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Tran, Le Chung Dr

PhD, SMIEEE, Humboldtian, WATTLE Fellow, Senior Lecturer

  • Fellow - Alexander von Humboldt (AvH) Foundation
  • Senior IEEE member - The Institute of Electrical and Electronics Engineers (IEEE)
  • School Adviser for International Students - School of Electrical, Computer and Telecommunications Engineering (University) 2017 -
  • Associate Dean of Equity, Diversity and Inclusion (AD EDI) Champion - EIS Faculty 2020 -

Overview


Dr Le Chung Tran received the B. Eng. degree with the first-class honour and highest distinction, and the M. Eng. degree with the highest distinction in Vietnam. He received Ph.D. in telecommunications engineering from University of Wollongong, Australia, in 2006. From 1997 to 2012 he was lecturer at Hanoi University of Communications and Transport, Vietnam. Since 2009, he has been with the University of Wollongong, Australia. He has achieved numerous national and overseas awards, including World University Services (WUS) awards (twice), Vietnamese Government’s doctoral scholarship, International Postgraduate Research Scholarship (IPRS), and the prestigious Alexander von Humboldt (AvH) research fellowships (twice). His research interests include 5G, IoT, multiple-input multiple-output (MIMO) systems, ultra-wideband (UWB) communications, space-time-frequency processing for wireless and mobile communications, cooperative and cognitive communications, software defined radio (SDR), network coding, wireless personal area networks (WPANs) and wireless body area networks (WBANs), digital signal processing for communications.

Top Publications


Research Overview


    • wireless communications, mobile communications, signal processing for communications
    • full duplex communications, 5G systems
    • cooperative, cognitive communications, and network coding
    • multiple-input multiple output (MIMO), massive MIMO, 3D MIMO, space-time-frequency processing
    • software-defined radio (SDR)
    • ultra-wideband communications (UWB)
    • wireless personal area networks (WPANs), wireless body area networks (WBANs)
    • navigation, localisation

Available as Research Supervisor

Available for Collaborative Projects

Selected Publications


Presentations


Featured In


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>
  • <ul><li>CIs: H. Dam, F. Hai, S. L. Phung, K. Maute<i>, </i>and<i> </i><b>L. C. Tran</b></li></ul><ul><li>Tram Chim National Park is a significant and complex wetland of tremendous biodiversity value in Vietnam. It is home to over 230 bird species and 130 fish species, including the endangered Sarus Crane. Continuous monitoring is critical to managing this environment, as rapidly changing hydrology conditions can result in negative outcomes for biodiversity and ecosystem services. Currently, environmental data is collected manually by human rangers and researchers with limited digital resources. The collected data is often inadequate and outdated, and does not correctly reflect the Park’s  ecosystem health. These present serious challenges for making informed decisions and deploying intervention management strategies for the Park’s ecosystem on a daily basis.  This project proposes an AIoT system which helps digitally transform the monitoring of the Park’s complex wetland ecosystems. The system will provide real-time insights into the Park’s ecosystem health to support the decision making and adaptive management of water levels, wildlife and weed control. The system will also offer a clear demonstration of how management interventions impact important metrics of the Park’s ecosystem health.</li></ul><ul><li>More information can be found at <strong><a href="https://documents.uow.edu.au/~lctran/grants_files/TramChim.mp4" target="_blank" rel="noopener">here</a></strong>.</li></ul>
  • <p>CIs: <b>L. C. Tran</b>, C. Ritz, S. Bouzerdoum, S. L. Phung, X. Huang, E. Dutkiewicz, and D. Franklin<br /><br />This project aims to address the challenge that SOCOMD (and the Australian army as well as other civilian areas, such as fishing industry and autonomous transportation systems) is currently facing, namely positioning, navigation and timing without using GPS. The project proposes a novel solution by investigating i) the capability of combining existing RF signals (e.g., WiFi, LTE, television or radio broadcasting, ultrasonic signals) used as RF beacons for non-GPS navigation and positioning; ii) various weighted combinations of existing navigation and positioning techniques, e.g., TDOA, AOA, RSSI, differential Doppler and time reversal, to optimise the localisation precision and accuracy of the RF solution; iii) transmission and deployment techniques of RF beacon signals if the existing RF signals are unavailable or unusable; iv) a new image-based approach to navigation and localisation using deep learning and convolutional neural networks to improve the accuracy; and v) the simplified Network Timing Protocol (NTP) for nodes to retrieve accurate local time.<br /><br />This on-going research, supported by the <em>NSW Defence Innovation Network </em>(DIN), is conducted in collaboration with <i>University of Technology Sydney (UTS)</i>. The research team includes Dr. Le Chung Tran (<em>Technical Lead, Principal Supervisor</em>), Prof. Christian Ritz (CI), A/Prof Son Lam Phung (CI), Senior Prof. Salim Bouzerdoum (CI), Prof Xiaojing Huang (CI, UTS), Prof Eryk Dutkiewicz (CI, UTS), Dr. Daniel Franklin (CI, UTS), Dr. Anh Tuyen Le (Associate Research Fellow), and Dr. Ngoc Phuc Le (Visiting Fellow).</p><p>In this project, we have developed a number of techniques for localisation and positioning with the focus on military applications. Further discussions are in progress to apply the project outcomes. </p><p><strong>Publications</strong>: <br /><br />4. N. P. Le, L. C. Tran, X. Huang, J. Choi, E. Dutkiewicz, C. Ritz, S. L. Phung, and A. Bouzerdoum, "Energy-Harvesting Aided Unmanned Aerial Vehicles for Reliable Ground User Localization Under Lognormal-Nakagami-m Fading Channels," <em>IEEE Transactions on Vehicular Technology</em>. DOI: 10.1109/TVT.2021.3086045.<br /><br /> 3. N. P. Le, L. C. Tran, X. Huang, E. Dutkiewicz, C. Ritz, S. L. Phung, A. Bouzerdoum, D. Franklin, and L. Hanzo, "Energy-Harvesting Aided Unmanned Aerial Vehicles for Reliable Ground User Localization Under Lognormal-Nakagami-m Fading Channels," <i>IEEE Trans. Veh. Technol.</i>, Jan 2021. DOI: 10.1109/TVT.2021.3054987. <br /><br />2. A. T. Le, L. C. Tran, X. Huang, C. Ritz, E. Dutkiewicz, S. L. Phung, S. Bouzerdoum, and D. Franklin, “<a href="https://documents.uow.edu.au/~lctran/publications_files/Sensors%20July%202020.pdf" target="_blank" rel="noopener">Unbalanced Hybrid AOA/RSS Localization for Simplified Wireless Sensor Networks</a>”, <i>Sensors</i>, vol. 20, no. 14, pp. 3838, July 2020.<br /><br />1. A. T. Le, L. C. Tran, X. Huang, C. Ritz, E. Dutkiewicz, S. Bouzerdoum, and D. Franklin, “<a href="https://documents.uow.edu.au/~lctran/publications_files/ICSPCS20.pdf" target="_blank" rel="noopener">Hybrid TOA/AOA Localization with 1D Angle Estimation in UAV-assisted WSN</a>”<i>, Proc. IEEE </i><i>ICSPCS,</i> Adelaide, Australia, 14–16 Dec 2020.</p>
  • <strong>RESEARCH TEAM<br /><br /></strong>P. Perez, <strong>L. C. Tran</strong>, H. Dam, S. L. Phung, J. Barthelemy, R. W. Denagamage, T. Duong, C. Ritz, M. Ros, J. Xi, and F. Safaei<strong><br /><br />CONTEXT</strong><br /><br />Lam Son Sugarcane Joint Stock Company (LASUCO) is one of the largest sugar producers in Vietnam. LASUCO has also diversified into organic agricultural products. In the last five years, LASUCO has invested into high-tech solutions to increase the productivity of its sugar mills and rationalise production. However, Return on Investment has not reached expectations because local sugarcane cultivation is still based on limited mechanisation and there is a lack of digital technologies for real-time monitoring of nutrient deficiencies and early detection of disease outbreaks. There is an urgent need to enhance the competitiveness of Vietnamese sugar industry and protect the livelihood of tens of thousands of farmers.<br /><br /><p><strong>SOLUTION</strong></p><p>The research team at University of Wollongong, Australia, in collaboration with Vietnamese partners, including VIGREEN, LASUCO, Hanoi University of Science and Technology, and Hong Duc University, proposes to design, test, and deploy a technological system that will enable airborne and AI-driven real-time assessments of sugarcane to improve production and support farmers’ livelihood. This AI-driven system will allow LASUCO and local farmers to monitor continuously the nutrition levels and disease infestation of sugarcane fields. The system can display in real-time on an end-user dashboard important information, including soil water level, soil nutrient contents, and plant health.</p>The system captures sensing data from an IoT sensor network, and image data from multispectral cameras on drones. The data will then be analysed using advanced machine learning algorithms. The insightful information of sugarcane will be displayed on an end-user dashboard, which will help LACUSO and farmers make a right decision at a right time, thus optimising their sugarcane cultivation and production. Ultimately, the system will help improve the farming productivity, enhance product quality, reduce production costs, and increase income of the farmers and producers. Upon completion, the system can be extended to other agricultural plants, regions, and sectors (including horticultures and forestry) in Vietnam and other countries.<br /><br /><p><strong>KEY ACTIVITIES/OUTCOMES</strong></p><ol><li>Develop the pioneering AI-driven airborne system to digitally transform the monitoring and assessment processes of sugarcane in LASUCO;</li><li>Transfer the technology to VIGREEN;</li><li>Train the core technical staffs at LASUCO and VIGREEN;</li><li>Demonstrate the system to end-users.</li></ol><p><br />Further information can be found <strong><a href="https://documents.uow.edu.au/~lctran/grants_files/Smart-Eye.mp4" target="_blank" rel="noopener">here</a></strong><strong><a href="https://documents.uow.edu.au/~lctran/grants_files/Smart-Eye.mp4" target="_blank" rel="noopener"> </a></strong>or contact Dr Le Chung Tran (<a href="mailto:lctran@uow.edu.au" target="_blank" rel="noopener">lctran@uow.edu.au</a>)</p>

Available as Research Supervisor

Potential Supervision Topics


    1. 5G
    2. Wireless Body Area Networks
    3. Massive MIMO
    4. Software Defined Radio
    5. Smart Transportation Systems
    6. Navigation and Localisation

Teaching Activities


Advisees


  • Graduate Advising Relationship

    Degree Research Title Advisee
    Doctor of Philosophy Throughput Analysis in Full-Duplex Relaying Systems with Wireless Power Transfer Li, Jiaman
    Doctor of Philosophy Sensor Localization using WBAN Channel Properties Yang, Zanru

Organizer Of Event


Outreach And Community Service Activities


Top Publications


Research Overview


    • wireless communications, mobile communications, signal processing for communications
    • full duplex communications, 5G systems
    • cooperative, cognitive communications, and network coding
    • multiple-input multiple output (MIMO), massive MIMO, 3D MIMO, space-time-frequency processing
    • software-defined radio (SDR)
    • ultra-wideband communications (UWB)
    • wireless personal area networks (WPANs), wireless body area networks (WBANs)
    • navigation, localisation

Selected Publications


Presentations


Featured In


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>
  • <ul><li>CIs: H. Dam, F. Hai, S. L. Phung, K. Maute<i>, </i>and<i> </i><b>L. C. Tran</b></li></ul><ul><li>Tram Chim National Park is a significant and complex wetland of tremendous biodiversity value in Vietnam. It is home to over 230 bird species and 130 fish species, including the endangered Sarus Crane. Continuous monitoring is critical to managing this environment, as rapidly changing hydrology conditions can result in negative outcomes for biodiversity and ecosystem services. Currently, environmental data is collected manually by human rangers and researchers with limited digital resources. The collected data is often inadequate and outdated, and does not correctly reflect the Park’s  ecosystem health. These present serious challenges for making informed decisions and deploying intervention management strategies for the Park’s ecosystem on a daily basis.  This project proposes an AIoT system which helps digitally transform the monitoring of the Park’s complex wetland ecosystems. The system will provide real-time insights into the Park’s ecosystem health to support the decision making and adaptive management of water levels, wildlife and weed control. The system will also offer a clear demonstration of how management interventions impact important metrics of the Park’s ecosystem health.</li></ul><ul><li>More information can be found at <strong><a href="https://documents.uow.edu.au/~lctran/grants_files/TramChim.mp4" target="_blank" rel="noopener">here</a></strong>.</li></ul>
  • <p>CIs: <b>L. C. Tran</b>, C. Ritz, S. Bouzerdoum, S. L. Phung, X. Huang, E. Dutkiewicz, and D. Franklin<br /><br />This project aims to address the challenge that SOCOMD (and the Australian army as well as other civilian areas, such as fishing industry and autonomous transportation systems) is currently facing, namely positioning, navigation and timing without using GPS. The project proposes a novel solution by investigating i) the capability of combining existing RF signals (e.g., WiFi, LTE, television or radio broadcasting, ultrasonic signals) used as RF beacons for non-GPS navigation and positioning; ii) various weighted combinations of existing navigation and positioning techniques, e.g., TDOA, AOA, RSSI, differential Doppler and time reversal, to optimise the localisation precision and accuracy of the RF solution; iii) transmission and deployment techniques of RF beacon signals if the existing RF signals are unavailable or unusable; iv) a new image-based approach to navigation and localisation using deep learning and convolutional neural networks to improve the accuracy; and v) the simplified Network Timing Protocol (NTP) for nodes to retrieve accurate local time.<br /><br />This on-going research, supported by the <em>NSW Defence Innovation Network </em>(DIN), is conducted in collaboration with <i>University of Technology Sydney (UTS)</i>. The research team includes Dr. Le Chung Tran (<em>Technical Lead, Principal Supervisor</em>), Prof. Christian Ritz (CI), A/Prof Son Lam Phung (CI), Senior Prof. Salim Bouzerdoum (CI), Prof Xiaojing Huang (CI, UTS), Prof Eryk Dutkiewicz (CI, UTS), Dr. Daniel Franklin (CI, UTS), Dr. Anh Tuyen Le (Associate Research Fellow), and Dr. Ngoc Phuc Le (Visiting Fellow).</p><p>In this project, we have developed a number of techniques for localisation and positioning with the focus on military applications. Further discussions are in progress to apply the project outcomes. </p><p><strong>Publications</strong>: <br /><br />4. N. P. Le, L. C. Tran, X. Huang, J. Choi, E. Dutkiewicz, C. Ritz, S. L. Phung, and A. Bouzerdoum, "Energy-Harvesting Aided Unmanned Aerial Vehicles for Reliable Ground User Localization Under Lognormal-Nakagami-m Fading Channels," <em>IEEE Transactions on Vehicular Technology</em>. DOI: 10.1109/TVT.2021.3086045.<br /><br /> 3. N. P. Le, L. C. Tran, X. Huang, E. Dutkiewicz, C. Ritz, S. L. Phung, A. Bouzerdoum, D. Franklin, and L. Hanzo, "Energy-Harvesting Aided Unmanned Aerial Vehicles for Reliable Ground User Localization Under Lognormal-Nakagami-m Fading Channels," <i>IEEE Trans. Veh. Technol.</i>, Jan 2021. DOI: 10.1109/TVT.2021.3054987. <br /><br />2. A. T. Le, L. C. Tran, X. Huang, C. Ritz, E. Dutkiewicz, S. L. Phung, S. Bouzerdoum, and D. Franklin, “<a href="https://documents.uow.edu.au/~lctran/publications_files/Sensors%20July%202020.pdf" target="_blank" rel="noopener">Unbalanced Hybrid AOA/RSS Localization for Simplified Wireless Sensor Networks</a>”, <i>Sensors</i>, vol. 20, no. 14, pp. 3838, July 2020.<br /><br />1. A. T. Le, L. C. Tran, X. Huang, C. Ritz, E. Dutkiewicz, S. Bouzerdoum, and D. Franklin, “<a href="https://documents.uow.edu.au/~lctran/publications_files/ICSPCS20.pdf" target="_blank" rel="noopener">Hybrid TOA/AOA Localization with 1D Angle Estimation in UAV-assisted WSN</a>”<i>, Proc. IEEE </i><i>ICSPCS,</i> Adelaide, Australia, 14–16 Dec 2020.</p>
  • <strong>RESEARCH TEAM<br /><br /></strong>P. Perez, <strong>L. C. Tran</strong>, H. Dam, S. L. Phung, J. Barthelemy, R. W. Denagamage, T. Duong, C. Ritz, M. Ros, J. Xi, and F. Safaei<strong><br /><br />CONTEXT</strong><br /><br />Lam Son Sugarcane Joint Stock Company (LASUCO) is one of the largest sugar producers in Vietnam. LASUCO has also diversified into organic agricultural products. In the last five years, LASUCO has invested into high-tech solutions to increase the productivity of its sugar mills and rationalise production. However, Return on Investment has not reached expectations because local sugarcane cultivation is still based on limited mechanisation and there is a lack of digital technologies for real-time monitoring of nutrient deficiencies and early detection of disease outbreaks. There is an urgent need to enhance the competitiveness of Vietnamese sugar industry and protect the livelihood of tens of thousands of farmers.<br /><br /><p><strong>SOLUTION</strong></p><p>The research team at University of Wollongong, Australia, in collaboration with Vietnamese partners, including VIGREEN, LASUCO, Hanoi University of Science and Technology, and Hong Duc University, proposes to design, test, and deploy a technological system that will enable airborne and AI-driven real-time assessments of sugarcane to improve production and support farmers’ livelihood. This AI-driven system will allow LASUCO and local farmers to monitor continuously the nutrition levels and disease infestation of sugarcane fields. The system can display in real-time on an end-user dashboard important information, including soil water level, soil nutrient contents, and plant health.</p>The system captures sensing data from an IoT sensor network, and image data from multispectral cameras on drones. The data will then be analysed using advanced machine learning algorithms. The insightful information of sugarcane will be displayed on an end-user dashboard, which will help LACUSO and farmers make a right decision at a right time, thus optimising their sugarcane cultivation and production. Ultimately, the system will help improve the farming productivity, enhance product quality, reduce production costs, and increase income of the farmers and producers. Upon completion, the system can be extended to other agricultural plants, regions, and sectors (including horticultures and forestry) in Vietnam and other countries.<br /><br /><p><strong>KEY ACTIVITIES/OUTCOMES</strong></p><ol><li>Develop the pioneering AI-driven airborne system to digitally transform the monitoring and assessment processes of sugarcane in LASUCO;</li><li>Transfer the technology to VIGREEN;</li><li>Train the core technical staffs at LASUCO and VIGREEN;</li><li>Demonstrate the system to end-users.</li></ol><p><br />Further information can be found <strong><a href="https://documents.uow.edu.au/~lctran/grants_files/Smart-Eye.mp4" target="_blank" rel="noopener">here</a></strong><strong><a href="https://documents.uow.edu.au/~lctran/grants_files/Smart-Eye.mp4" target="_blank" rel="noopener"> </a></strong>or contact Dr Le Chung Tran (<a href="mailto:lctran@uow.edu.au" target="_blank" rel="noopener">lctran@uow.edu.au</a>)</p>

Potential Supervision Topics


    1. 5G
    2. Wireless Body Area Networks
    3. Massive MIMO
    4. Software Defined Radio
    5. Smart Transportation Systems
    6. Navigation and Localisation

Teaching Activities


Advisees


  • Graduate Advising Relationship

    Degree Research Title Advisee
    Doctor of Philosophy Throughput Analysis in Full-Duplex Relaying Systems with Wireless Power Transfer Li, Jiaman
    Doctor of Philosophy Sensor Localization using WBAN Channel Properties Yang, Zanru

Organizer Of Event


Outreach And Community Service Activities


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