MCCAIR

Centre for Multimedia Computing,Communications, andArtificial IntelligenceResearch

Established in 2008, the Centre for MultimediaComputing, Communications, and ArtificialIntelligence Research develops innovativeconcepts, technologies, and applications with afocus on information and communicationstechnology

MCCAIR’s research in Artificial Intelligence and Image Processing israted 4 (Above world Standard) in the Excellence in Research forAustralia (ERA) 2012 and 2015 rounds.

federation.edu.au/researchCRICOS Provider No. 00103D

Capabilities

Research at MCCAIR can be broadlycategorised into three key areas:

» Multimedia Signal Processing: Imageprocessing; Multimedia indexing andretrieval; Image and video compression

» Artificial Intelligence: Machine learning;Computational intelligence; Bioinformatics

» Communications and Networking:Wireless communications; Sensornetworks; Cloud computing

Bringing together local and internationalexperts, MCCAIR works closely with leadinguniversities, government agencies, andindustry to improve the digital divide inAustralia by addressing its strategic researchpriorities in Lifting productivity and economicgrowth; Promoting population health andwellbeing; and Cybersecurity.

Key Strengths

» A strong team of long-term researchers capable of conductingboth fundamental research with theoretical underpinning andapplied research with high impact on core ICT issues

» A unique centre in Australia with complementary researchstrengths in three core areas of research with nationalsignificance and priorities

» Proven track record of attracting nationally competitiveCategory 1 research grants and industry-supported researchfunding at national and international levels

» Strong track record of publishing high volume refereedresearch papers in high-impact top-tier journals and premiereconferences

» Exceptional record of attracting high-quality research studentsand successfully supervising their higher degrees by research(HDR) to completion with completion time and rate significantlybetter than the national average

» Well-established research collaboration with some of theleading research groups in top-ranked universities in Australiaand overseas

Current National Competitive Grants

Effective image search and retrieval through automatic image annotationProf G. Lu, Dr D. Zhang, and Dr S. W. Teng | ARC Discovery Project (2013–2015)This project aims to develop an effective and efficient image retrieval system, so that images are retrieved as easily astextual data. The project researches and develops several key know-hows in image retrieval. It enables Australia tomaintain significant advantage in the frontier technology of information processing.

Multiview video coding using cuboid data compressionProf M. Murshed and Dr M. Paul (Charles Sturt University) | ARC Discovery Project (2013–2015)This project investigates novel approaches to multiview video coding that use new data compression techniques and explicitocclusion handling. These new approaches complement the state-of-the-art, improving interactivity with instantaneous viewchange and VCR functionality, reducing encoding complexity, and increasing compression efficiency.

Building change detection and map update using multispectral imagery and height dataDr M. Awrangjeb | ARC Discovery Early Career Researcher Award (2012–2015)This project investigates an effective building change detection procedure and a digital building map. Automatic buildingdetection assists in taking possible precautions during natural disasters, whilst automatic building change detectionfacilitates an effective and efficient management of affected areas during and after the calamity.

Improving management decisions in mental health care through applications of advanced simulation modellingProf G. Meadows, Prof S. Patten, A/Prof J. Kamruzzaman, Dr G. Karmakar, et al. | ARC Linkage Project (2011 – 2015)Evidence to inform management decisions about the best way to structure mental health care services is currently lacking.This project develops a simulation modelling tool that will allow managers to explore various service configurations invirtual reality, enabling predictions to be made about the cost and quality of care.

Solar bio-fuel and carbon sequestration with cyanobacteria: role of genetic networksA/Prof M. Chetty and Prof P. Wangikar (Indian Institute of Technology Mumbai) | Australia-India Strategic Research Fund(2010–2015)Cyanobacteria, also known as blue-green algae, due to their naturally occurring biosynthetic machinery have a greatpotential in energy, carbon sequestration, oxygen production, and nitrogen cycle. Apart from being responsible for naturallysequestering a large part of carbon dioxide from the earth’s atmosphere, cyanobacteria can play a key role in theharvesting of solar energy to create biomass and biofuel. This project investigates an emerging idea to sequester CO2 atsource by using cyanobacteria ponds.

About Us

MCCAIR carries out mostly fundamental theoretical research. It has contributed to the development of innovativeconcepts and technologies in the areas of video coding, multimedia annotation and retrieval, multimodal imageregistration, core machine learning methods, protein structure prediction, wireless sensor networks, Internet-of-Things,cognitive radio, cyber security and privacy, and Cloud resource management.

MCCAIR also emphasises the applied aspects of its theoretical research that have benefited medical imaging, digitaltelevision and video conferencing, energy-efficient communications, anomaly detection, and disaster rescue andrecovery management.

LocationFederation University Australia4N Building, Northways RoadChurchill VIC 3842

Postal AddressFederation University AustraliaPO Box 3191Gippsland Mail Centre VIC 3841

CC_161215

Assoc Prof Joarder KamruzzamanPhD, Muroran Institute of Technology,Japan» Machine learning» Computer networks» Wireless sensor networks

Dr Gour Karmakar, PhDMonash University, Australia» Multimedia signal processing» Wireless communication and social

networks» Opinion mining and big data analytics» Artificial intelligence and simulation

modelling

Professor Manzur Murshed, PhD Australian National University, Australia» Video and image coding, processing,

and understanding

» Communication networks, cyber-security, and privacy-by-design

» Cloud resource management andsimulation

Mr Shane Moore, BCompSci(Hons)Monash University, Australia» Software agents» Component-based software engineering

Dr Dengsheng Zhang, PhD MonashUniversity, Australia» Image classification and retrieval» Audio classification and retrieval» Machine learning» Pattern recognition

Assoc Prof Peter Vamplew PhDUniversity of Tasmania, Australia» Reinforcement learning» Multiobjective decision-making

Professor Kai Ming Ting, PhDSydney University, Australia» Data mining

» Machine learning

» Information retrieval

» Anomaly detection

Professor Guojun Lu, PhDLoughborough University, UK» Image processing

» Image annotation, indexing andretrieval

» Multimedia signal processing

Dr Shyh Wei Teng, PhDMonash University, Australia» Image processing, retrieval and

registration» Machine learning» Bioinformatics

Assoc Prof Madhu Chetty, PhDNagpur University, India» Computational intelligence, evolutionary

optimisation, machine learning» Bioinformatics and computational

biology» Modelling and simulation of large scale

systems

Dr Mortuza Ali, PhDMonash University, Australia» Information theory» Image and video compression» Distributed source coding

Dr Richard Dazeley, PhDUniversity of Tasmania, Australia» Multi-objective reinforcement learning» Assisted reinforcement learning» Ripple down rules» Prudence analysis

Our Researchers

Contact Us

To learn more please contact:

MCCAIRProfessor Manzur MushedCentre Research Director

T: + 61 3 5122 6467E: manzur.murshed@federation.edu.au