funding-world-leading-research" target="_blank" rel="noopener">(link)</a>. The research team includes blind volunteers, Assoc. Prof. Son Lam Phung (CI), Senior Prof. Salim Bouzerdoum (CI), Dr Thi N. A. Nguyen (Associate Research Fellow), and a number of PhD/MPhil students.</p><p>This on-going research has received the <strong><em>Best Recognition Paper Award</em></strong> at the International Conference on Digital Image Computing: Techniques and Applications (DICTA) in 2014, and the <em><strong>Highly Commended Award</strong></em> at the Canon Extreme Imaging Competition in 2012. Recently, we have reached a project milestone by successfully developing a deep learning-based tool to locate the pedestrian path for the blind. This tool is published in <em>IEEE TNNLS</em> which has an impact factor of 8.793 (top 10% in 136 computer science and AI journals, and top 5% in 266 electrical/electronic engineering journals).</p><p><strong>Publication</strong>: T. N. A. Nguyen, S. L. Phung, and A. Bouzerdoum, "Hybrid deep learning-Gaussian process network for pedestrian lane detection in unstructured scenes," <em>IEEE Transactions on Neural Networks and Learning Systems, </em>2020. <a href="https://ieeexplore.ieee.org/document/8998354" target="_blank" rel="noopener">(URL)</a></p>

Available as Research Supervisor

Potential Supervision Topics


  • - image, video, and signal processing
    - assistive navigation for vision-impaired people, autonomous navigation, self-driving cars
    - machine learning, pattern recognition, neural networks, deep learning
    - imaging applications in defence, security, and healthcare
    - magnetic resonance imaging, atomic force microscopy
    - sonar, hyperspectral, satellite, thermal imaging
    - depth sensing and 3D object scanning
    - through-wall radar imaging, ground-penetrating radar, micro-Doppler radar

Teaching Activities


Advisees


  • Graduate Advising Relationship

    Degree Research Title Advisee
    Doctor of Philosophy Deep Learning Algorithms for Image Segmentation and Generation Ang, Paul
    Doctor of Philosophy Depth Perception from a Single Colour Image Thompson, Joshua
    Doctor of Philosophy Real-time Automatic Target Recognition of Naval Mines with Autonomous Underwater Vehicle Sonar Imaging Russo, Luca
    Doctor of Philosophy Applied Deep Learning for Computer Vision and Scene Understanding Phan, Hieu
    Doctor of Philosophy 3D Face Modelling and Recognition for Automatic Selection of Worker Respiratory Protective Equipment Nguyen, Tran Thanh Phong
    Doctor of Philosophy Lane Detection and Obstacle Sensing for Assistive Micro-Navigation of Blind People Lei, Yunjia
    Doctor of Philosophy Real-time 3D Semantic Scene Segmentation for Autonomous and Assistive Navigation Bui, Ly
    Doctor of Philosophy Multi-Task Deep Learning Scene Perception for Assistive Navigation of Blind People Di, Yang
    Doctor of Philosophy Predicting Cancer Therapy Outcomes with Multimodal Imaging and Deep Learning. Imtiaz, Muhammad Atif

Reviewer Of


Organizer Of Event


Awards And Honors


Teaching Overview


  • Teaching Awards

    - UOW Outstanding Contribution to Teaching and Learning (OCTAL), Faculty of Informatics Early Career Award 2010.

    - Peer Recognition Teaching Award by the Australian College of Educators in 2011.

    - Peer Recognition Teaching Award by the Australian College of Educators in 2007.

    - Nomination for OCTAL 2021, 2017, 2015.

    - Nomination for VC Excellence in Research Supervision Award 2020.

    Teaching Grants

    - Department of Foreign Affairs and Trade New Colombo Plan Mobility Program Grant, "Cross cultural summer school - UOW with IUH, SGU and TTU," W. Susilo, D. Duong, S. L. Phung, C. Chow, P. S. Roy, 2021-2022, $72,6k.

    - UOW Educational Resource Development Agreement, ERDA Project, “Enhancing microcontroller curriculum with video resources,” 2015.

    - UOW Educational Strategic Development Fund (ESDF) grant, "Integrating MATLAB-based experimentation into engineering curriculum,” S. L. Phung, A. Bouzerdoum, M. Ros, 2010, $6k.

    - UOW ESDF grant, "Developing an immersive environment and platform for microcontroller study,” M. Ros, S. L. Phung, D. Stirling, P. Vial, C. Ritz, 2008, $14k.

Keywords


  • image processing, machine learning, artificial intelligence, pattern recognition, signal processing, deep learning, neural networks, radar imaging, magnetic resonance imaging, atomic force microscopy, multispectral imaging, thermal imaging, mine countermeasures.

Mailing Address


  • University of Wollongong, SECTE (EIS)

    Northfields Avenue

    Wollongong

    NSW

    2522

    Australia

Top Publications


Research Overview


  • My research is in the areas of image processing, pattern recognition, signal processing, machine learning and artificial intelligence. I focus on developing new algorithms and systems to solve strategic and practical problems in defence, security, healthcare, and industry. 

    I have published over 130 Scopus-listed papers in international journals and conferences. Several publications appear in high-impact journals, including IEEE Trans. Neural Networks and Learning Systems, IEEE. Trans. Image Processing, Pattern Recognition, Artificial Intelligence ReviewIEEE. Trans. Signal Processing, IEEE Trans. Pattern Analysis and Machine Intelligence, IEEE Trans. Geoscience and Remote Sensing. My papers have received 2240+ Scopus citations (h-index: 21), and 3860+ Google Scholar citations (h-index: 27).

    Among several research projects, I and my team are currently developing algorithms and systems for assistive navigation of vision-impaired people, underwater mine detection sonar imaging, vessel classification in satellite images, big crowd analytics, drone image processing for agriculture, and through-wall radar imaging. I am mentoring a group of highly motivated, hard-working, and capable research students.

    I conduct research in the Centre for Signal and Information Processing (CSIP) at the Signals, Information and Communications Research Institute (SICOM). I have been a Conference Organizing Committee member for 5 international conferences, and a peer reviewer for numerous high-impact journals and conferences.

Member Of


  • IEEE Senior Member 2014 -

Selected Publications


Investigator On


Other Research Activities


Impact Story


  • <p>Underwater mines are a cost-effective tool in asymmetric warfare and are commonly used to block shipping lanes and restrict naval operations. The aim of this research is develop an automatic system based on sonar imaging for locating naval mines and then recognizing their various types. It involves investigating the latest advances in deep learning and artificial intelligence for object detection, image segmentation, and image classification.<br /><br />This on-going research, supported by the <em>NSW Defence Innovation Network </em>(DIN), is conducted in collaboration with <em>Defence and Science Technology Group</em> (DSTG) and a defence software company. The research team includes Assoc. Prof. Son Lam Phung (<em>CI, Technical Lead, Principal Supervisor</em>), Mr Hoang Thanh Le (PhD Student), Mr Luca Russo (PhD student), Dr. Thi N. A. Nguyen (Associate Research Fellow), Senior Prof. Salim Bouzerdoum (CI), Prof. Christian Ritz (CI), and Dr. Le Chung Tran (CI).<br /><br />In this project, we have developed a deep learning-based tool for underwater mine detection. Further discussions are in progress to apply the project outcomes. </p><p><strong>Publication</strong>: H. T. Le, S. L. Phung, P. B. Chapple, A. Bouzerdoum, C. H. Ritz, L. C. Tran, "<a href="https://ieeexplore.ieee.org/document/9095329?source=authoralert" target="_blank" rel="noopener">Deep Gabor Neural Network for Automatic Detection of Mine-like Objects in Sonar Imagery</a>," <em>IEEE Access</em>, vol. 8, no. 1, pp. 94126-94139, 2020.</p>
  • <p>Over 250 million people worldwide and 380,000 people in Australia suffer from visual impairment. The aim of the project is to develop, based on deep learning and artificial intelligence technologies, a portable electronic tool that enables a vision-impaired user to perform micro-navigation tasks. The targeted tasks include locating the pedestrian path in crowded scenes, evading obstacles and hazards, and recognising relevant landmarks. The technologies developed in this project can be adopted for road safety, self-driving vehicles, and autonomous robots.<br /><br />This project is funded by an ARC Discovery Project DP190100607, titled "<em>Assistive Micro-Navigation for Vision Impaired People"</em> (2019-2021). It is one of five national projects highlighted in the Ministerial Media Release on 27 Nov 2018 <a href="https://www.arc.gov.au/news-publications/media/media-releases/funding-world-leading-research" target="_blank" rel="noopener">(link)</a>. The research team includes blind volunteers, Assoc. Prof. Son Lam Phung (CI), Senior Prof. Salim Bouzerdoum (CI), Dr Thi N. A. Nguyen (Associate Research Fellow), and a number of PhD/MPhil students.</p><p>This on-going research has received the <strong><em>Best Recognition Paper Award</em></strong> at the International Conference on Digital Image Computing: Techniques and Applications (DICTA) in 2014, and the <em><strong>Highly Commended Award</strong></em> at the Canon Extreme Imaging Competition in 2012. Recently, we have reached a project milestone by successfully developing a deep learning-based tool to locate the pedestrian path for the blind. This tool is published in <em>IEEE TNNLS</em> which has an impact factor of 8.793 (top 10% in 136 computer science and AI journals, and top 5% in 266 electrical/electronic engineering journals).</p><p><strong>Publication</strong>: T. N. A. Nguyen, S. L. Phung, and A. Bouzerdoum, "Hybrid deep learning-Gaussian process network for pedestrian lane detection in unstructured scenes," <em>IEEE Transactions on Neural Networks and Learning Systems, </em>2020. <a href="https://ieeexplore.ieee.org/document/8998354" target="_blank" rel="noopener">(URL)</a></p>

Potential Supervision Topics


  • - image, video, and signal processing
    - assistive navigation for vision-impaired people, autonomous navigation, self-driving cars
    - machine learning, pattern recognition, neural networks, deep learning
    - imaging applications in defence, security, and healthcare
    - magnetic resonance imaging, atomic force microscopy
    - sonar, hyperspectral, satellite, thermal imaging
    - depth sensing and 3D object scanning
    - through-wall radar imaging, ground-penetrating radar, micro-Doppler radar

Teaching Activities


Advisees


  • Graduate Advising Relationship

    Degree Research Title Advisee
    Doctor of Philosophy Deep Learning Algorithms for Image Segmentation and Generation Ang, Paul
    Doctor of Philosophy Depth Perception from a Single Colour Image Thompson, Joshua
    Doctor of Philosophy Real-time Automatic Target Recognition of Naval Mines with Autonomous Underwater Vehicle Sonar Imaging Russo, Luca
    Doctor of Philosophy Applied Deep Learning for Computer Vision and Scene Understanding Phan, Hieu
    Doctor of Philosophy 3D Face Modelling and Recognition for Automatic Selection of Worker Respiratory Protective Equipment Nguyen, Tran Thanh Phong
    Doctor of Philosophy Lane Detection and Obstacle Sensing for Assistive Micro-Navigation of Blind People Lei, Yunjia
    Doctor of Philosophy Real-time 3D Semantic Scene Segmentation for Autonomous and Assistive Navigation Bui, Ly
    Doctor of Philosophy Multi-Task Deep Learning Scene Perception for Assistive Navigation of Blind People Di, Yang
    Doctor of Philosophy Predicting Cancer Therapy Outcomes with Multimodal Imaging and Deep Learning. Imtiaz, Muhammad Atif

Reviewer Of


Organizer Of Event


Awards And Honors


Teaching Overview


  • Teaching Awards

    - UOW Outstanding Contribution to Teaching and Learning (OCTAL), Faculty of Informatics Early Career Award 2010.

    - Peer Recognition Teaching Award by the Australian College of Educators in 2011.

    - Peer Recognition Teaching Award by the Australian College of Educators in 2007.

    - Nomination for OCTAL 2021, 2017, 2015.

    - Nomination for VC Excellence in Research Supervision Award 2020.

    Teaching Grants

    - Department of Foreign Affairs and Trade New Colombo Plan Mobility Program Grant, "Cross cultural summer school - UOW with IUH, SGU and TTU," W. Susilo, D. Duong, S. L. Phung, C. Chow, P. S. Roy, 2021-2022, $72,6k.

    - UOW Educational Resource Development Agreement, ERDA Project, “Enhancing microcontroller curriculum with video resources,” 2015.

    - UOW Educational Strategic Development Fund (ESDF) grant, "Integrating MATLAB-based experimentation into engineering curriculum,” S. L. Phung, A. Bouzerdoum, M. Ros, 2010, $6k.

    - UOW ESDF grant, "Developing an immersive environment and platform for microcontroller study,” M. Ros, S. L. Phung, D. Stirling, P. Vial, C. Ritz, 2008, $14k.

Keywords


  • image processing, machine learning, artificial intelligence, pattern recognition, signal processing, deep learning, neural networks, radar imaging, magnetic resonance imaging, atomic force microscopy, multispectral imaging, thermal imaging, mine countermeasures.

Mailing Address


  • University of Wollongong, SECTE (EIS)

    Northfields Avenue

    Wollongong

    NSW

    2522

    Australia

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