Engineering
Liuxin Chen
- Lecturer
- liuxin.chen@jcu.edu.au
Mohamadreza Chalak Qazani
- Lecturer, Mechanical Engineering
- mohamadreza.chalakqazani@jcu.edu.au
Lewis Gooch
- Lecturer
- lewis.gooch@jcu.edu.au
Judy Yang
- Postdoctoral Research Fellow, Applied AI for Forestry Weed Detection and Mapping
- judy.yang@jcu.edu.au
Bouchra Senadji
- Head, Engineering
- bouchra.senadji@jcu.edu.au
Alzayat Saleh
- AIMS@JCU Postdoctoral Research Fellow, Marine Science Technology
- alzayat.saleh@jcu.edu.au
Anne Steinemann
- Adjunct Professor
- anne.steinemann@jcu.edu.au
Nico Adams
- Professor, Electronic Systems & IoT Engineering
- nico.adams@jcu.edu.au
Elsa Antunes
- Associate Professor, Mechanical Engineering
- elsa.antunes1@jcu.edu.au
Eric Wang
- Senior Lecturer, Electronic Systems and IoT Engineering
- eric.wang@jcu.edu.au
Sugar-AI: Smart Sugarcane Health Monitoring and Ratoon Stunting Disease (RSD) Detection with Satellite Remote Sensing and Machine Learning (Old ID 30057)
Ethan Waters
01 Aug 2024 - 31 Dec 2025
The aim of the Sugar-AI project is to develop a prototype smart software system for sugarcane health monitoring. The prototype will focus on accurately diagnosing Ratoon Stunting Disease (RSD), which is considered by most sugarcane pathologists globally to be the most important disease that impacts on sugarcane yields between 5-60%.
Internet of Things and Big Data Analytics for Cairns Marine (Old ID 26460)
Tao Huang
29 Apr 2019 - 28 Apr 2020
This project will assist to analyse the large repository of data held by Cairns marine in relation to its full operations, i.e., from harvest to husbandry, inventory tracking and sales fulfilment. The physical systems currently in use at Cairns Marine are extensive and complex, involving a range of activities in Northern Australia, all activities on site (including R&D) in Cairns and full product (marine animals) stewardship to destination. A deep and informed understanding of data is required by the business to meet its growing global product commitments.
Internet of Things and Big Data Analytics for Cairns Marine (Old ID 26460)
Mostafa Rahimi Azghadi
29 Apr 2019 - 28 Apr 2020
This project will assist to analyse the large repository of data held by Cairns marine in relation to its full operations, i.e., from harvest to husbandry, inventory tracking and sales fulfilment. The physical systems currently in use at Cairns Marine are extensive and complex, involving a range of activities in Northern Australia, all activities on site (including R&D) in Cairns and full product (marine animals) stewardship to destination. A deep and informed understanding of data is required by the business to meet its growing global product commitments.
NESP 3.4 Better management of catchment runoff to marine receiving environments in northern Australia. (Old ID 28969)
Mohammad Jahanbakht
01 Feb 2023 - 30 Jun 2024
Runoff from catchments in northern Australia has the potential to carry large amounts of sediment and nutrients. These available nutrients are important in driving coastal and estuary productivity, including many commercially and recreationally targeted species. Water resource development in northern Australia could reduce the supply of freshwater flow to the coast, thereby limiting supply of nutrients. This research project will examine the potential risks water resource development presents to Gilbert (QLD), Daly (NT) and Ord (WA) marine and coastal areas. We will complete a literature review, undertake flood plume modelling and examine vegetation damage along these coastal areas.
Identification of benthic structure using machine learning (Old ID 26732)
Bronson Philippa
30 Jan 2020 - 30 Jun 2020
The Project aims at automating the classification of benthic structure and biota from images using machine learning
Millimeter-wave radar for monitoring animals in the airspace (Old ID 28946)
Tao Huang
01 Mar 2023 - 30 Jun 2023
This project aims to develop a prototype of a millimetre-wave imaging radar for the detection of animals in the airspace. These radar systems have been developed for the automotive industry but have yet to be assessed for their capability as a detection tool for biological targets such as insects, birds and bats. There is a huge market gap for low-cost biological sensors in industries such as agriculture, environmental management and conservation, and human health. This project will provide a proof of concept that can then be used to generate further funding and a start-up to further develop this product, with the intention to deploy a network of units as a sensor array.
Millimeter-wave radar for monitoring animals in the airspace (Old ID 28946)
Bronson Philippa
01 Mar 2023 - 30 Jun 2023
This project aims to develop a prototype of a millimetre-wave imaging radar for the detection of animals in the airspace. These radar systems have been developed for the automotive industry but have yet to be assessed for their capability as a detection tool for biological targets such as insects, birds and bats. There is a huge market gap for low-cost biological sensors in industries such as agriculture, environmental management and conservation, and human health. This project will provide a proof of concept that can then be used to generate further funding and a start-up to further develop this product, with the intention to deploy a network of units as a sensor array.
Near-infrared spectroscopy to improve detection of the invasive yellow crazy ant, Anoplolepis gracilipes. (Old ID 28988)
Bronson Philippa
01 Apr 2023 - 30 Apr 2025
This project aims to develop a proof-of-concept device capable of distinguishing the invasive yellow crazy ant from native ants in the Wet Tropics World Heritage Area using a miniaturised near infrared (NIR) spectroscopic sensor. I will conduct an initial study into the variability of the yellow crazy ant NIR spectra then build a classification model to distinguish yellow crazy ants from common native ants using NIR spectra. These studies will be used to inform the prototype development. Prototype development will consist of the design and manufacture of a printed circuit board containing the NIR sensor and its support electronics. This prototype will be then validated using the same classification experiment of YCA against native ants.
Development and characterization of a Novel Zirconia Implant material with enhanced mechanical properties and bio-compatibility (Old ID 27124)
Elsa Antunes
01 Jan 2021 - 31 Dec 2021
Zirconia dental implants have been recently developed as a viable, aesthetic, non-metallic alternative for dental implant fabrication compared to the titanium implants. However, significant challenges have been encountered in acceptance of zirconia implants in clinical practice due to the low fracture resistance inherent within the zirconia material that can lead to structural failure of implant. This has created a need for further research into its crystalline composition and forms to further enhance the mechanical properties of zirconia making it more suitable for dental implants. This study will assess the mechanical and chemical properties of the novel zirconia material developed in our lab and evaluates the effect of various surface modifications on its mechanical properties. Furthermore, cellular interactions that play a vital role in wound healing after placement of implant will be ascertained to promote successful implant osseointegration of novel zirconia dental implants.
SENSOR-Q: Sensor data solutions (Old ID 21566)
Mohan Jacob
14 Nov 2014 - 14 Mar 2017
The SENSOR-Q project brings together world leading technology from industry and academia - data management and analytics expertise from CoastalCOMS (CCOMS), telemetry solutions from Taggle Systems and sensor expertise from James Cook University (JCU), in order to develop a unique end-to-end water monitoring, analysis and management solution for Townsville City Council (TCC). The SENSOR-Q project combines a multi disciplinary collaborative team that aims to create an integrated data collection, storage, analysis and visualisation platform that can be implemented across a variety of applications and industry sectors, and so enable Queensland to more effectively participate in the big data economy.
Distributed Deep Learning in the Cloud and Energy-efficient Real-time Image Processing at the Edge for Fish Segmentation in Underwater Videos Segmentation in Underwater Videos
- 2022
- Institute of Electrical and Electronics Engineers
- Researchers:Mohammad JahanbakhtWei XiangNathan WalthamMostafa Rahimi Azghadi
Start Date:
01 Jan 2010
End Date:
01 Jan 2015
Start Date:
01 Jan 2004
End Date:
01 Jan 2006
Start Date:
01 Jan 2010
Title:
Senior Member, IEEE
Start Date:
01 Jan 2012
Title:
Reperio Innovation Award
Start Date:
01 Jan 2015
Start Date:
01 Jan 2015
Start Date:
01 Jan 2013
Start Date:
01 Jan 2010
Start Date:
01 Jan 2006
Start Date:
01 Jan 2006