Dr Eric (Gengkun) Wang received his B.Eng. degree in mechanical engineering and his M.Eng. degree in mechatronic engineering from the University of Science and Technology, Beijing, China, in 2006 and 2009, respectively. He completed his PhD in telecommunications engineering from the University of South Queensland, Australia. He has been working as a Post-Doctoral Research Fellow with the School of Electrical and Mechanical, USQ for two and a half years. He has authored more than 20 high quality papers, and participated in many national and international research projects. He was the runner-up in The Asia-Pacific Robot Contest (ABU Robocon) 2006, and received the best paper award from the IEEE Wireless Communications and Networking Conference, Cancun, Mexico, in 2011.

  • CC2511: Embedded Systems Design (Level 2; CNS & TSV)
  • CC3650: Digital Control and Automation (Level 3; CNS)
  • CC4950: Design Project (Level 4; CNS)
  • EE3600: Automatic Control 1 (Level 3; CNS)
  • EE4600: Automatic Control 2 (Level 4; CNS)
  • EG5311: Research Project 1 (Level 5; CNS & TSV)
  • EG5312: Research Project 2 (Level 5; CNS & TSV)
  • Mechanical design
  • Software programming
  • Graphic design
  • 3D imaging and video
  • Light field
  • Virtual reality
  • Telemedicine technology
  • Auto-irrigation
  • Internet of Things
  • Matlab programming
  • Digital control systems
  • Digital system design
  • 2014 to 2016 - Postdoc Research Fellow, University of Southern Queensland (Toowoomba, Australia)
  • 2009 to 2010 - Software Engineer, ZBL Tech. (Beijing, China)
  • 2011 - Best Paper Award at IEEE Wireless Communications and Networking Conference, Cancun, Mexico, in 2011
  • 2006 - Runner-up in The Asia-Pacific Robot Contest (ABU Robocon) 2006

These are the most recent publications associated with this author. To see a detailed profile of all publications stored at JCU, visit ResearchOnline@JCU. Hover over Altmetrics badges to see social impact.

Journal Articles
Conference Papers

ResearchOnline@JCU stores 23+ research outputs authored by Dr Eric Wang from 2010 onwards.

Current Funding

Current and recent Research Funding to JCU is shown by funding source and project.

Australian Government Department of Agriculture, Water and the Environment - National Landcare Program: Smart Farming Partnerships Grant

Climate Smart Sugarcane Irrigation Partnerships (CSSIP)

Indicative Funding
$1,586,884 over 5 years
CSSIP will minimise nutrient runoff, improve soil health and increase wetlands water quality by facilitating the adoption of world-class irrigation practices in sugarcane farming systems. Currently, best practice irrigation is assisted by an Irrigation Decision Support Tool (IDST) that provides evidence-based advice. However, IDSTs have not reached their full potential. Firstly, they do not integrate short to medium term weather forecasts (e.g. weekly to multi-weekly forecasts). Secondly, IDSTs do not operate at a spatial scale relevant to farmers. CSSIP will incorporate the Bureau or Meteorology?s new high-resolution climate model into the Irrigation Decision Support Tool. Thirdly, IDSTs require substantial time in manual data entry, which can be alleviated using real-time monitoring via Internet of Things technologies. This will increase irrigation efficiency, reducing excessive runoff into river systems and onto the Reef, and, will help farmers save water and energy costs.
Bronson Philippa, Yvette Everingham, Eric Wang, Stephen Attard and Wei Xiang in collaboration with Geoff Inman-Bamber, Marian Davis, Andrew Schepen, Brock Dembowski, Peter Larsen and Andres Jaramillo (College of Science & Engineering, AgriTech Solutions, Burdekin Productivity Services, Commonwealth Scientific & Industrial Research Organisation, Department of Agriculture, Fisheries & Forestry, Wilmar Sugar Australia and Sugar Research Australia)
Sugarcane; Internet of Things; Irrigation

QLD Department of Agriculture and Fisheries - Grant

Sex determination of fruit fly pupa using Near Infrared Spectroscopy

Indicative Funding
$119,894 over 2 years
Implementation of an effective sterile insect program for fruit fly species Bactrocera tryoni requires that only sterile male insects be released. Therefore at some stage of the fly production process the females need to be removed. Hand sexing is very labour intensive and current automated systems based on colour and size are not effective in differentiating between male and female pupae for B. tryoni. Hence, a non-destructive, rapid method of sex separation is required that does not impact on the viability of the pupae and which can be incorporated into a mass rearing system. Preliminary research has demonstrated that Near Infrared Spectroscopy (NIRS) has great potential as an objective non-invasive method to sex B. tryoni pupae. The aim of this project is to develop and semi-automate a non-invasive rapid assessment technique to rapidly and consistently sort male and female B. tryoni pupae based on NIRS technology.
Bronson Philippa and Eric Wang (College of Science & Engineering)
sterile insect program; near infrared spectroscopy; bactrocera tryoni; Automation

Department of Industry - Innovations Connections

Vacuum solution with an IoT-integrated management system.

Indicative Funding
$49,918 over 1 year, in partnership with LG Utilities Pty Ltd ($49,918)
Vacuum cleaners used in commercial cleaning services have been primarily dominated by corded and cumbersome vacuum cleaners. Although cordless and lightweight vacuum cleaners have been around for domestic users for some time, there ar eno cordless vacuum cleaners designed for commercial cleaning services in the market. In view of this fact, this project aims to develop a new cordless vacuum cleaner for commercial vacuum cleaning services with an IoT-integrated management system.
Eric Wang and Jeremy Gordon (College of Science & Engineering)
Automation; Internet Of Things; Structure design

QLD Department of Environment and Science - Advance Queensland Ignite Ideas Fund - Contract Research

Automated Catch'n'Release Anchor Retrieval Device Reliability Test and Materials Research

Indicative Funding
$18,200 (administered by Kahest Pty Ltd)
The Catch'n'Release Anchor Retrieval Device provides the safest and most sustainable way to retrieve your anchor. Specifically designed for recreational vessels, the device is easy to attach and in just seconds the anchor is pulled up the same way it went down. The device is designed to be used every time you head out on the boat and serves to protect precious marine habitats by minimising damage while promoting a safet boating experience. However, to prove its reliability, the device will have to pass thousands of connect and disconnect test cylces, which is apparantly not viable via manual operation. Therefore, this project aims to develop a solution for automated Catch'n'Release Anchor Retrieval Device reliability test, which can perform the connect-disconnect process automatically thousands of times. On top of that, corrosion and braking tests will be also introduced between every 500 test cycles to simulate the practical usage cases of this device.
Eric Wang (College of Science & Engineering)
Automation; defect testing; Programming logic controller

TCB Innovations Pty Ltd - Contract Research

Virtual Reality 3D First Aid Training Scene Capture Solution

Indicative Funding
$5,500 over 1 year
To provide a much more immersive experience for first aid training, the funding body - Rescue Swag is seeking a solution to replace the conventional 2D first aid scene capture solution. This project is aiming at design a 3D stereoscopic shooting solution using the high definition camera, including both system design, hardware and software solutions. At the end of this project, a system will be experience, and improve the training outcomes.
Wei Xiang and Eric Wang (College of Science & Engineering)
Virtual Reality; First aid training; First person view

Advisory Accreditation: I can be on your Advisory Panel as a Primary or Secondary Advisor.

These Higher Degree Research projects are either current or by students who have completed their studies within the past 5 years at JCU. Linked titles show theses available within ResearchOnline@JCU.

  • Recovering 3D Geometry from Multiple-view Images by Deep Learning: Depth Estimation, View Rendering and Surface Reconstruction (PhD , Secondary Advisor)
  • Decision support system for precision agriculture using deep learning (PhD , Primary Advisor)
  • Explainable AI for Medical Imaging (PhD , Secondary Advisor)
  • Interactive Light-Field Video Compression and Streaming (PhD , Secondary Advisor)

The map shows research collaborations by institution from the past 7 years.
Note: Map points are indicative of the countries or states that institutions are associated with.

  • 5+ collaborations
  • 4 collaborations
  • 3 collaborations
  • 2 collaborations
  • 1 collaboration
  • Indicates the Tropics (Torrid Zone)

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