Taste of Research Scholarships

Project Title:	Engineering biosynthetic cell-based systems for treatment of diabetes

Name of Supervisor:	Penny Martens

Email of Supervisor:	p.martens@unsw.edu.au

Name of Joint/Co-Supervisor:	Laura Poole-Warren

Email of Joint/Co-Supervisor:	l.poolewarren@unsw.edu.au

School:	Graduate School of Biomedical Engineering

Faculty Research Area (Theme):	Advanced Materials

School Research Area:	Biomaterials and Tissue Engineering

Applicable to other Engineering schools/disciplines:	Chemical Sciences and Engineering Mechanical & Manufacturing Engineering Sciences – Maths, Physics, Chemistry

Abstract:	The treatment and repair of various human tissues is a complex and important issue that is currently being researched. The encapsulation of islet cells for the treatment of diabetes is the model system the research team is working on, however it is envisioned that the technology can be spread across a variety of tissues. The engineering of a biosynthetic system to treat these diseases has many different critical elements/issues that need to be solved. Key design inputs include the type of extracellular matrix required for cell survival, the oxygen, nutrient and insulin diffusivity of the biosynthetic matrices and the fabrication approaches. Each of these areas needs significant research activity to solve.

Research Environment:	This work is part of an international and multi-disciplinary team and is funded by an ARC Discovery Grant. The Student will work in the Graduate School of Biomedical Engineering with the rest of the team that are working on this project. The team assembled in biomedical engineering has academics, post-docs and research students.

Novelty and Contribution:	This work has the unique aspect of combining biological and synthetic polymers into a co-polymer hydrogel system.

Expected Outcomes:	It is anticipated that the student will produce biosynthetic co-hydrogels, and that substantial characterisation of these hydrogels will occur. Depending on the quality of the results, it is anticipated that a research journal paper could also be generated.

Reference Material Links:	Nilasaroya A, Poole-Warren LA, Whitelock JM, Jo Martens P. (2008) Structural and functional characterisation of poly(vinyl alcohol) and heparin hydrogels. Biomaterials. 35:4658-64.

Project Title:	Fabrication and Characterisation of Silk/PVA copolymer gels

Name of Supervisor:	Penny Martens

Email of Supervisor:	p.martens@unsw.edu.au

Name of Joint/Co-Supervisor:	Laura Poole-Warren

Email of Joint/Co-Supervisor:	l.poolewarren@unsw.edu.au

School:	Graduate School of Biomedical Engineering

Faculty Research Area (Theme):	Advanced Materials

School Research Area:	Biomaterials and Tissue Engineering

Applicable to other Engineering schools/disciplines:	Chemical Sciences and Engineering Mechanical & Manufacturing Engineering Sciences – Maths, Physics, Chemistry

Abstract:	Poly (vinyl alcohol) (PVA) based synthetic gels have many advantageous properties for use in soft-tissue engineering applications. In addition, much research has been undertaken in this lab to gain a basic understanding of the structure and function of PVA gels. However, they do have a limitation – cells don’t like to grow in them. Therefore, this research will involve the incorporation of a series of silk proteins into the base PVA gel. Silk has been shown to encourage cell attachment and proliferation, which would add the needed biological function to our hydrogels. There are many new and exciting aspects to this research, of which the student will have the ability to choose the exact area that interests them the most. This research can include chemical synthesis, hydrogel formulation, mechanical and physical characterisation of the gels, biochemical characterisation of the silks, and cell interaction studies.

Research Environment:	This work is part of an international and multi-disciplinary team and is funded by the Australian Indian Strategic Research Priorities grant. The student will work in the Graduate School of Biomedical Engineering labs in conjunction with other undergraduate and post-graduate students.

Novelty and Contribution:	This work has the unique aspect of combining biological and synthetic polymers into a co-polymer hydrogel system. In addition, this work focuses on using a wild-type silkworm that is only found in India. Comparisons will be made between this unique silk and the more commonly used domesticated silk.

Expected Outcomes:	It is anticipated that the student will produce silk/PVA co-hydrogels, and that substantial characterisation of these hydrogels will occur. Depending on the quality of the results, it is anticipated that a research journal paper could also be generated.

Reference Material Links:	Nilasaroya A, Poole-Warren LA, Whitelock JM, Jo Martens P. (2008) Structural and functional characterisation of poly(vinyl alcohol) and heparin hydrogels. Biomaterials. 35:4658-64. “Silk fibroin protein from mulberry and non-mulberry silkworms: cytotoxicity, biocompatibility and kinetics of L929 murine fibroblast adhesion.” C. Acharya, S. K. Ghosh, S. C. Kundu. J Mater Sci: Mater Med (2008) 19: 2827–2836 “Non-bioengineered silk gland fibroin protein: characterization and evaluation of matrices for potential tissue engineering applications.” B. B. Mandal, S. C. Kundu. Biotechnol Bioeng (2008) 100(6): 1237-50

Project Title:	Growth factor delivery using PVA hydrogels

Name of Supervisor:	Professor John Whitelock

Email of Supervisor:	j.whitelock@unsw.edu.au

Name of Joint/Co-Supervisor:	Dr Penny Martens

Email of Joint/Co-Supervisor:	p.martens@unsw.edu.au

School:	Graduate School of Biomedical Engineering

Faculty Research Area (Theme):	Health & Medical Technologies

School Research Area:	Biomaterials and Tissue Engineering

Applicable to other Engineering schools/disciplines:

Abstract:	The growth of good quality cartilage tissue in the laboratory is still a challenge because we do not fully understand the individual growth signals that prevent the death of cells in 3-dimensional constructs. We have recently identified that fibroblast growth factors interact specifically with an extracellular matrix molecule known as perlecan via its heparin / heparan sulfate side chains. We have developed a hybrid biomaterial that incorporates the mechanical properties of polyvinyl alcohol (PVA) with the biological properties of heparin.

Research Environment:	The project involves working closely with biomaterial and matrix biology researchers within the Graduate School of Biomedical Engineering, consisting of researchers at the undergraduate, postgraduate and postdoctoral level.

Novelty and Contribution:	We are interested in testing this biomaterial to assess whether it in combination with different growth signaling molecules it can stimulate chondrocyte growth and cartilage tissue synthesis.

Expected Outcomes:	An understanding of how growth factors interact with natural, synthetic and hybrid scaffolds and how this might be useful to the successful engineering of cartilage tissue. The candidate will gain valuable experience in polymer synthesis, tissue culture and immunohistochemistry.

Reference Material Links:	Nilasaroya A, Poole-Warren LA, Whitelock JM, Jo Martens P. (2008) Structural and functional characterisation of poly(vinyl alcohol) and heparin hydrogels. Biomaterials. 35:4658-6

Project Title:	Lab-on-a-chip system for analysis of stem cell division trees

Name of Supervisor:	Dr Robert Nordon

Email of Supervisor:	r.nordon@unsw.edu.au

Name of Joint/Co-Supervisor:	Dr Gary Rosengarten

Email of Joint/Co-Supervisor:	g.rosengarten@unsw.edu.au

School:	Graduate School of Biomedical Engineering

Faculty Research Area (Theme):	MEMS, Micro & Nano Technologies

School Research Area:	Biomaterials and Tissue Engineering

Applicable to other Engineering schools/disciplines:	Chemical Sciences and Engineering Computer Science & Engineering Electrical Engineering & Telecommunications Mechanical & Manufacturing Engineering

Abstract:	Techniques for cell culture are labour-intensive and expensive limiting the number of cell cultures that can be maintained and analysed in parallel. We are developing lab-on-a-chip devices for miniaturisation and automation of cell culture and analysis. The microfluidic device will consist of hundreds of indepenent culture experiments that can be analysed in real time using an automated fluorscence microscope incubator. Software will be developed to control scanning of microchambers and tracking of individual cell trajectories and divisions.

Research Environment:	Our lab has one PhD, one Masters by research student, and 4 undergraduate thesis students working on various aspects of this project (Micro Manufacture, Electronics hardware, Image analysis and cell biology). We are also collaborating with Professor Richard Harvey, a leading stem cell scientist at the Victor Chang Cardiac Research Institute (Australian Stem Cell Centre). They wish to understand cardiac stem cell lineage development using live cell division tree analysis.

Novelty and Contribution:	This project offers the opportunity to make a unique contribution to stem cell research by development of a device for high throughput analysis of stem cell division trees

Expected Outcomes:	Working prototype device and publication

Reference Material Links:	Rafael Gomez-Sjoberg Anne A. Leyrat, Dana M. Pirone, Christopher S. Chen, and Stephen R. Quake Versatile, Fully Automated, Microfluidic Cell Culture System Anal. Chem. 2007, 79, 8557-8563

Project Title:	The chitosan bandage - a potent mediator of wound healing

Name of Supervisor:	Dr Megan Lord

Email of Supervisor:	m.lord@unsw.edu.au

Name of Joint/Co-Supervisor:	Professor John Whitelock

Email of Joint/Co-Supervisor:	j.whitelock@unsw.edu.au

School:	Graduate School of Biomedical Engineering

Faculty Research Area (Theme):	Health & Medical Technologies

School Research Area:	Biomaterials and Tissue Engineering

Applicable to other Engineering schools/disciplines:

Abstract:	HemCon Medical Technologies, our industry partner, have developed a chitosan (natural polysaccharide)-based bandage that is able to stop severe bleeding from combat wounds with a success rate of more than 90%. The company has preliminary evidence that the bandage aids wound healing but they do not understand how it functions so they are committed to determine the fundamental mechanisms involved, which is the central aim of this project. It is important to understand how these materials interact with the various cellular and protein components of blood and vascularised tissues in order to develop the next generation of bandages. This project will involve an investigation of blood cell adhesion and activation on the chitosan bandages through the use of cell-based assays and gravimetric techniques.

Research Environment:	The TOR candidate will interact with a team of 10 people undertaking research at the undergraduate, postgraduate and postdoctoral level within the Graduate School of Biomedical Engineering.

Novelty and Contribution:	The project will advance our understanding of the role of chitosan in wound healing which will lead to the development of the next generation of bandage products.

Expected Outcomes:	An understanding of the role of blood cells in chitosan bandage mediated wound healing. The candidate will gain valuable experience in sterile cell culture techniques and cell-based assays.

Reference Material Links:	Chou TC, Fu E, Wu, CJ, Yeh JH. (2003) Chitosan enhances platelet adhesion and aggregation. Biochemical and Biophysical Research Communications 302: 480-483.

Project Title:	Wound Healing Modulation using Nano-Silver

Name of Supervisor:	Dr Megan Lord

Email of Supervisor:	m.lord@unsw.edu.au

Name of Joint/Co-Supervisor:	Dr Wey Yang Teoh, Dr Cindy Gunawan, Prof Rose Amal

Email of Joint/Co-Supervisor:	wy.teoh@unsw.edu.au c.gunawan@unsw.edu.au

School:	Graduate School of Biomedical Engineering

Faculty Research Area (Theme):	Health & Medical Technologies

School Research Area:	Biomaterials and Tissue Engineering

Applicable to other Engineering schools/disciplines:	Chemical Sciences and Engineering

Abstract:	Nano-silver has a potent antimicrobial effect against bacteria, viruses and fungi and is widely used for wound dressings and sterilisation of surgical materials. The use of reversible photo-switchable nano-silver is a new concept to tune the biological properties of the nano-silver using an optical wavelength-selective technique. This project will investigate the use of photo-switchable nano-silver for use in wound dressings through an investigation of the toxicity these nanoparticles with mammalian cells, including fibroblasts and keratinocytes, and possible molecular mechanisms of action.

Research Environment:	The TOR candidate will interact with a team of researchers at the undergraduate, postgraduate and postdoctoral level within the Graduate School of Biomedical Engineering as well as the School of Chemical Sciences and Engineering.

Novelty and Contribution:	This project proposes a new concept in anti-microbial materials through the use of tunable nano-silver which will also advance our fundamental understanding of the interactions of nanoparticles with cells and proteins.

Expected Outcomes:	The project will advance our understanding of the interactions between biological systems and nanoparticles. The candidate will gain valuable experience in sterile cell culture, cell-based assays and biochemical analyses.

Reference Material Links:	Gunawan C, Teoh WY, Marquis CP, Lifia J, Amal R. Reversible Antimicrobial Photoswitching in Nanosilver. 2009 Small 5(3): 341-4.

Physiological Measurement, Modelling and Neurostimulation

Project Title:	FES Cycling - stimulus programming system

Name of Supervisor:	A/Prof Gregg Suaning

Email of Supervisor:	g.suaning@unsw.edu.au

Name of Joint/Co-Supervisor:	Prof. Nigel Lovell

Email of Joint/Co-Supervisor:	n.lovell@unsw.edu.au

School:	Graduate School of Biomedical Engineering

Faculty Research Area (Theme):	Health & Medical Technologies

School Research Area:	Physiological Measurement, Modelling and Neurostimulation

Applicable to other Engineering schools/disciplines:	Computer Science & Engineering Electrical Engineering & Telecommunications Mechanical & Manufacturing Engineering

Abstract:	Through electrical neurostimulation, paraplegics are able to - under their own power - ride a custom cycle over significant distances. The Implantable Bionics group in the Graduate School of Biomedical Engineering has developed the hardware for this to happen, and now seek an easy-to-understand graphical user interface to program the system such that electrical stimulation for leg movements may be coordinated with the crank angle of the cycle.

Research Environment:	Within the laboratories of the Australian Vision Prosthesis Group (Graduate School of Biomedical Engineering)

Novelty and Contribution:	The student will be instrumental in designing and implementing a means through which the electrical stimulators can be programmed.

Expected Outcomes:	By the end of the project, the student will have learned the basics of electrical stimulation of leg muscles, and contributed in a significant way towards enabling paraplegic patients to conduct exercise and to enjoy cycling in the outdoors.

Reference Material Links:	see http://bionic.gsbme.unsw.edu.au

Project Title:	Wireless gyroscope position indicator for human joints

Name of Supervisor:	Prof Nigel Lovell

Email of Supervisor:	N.Lovell@unsw.edu.au

Name of Joint/Co-Supervisor:	A/Prof Gregg Suaning

Email of Joint/Co-Supervisor:	G.Suaning@unsw.edu.au

School:	Graduate School of Biomedical Engineering

Faculty Research Area (Theme):	Health & Medical Technologies

School Research Area:	Physiological Measurement, Modelling and Neurostimulation

Applicable to other Engineering schools/disciplines:	Computer Science & Engineering Electrical Engineering & Telecommunications Mechanical & Manufacturing Engineering

Abstract:	We have a need to sense the position and movement of various body joints for both rehabilitation and neurostimulation purposes. You will be interfacing a gyroscope to a small Bluetooth wireless module and communicating information from the body back to a laptop computer. The computer may also be used to send out commands on a separate Bluetooth channel to control a wireless neurostimulator. This is all good clean fun and we promise that no one will get hurt doing the project

Research Environment:	The work environment will be within the Graduate School of Biomedical Engineering. Assistance will also be provided by engineers from the Biomedical Systems Laboratory situated in the School of Electrical Engineering and Telecommunications and the Prince of Wales Medical Research Institute.

Novelty and Contribution:	The portable gyroscopic sensor is a new sensor just released. Interfacing this with a Bluetooth wireless and using this to communicate in real time limb and body position so that it can be used to control neurostimulation protocols is cutting edge research that only a handful of laboratories around the world are working on.

Expected Outcomes:	A functional device that communicates back to a laptop, body position information.

Reference Material Links:	Work from the group can be found at http://bionic.gsbme.unsw.edu.au and http://bsl.unsw.edu.au

Projects offered by other Engineering Schools that may be of interest are:

Project Title:	Applying lessons from Netflix challenge to image search

Name of Supervisor:	Arcot Sowmya

Email of Supervisor:	sowmya@cse.unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Computer Science and Engineering

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Artificial Intelligence

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Electrical Engineering & Telecommunications

Abstract:	The recently awarded 'The Netflix Prize' of $1m was offered by the DVD rental service Netflix for a new algorithm that would substantially improve the accuracy of predictions about how much someone is going to like a movie based on their existing movie preferences. This has brought about substantial amount of research into predictive modelling on very large realworld datasets. The goal for the summer project will be to study the techniques used by the winning entries and to apply them to the problem of automated image annotation and keyword based image search. This will give students an opportunity to become familiar with some of the techniques used for large scale data mining.

Research Environment:	The research team at CSE contains a senior academic and about 7 PhD students / postdocs. The team has solid experience on machine learning in computer vision, with large applications developed for satellite image analysis, medical imaging, motion tracking and activity recognition. These provide the basis to extend to the proposed project area.

Novelty and Contribution:	Nearest-neighbour based methods have proven to be highly effective for large scale image search. It may be possible to achieve state of the art performance.

Expected Outcomes:	Software implementation and demo / publication

Reference Material Links:	'The million dollar programming prize' - IEEE Spectrum http://www.spectrum.ieee.org/computing/software/the-million-dollar-programming-prize A Makadia, V Pavlovic and S Kumar, 'A New Baseline for Image Annotation', European Conference on Computer Vision, 2008 http://www.cs.rutgers.edu/~vladimir/pub/makadia08eccv.pdf

Project Title:	Multi-level Incremental Knowledge Acquisition for Computer Aided Diagnosis of Lung CT Images

Name of Supervisor:	Arcot Sowmya

Email of Supervisor:	sowmya@cse.unsw.edu.au

Name of Joint/Co-Supervisor:	Paul Compton

Email of Joint/Co-Supervisor:	compton@cse.unsw.edu.au

School:	School of Computer Science and Engineering

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Artificial Intelligence

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Electrical Engineering & Telecommunications

Abstract:	Recent developments in computerised tomography (CT) scanning have revolutionized the detection and assessment of lung diseases. Current CT scanners can acquire 150 images in approximately 12 seconds and visualise these in 2 or 3 dimensions. Along with these developments has come the need for automated techniques for in terpretation and analysis. Pleural disease is the commonest manifestation of asb estos exposure, and is currently receiving high attention from medico-legal quar ters. This project is part of a larger ARC funded to develop a a multi-level self-maintaining Computer Aided Diagnosis (CAD) system that incorporates novel computer vision and knowledge acquisition techniques for quantification and assessment of asbestos-related pleural disease (ARPD). The summer project will investigate a multi-level incremental knowledge-based framework, based on Ripple Down Rules (RDR), that can handle multiple expert input and the segmented feature attributes to acquire diagnostic rules. RDR is a well-established technique for obtaining knowledge from domain experts and has been commercialised in different areas. The central idea in RDR is that experts construct rules to deal with individual cases as they occur in normal practice, allowing a knowledge base to gradually evolve. Computer-aided Diagnosis will be based on a cooperating hierarchy of RDRs (called HRDR), where higher-level RDR layers can override conclusions from lower layers. In the summer project. HRDR will be implemented and trained on patient scans, with the input of medical specialists.

Research Environment:	The summer project will be undertaken within the context of the larger ARC funded project, with 3 university researchers from Computer Science and Eng and Medicine, as well as a team of 5 medical specialists (respiratory physicians and radiologists) from St Vincent's and Liverpool hospitals. The main work will be based at the School of CSE, with occasional meetings with the other project members (possibly offsite).

Novelty and Contribution:	Automated methods for the acquisition of knowledge are at the forefront of technology. RDR based CAD systems are ideal to overcome issues in CT image interpretation, which have confounded earlier attempts. They allow clinical data to be seamlessly integrated with image data as required \u2013 similar to how radiologists qualify their interpretation of an image because of other clinical information. Secondly results from different feature detection modules can be combined easily.

Expected Outcomes:	A tool for immediate and accurate assessment of the extent of pleural disease in patients, that will be evaluated in situ by specialists at St Vincent's and Liverpool Hospitals

Reference Material Links:	contact supervisor and co-supervisor

Project Title:	Asbestos-Related Pleural Disease Feature Detection in Lung CT Images

Name of Supervisor:	Arcot Sowmya

Email of Supervisor:	sowmya@cse.unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Computer Science and Engineering

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Artificial Intelligence

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Electrical Engineering & Telecommunications

Abstract:	Recent developments in computerised tomography (CT) scanning have revolutionized the detection and assessment of lung diseases. Current CT scanners can acquire 150 images in approximately 12 seconds and visualise these in 2 or 3 dimensions. Along with these developments has come the need for automated techniques for interpretation and analysis. Pleural disease is the commonest manifestation of asbestos exposure, and is currently receiving high attention from medico-legal quarters. This project is part of a larger ARC funded to develop a a multi-level self-maintaining Computer Aided Diagnosis (CAD) system that incorporates novel computer vision and knowledge acquisition techniques for quantification and assessment of asbestos-related pleural disease (ARPD). The summer project will investigate automatic feature extraction and quantification techniques for ARPD features, and compare results to current “best practice” techniques for assessment of the clinical consequences of benign ARPD. The goal is to build an automated technique to segment pleural plaques and diffuse pleural thickening in volumetric scans using 3D image analysis and features, and expert input from the medical specialists.

Research Environment:	The summer project will be undertaken within the context of the larger ARC funded project, with 3 university researchers from Computer Science and Eng and Medicine, as well as a team of 5 medical specialists (respiratory physicians and radiologists) from St Vincent's and Liverpool hospitals. The main work will be based at the School of CSE, with occasional meetings with the other project members (possibly offsite).

Novelty and Contribution:	ARPD is likely to become one of the commonest asbestos-related disorders compensated in the next 15 years. It is currently the second commonest abnormality compensated in NSW, representing 30% of all cases compensated in 1994-2005 and a considerable financial burden to industry and the community. This project will use unique clinical data collected from a study conducted within St Vincent’s Hospital that provides scientifically useful data which will also be extremely relevant for clinical and compensation purposes.

Expected Outcomes:	The study will develop for the first time sophisticated, easy-to-use software for detection of DPT and pleural plaques and allow differentiation from asbestos-related malignancy. This is of considerable value for the affected individual and the community.

Reference Material Links:	contact supervisor

Project Title:	Intelligent digital matting

Name of Supervisor:	Arcot Sowmya

Email of Supervisor:	sowmya@cse.unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Computer Science and Engineering

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Artificial Intelligence

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Electrical Engineering & Telecommunications

Abstract:	Digital matting is the process of extracting a foreground object from an image or video together with an 'alpha channel' or opacity estimate for each pixel covered by the object. This operation enables to seamlessly merge the extracted object with a new background and plays a very important role in many image and video editing applications. The goal of this project will be to develop a system which allows realistic composition of objects from multiple video sequences with minimal user interaction. It will offer students an opportunity to become familiar with advanced video editing techniques.

Research Environment:	The research team at CSE contains a senior academic and about 7 PhD students / postdocs. The team has solid experience on machine learning in computer vision, with large applications developed for satellite image analysis, medical imaging, motion tracking and activity recognition. These provide the basis to extend to the proposed project area.

Novelty and Contribution:	The proposed techniques will be based on novel statistical machine learning.

Expected Outcomes:	Software implementation and demo / publication

Reference Material Links:	J Wang and M Cohen, 'Image and Video Matting: A Survey', Foundations and Trends in Computer Graphics and Vision Vol 3. http://w3.impa.br/~lvelho/ip08/reading/video-matting.pdf

Project Title:	Segmentation of Anatomical Landmarks in Lung CT images for Computer Aided Diagnosis

Name of Supervisor:	Arcot Sowmya

Email of Supervisor:	sowmya@cse.unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Computer Science and Engineering

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Artificial Intelligence

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Electrical Engineering & Telecommunications

Abstract:	Recent developments in computerised tomography (CT) scanning have revolutionized the detection and assessment of lung diseases. Current CT scanners can acquire 150 images in approximately 12 seconds and visualise these in 2 or 3 dimensions. Along with these developments has come the need for automated techniques for interpretation and analysis. Pleural disease is the commonest manifestation of asbestos exposure, and is currently receiving high attention from medico-legal quarters. This project is part of a larger ARC funded to develop a a multi-level self-maintaining Computer Aided Diagnosis (CAD) system that incorporates novel computer vision and knowledge acquisition techniques for quantification and assessment of asbestos-related pleural disease (ARPD). The summer project will investigate novel tissue segmentation techniques incorporating anatomy and diagnostic knowledge that will detect disease features of interest in high resolution CT images of the lung.

Research Environment:	The summer project will be undertaken within the context of the larger ARC funded project, with 3 university researchers from Computer Science and Eng and Medicine, as well as a team of 5 medical specialists (respiratory physicians and radiologists) from St Vincent's and Liverpool hospitals. The main work will be based at the School of CSE, with occasional meetings with the other project members (possibly offsite).

Novelty and Contribution:	Robust detection and segmentation of anatomical landmarks such as ribs, diaphragm and mediastinum are essential for both segmenting disease patterns and image registration between scans in the longitudinal study. While many reported techniques for anatomical segmentation may work well in normal cases, the disease-affected anatomy poses many challenges.

Expected Outcomes:	A tool for immediate and accurate assessment of anatomical landmarks of interest, that will be evaluated in situ by specialists at St Vincent’s and Liverpool Hospitals

Reference Material Links:	contact supervisor

Project Title:	Using Machine Learning to Model Connections between Political Competition and International Conflict

Name of Supervisor:	Arcot Sowmya

Email of Supervisor:	sowmya@cse.unsw.edu.au

Name of Joint/Co-Supervisor:	Ben Goldfield

Email of Joint/Co-Supervisor:	b.goldsmith@usyd.edu.au

School:	School of Computer Science and Engineering

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Artificial Intelligence

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Electrical Engineering & Telecommunications

Abstract:	The study of connections between domestic politics and international relations has been very active and productive in political science over the past two decades. The most prominent example of this is the “democratic peace” literature, exploring a widely acknowledged empirical finding, that historically democracies have been very unlikely to go to war with each other, although in general they may be no less war-prone than other kinds of state. But explaining why this is the case continues to prove challenging for theorists and empirical analysts. We propose to build on previous work (Goldsmith, Cai, & Sowmya 2008; Goldsmith, Chalup, & Quinlan 2008) to explore a particularly promising hypothesis about the role of institutions for political competition in the democratic peace. Machine learning techniques such as artificial neural networks (ANNs) and support vector machines (SVMs) are especially appropriate for the proposed work because they can capture the contingent and non-linear dynamics of complex political processes much better than standard econometric techniques almost universally employed in the literature. This project can stand alone and lead to a conference paper and/or journal article, but is also designed to complement work proposed in a larger project currently under review as an ARC Discovery Project.

Research Environment:	The summer project will be undertaken within the context of ongoing investigation into these dynamics, which is a collaborative project between the main supervisor, Prof. Arcot Sowmya, and Dr. Benjamin Goldsmith, of the Department of Government & International Relations, University of Sydney.

Novelty and Contribution:	There are very few applications of machine learning to quantitative analysis in political science, but those which have been done include some of the leading empirical researchers and methodologists in the field. Sowmya and Goldsmith have each contributed to this area in the papers cited above. The particular focus on political competition stems from strong findings by Goldsmith (2007) that this aspect of political systems is most strongly connected to behavior closely linked to warfare, especially military spending.

Expected Outcomes:	We will produce a joint conference paper , to be presented at a leading political science and/or computer science/engineering conference. This paper may then be revised for submission to leading journal, either in political science or computer science / engineering.

Reference Material Links:	Goldsmith, B.E. 2007. “Defense Effort and Institutional Theories of Democratic Peace and Victory: Why try harder?” Security Studies 16, 2: 189-222. Goldsmith, B.E., X. Cai & A. Sowmya. 2008. ‘Is International Trade Associated with Peace or War? Some new measures and methods,’ presented at Meeting of American Political Science Assoc., Boston, 28-31 August. Goldsmith, B.E., S.K. Chalup, and M.J. Quinlan. 2008. “Regime Type and International Conflict: Towards a general model,” Journal of Peace Research 45, 6: 743-763. Others available on request

Project Title:	Vision-based hazard detection for the visually impaired

Name of Supervisor:	Arcot Sowmya

Email of Supervisor:	sowmya@cse.unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Computer Science and Engineering

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Artificial Intelligence

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Electrical Engineering & Telecommunications

Abstract:	Persons with visual impairment usually rely on their hearing system to sense the environment, which also raises the issue of vision-audio-based interactive interfaces. Research in computer vision has make progress on 3-D reconstruction, moving object tracking and activity recognition. These techniques may be used to create intelligent systems to understand the environment and provide aids for hazard prediction, alarming and avoidance. The summer project is part of a larger endeavour to develop a hazard detection system that can actively sense the environment and learn to predict and classify any hazards to the user. It would also provide hazard avoidance facilities as spatial mapping and intelligent navigation, based on computer vision techniques. Cameras are used to capture the scene and construct a 3-D virtual world, in which the motion of objects are tracked, activities are analysed and recognised. The environment information will then be fused and analysed to understand the hazard impact of the environment. The results will be mapped to appropriate signals, such as sounds or vibration, to inform and warn the user.

Research Environment:	The research team at CSE contains a senior academic and about 7 PhD students / postdocs. The team has already developed many motion tracking and activity recognition systems, including for the iCinema AVIE environment as well as face tracking using webcams. All necessary infrastructure is available.

Novelty and Contribution:	Standard approaches for hazard detection use ultrasound sensors and visual cameras to detect objects near the user, which only provide unstructured information on potential barriers. However, without understanding the structure of the environment and activities within it, a hazard detection system is weak. Furthermore, current hazard detection systems concentrate more on the environment, and less on the user. This is unsatisfactory since the degree of attention and response of the user to any potential hazard is very important in analysing the severity of hazards. Developing intelligent hazard detection systems for the visually impaired, that can understand the environment and the user response, is a great challenge.

Expected Outcomes:	A real-time tracking system that converts a spatial map into sound signals, to serve as navigational aid to the visually impaired

Reference Material Links:	contact supervisor

Project Title:	Vision-based head activity and face expression analysis for machine control

Name of Supervisor:	Arcot Sowmya

Email of Supervisor:	sowmya@cse.unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Computer Science and Engineering

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Artificial Intelligence

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Electrical Engineering & Telecommunications

Abstract:	Within human-machine interaction (HCI) research, great effort is taken to create good user interfaces by directly employing the natural communication and manipulation skills of humans. Adopting vision in HCI will allow the deployment of a wide range of techniques in more sophisticated and restricted scenarios. Such systems are especially important for machine control by people with restricted limb movements. Future HCI interfaces will also need a good understanding of the person’s behaviour so that machines can learn from it, react accordingly, and reproduce this behaviour afterwards. The development of such systems involves addressing of challenging research problems including effective input and output techniques, interaction styles and evaluation methods. In the input domain, the computer vision approach offers the capturing and interpretation of the motion of head, eye gaze, face, hand, arms or even the whole body. The summer project is part of a larger project whose aim is to develop an intelligent vision-based HCI system for humans with restricted limb and body movements, that performs accurate and flexible machine control. It utilises cameras to capture the head pose and movement, as well as face appearance, which are analysed, interpreted and learned into knowledge. The knowledge will then be utilised to control machines.

Research Environment:	The research team at CSE contains a senior academic and about 7 PhD students / postdocs. The team has already developed a learnable level set-based active contour model for image segmentation and recognition and dynamic models for video tracking. These provide a working platform to extend and apply machine learning and active shape and/or appearance models to tackle the design, development and implementation of intelligent HCI for machine control.

Novelty and Contribution:	Face video analysis is often performed through the study of specific face features, such as eyes, eyebrows and mouth, to extract the most significant information regarding expression and speech. Most research on HCI treat head pose and facial expression separately, which prevents the system from using coupled information of head pose and face expression to improve the accuracy in terms of tracking and knowledge presentation. Even when both are used, head information is usually utilised only to predict the pose of the head rather than to study its activity, where the latter provides richer information for HCI. Fusing information of head activity and face expression for machine control is a big challenge.

Expected Outcomes:	Algorithms for one or more of intelligent head tracking, head activity recognition, facial expression recognition

Reference Material Links:	contact supervisor

Project Title:	A multi-touch SmartBoard project

Name of Supervisor:	Arcot Sowmya

Email of Supervisor:	sowmya@cse.unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Computer Science and Engineering

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Immersive Systems and Virtual Reality

School Research Area:	Autonomous Systems & Sensing Technologies

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Electrical Engineering & Telecommunications

Abstract:	In this project, you will implement a multi touch smart board that uses cameras and computer vision techniques to implement multi-touch interaction on a standard whiteboard. The system will combine Wii-mote tracking with webcam techniques to provide a multi-touch interface that can also recognise hand gestures for interaction.

Research Environment:	The research team at CSE contains a senior academic and about 7 PhD students / postdocs. The team has already developed a number of systems for image segmentation and recognition and for video tracking. These provide a working platform to extend and apply th etechnqius to the project.

Novelty and Contribution:	This research explores a unique combination of sensor technology. Both the wii-mote and the webcam are cheap but suffer some limitations in terms of tracking. By combining the two input methods, we may be able to overcome these.

Expected Outcomes:	A smart board interaction application which uses a combination of Wii and webcam to perform tracking.

Reference Material Links:	contact supervisor

Project Title:	iHMMune-align: machine learning models and antibody genes

Name of Supervisor:	Bruno Gaeta

Email of Supervisor:	bgaeta@unsw.edu.au

Name of Joint/Co-Supervisor:	Andrew Collins/Mike Bain

Email of Joint/Co-Supervisor:	a.collins@unsw.edu.au, mike@cse.unsw.edu.au

School:	School of Computer Science and Engineering

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Health & Medical Technologies

School Research Area:	Bioinformatics

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Sciences – Maths, Physics, Chemistry

Abstract:	This project is part of an effort to develop bioinformatics methods to understand the mechanisms through which the immune system is able to create antibodies against almost every possible bacteria/virus/parasite/toxin from a limited number of genes. We have previously developed iHMMune-align, a program that analyses DNA sequences encoding antibodies, which is now being adopted worldwide to study huge DNA sequence datasets in order to understand and improve treatment of conditions such as leukaemia and allergy. This project aims to improve the accuracy and robustness of iHMMune-align, as well as widening its applications. Good Java programming skills are required. Some understanding of the immune system and of bioinformatics would be useful but are not essential.

Research Environment:	The student will be working in the school of CSE and the school of Biotechnology and Biomolecular sciences, interacting with both computer scientists and immunologists.

Novelty and Contribution:

Expected Outcomes:	Improved antibody alignment software with broader applicability and robustness

Reference Material Links:	http://www.emi.unsw.edu.au/~ihmmune

Project Title:	Phonemote – Turning Mobile Phones into Wii-like Game Remote Controllers

Name of Supervisor:	Arcot Sowmya

Email of Supervisor:	sowmya@cse.unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Computer Science and Engineering

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Human Computer Interaction

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Electrical Engineering & Telecommunications

Abstract:	Human-computer interaction (HCI) is concerned with the design, evaluation and implementation of interactive computing systems for human use and the study of the major phenomena surrounding them. The goal of this project is to develop new perceptual interfaces for human-computer-interaction based on visual input captured by mobile phone cameras, and to investigate how such interfaces can complement or replace traditional interfaces based on keyboards, mics, remote control devices, data gloves or Wii Remote

Research Environment:	The research team at CSE contains a senior academic and about 7 PhD students / p ostdocs. The team has already developed a number of systems for image segmentation and recognition and dynamic models for video tracking. These provide a working platform to extend and apply to the HCI interfaces propsoed in this project.

Novelty and Contribution:	This research may result in new approaches of human computer interaction systems that have a large range of applications from computer input to entertainment equipment control.

Expected Outcomes:	Outcomes of this research include software packages and a short technical report documenting the research. The work may be publishable.

Reference Material Links:	contact supervisor

Project Title:	Smart Room Interaction Environment

Name of Supervisor:	Arcot Sowmya

Email of Supervisor:	sowmya@cse.unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Computer Science and Engineering

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Immersive Systems and Virtual Reality

School Research Area:	Human Computer Interaction

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Electrical Engineering & Telecommunications

Abstract:	Smart rooms provide a novel user interface to a physical or virtual environment. In this summer research project, you will be implementing an interaction mechanism for a Smart Room meeting environment, using computer vision techniques. The system will track users within the smart room using 4 static colour cameras and a pan-tilt-zoom camera as sensors. The system will then provide a method for users to interact with the room itself. Examples of such interactions include pointing to a light switch to turn it on or performing a certain gesture to turn on the projector.

Research Environment:	The research team at CSE contains a senior academic and about 7 PhD students / postdocs. The team has already developed many systems that perform image segmentation and recognition and build dynamic models for video tracking. These provide a working platform to extend and apply the techniques to smart room interactions. A prototype smart room, with PTZ cameras on overhead tracks, and servers that control them, already exists in CSE, and will be used in the project.

Novelty and Contribution:	This research may result in new Smart Room technology, which provides unique interaction mechanisms within the rooms.

Expected Outcomes:	Outcomes of this project include an interaction application for the smart room within CSE.

Reference Material Links:	contact supervisor

Project Title:	Image enhancement with a few high quality images

Name of Supervisor:	Getian Ye

Email of Supervisor:	Getian.Ye@nicta.com.au

Name of Joint/Co-Supervisor:	Yang Wang

Email of Joint/Co-Supervisor:	Yang.Wang@nicta.com.au

School:	School of Computer Science and Engineering

For CSE and EET Projects:	NICTA Project

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Image Processing

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Electrical Engineering & Telecommunications

Abstract:	The quality of a digital image is usually affected by many factors such as the size of camera sensor, noises, and environmental conditions. For example, the sensor of a webcam is usually small and noisy so that the image quality is low. When a camera is used in an uncontrolled environment, different artefacts like dust and blurring may be introduced in the image. Improving image quality can not only give the viewer more pleasing pictures but can also offer more details that may be critical in different kinds of applications. This project aims to develop a novel technique for image enhancement using a few high quality images, which are independent of the image to be improved. The nature of the work is mainly software development. The successful candidate will improve skill in problem solving and gain basic knowledge of image processing.

Research Environment:	The successful candidate will be working with researchers and postgraduate students at Kensington Laboratory, National ICT Australia.

Novelty and Contribution:	The novelty lies in improving image quality with unrelated high quality images. Part of the outcome of this project will contribute to NICTA’s Strategic Project – Smart Transport and Road (STaR).

Expected Outcomes:	Software implementation, experiments, and demo.

Reference Material Links:

Project Title:	Software for Advanced Patent Analysis

Name of Supervisor:	Vladimir Tosic

Email of Supervisor:	vtosic@cse.unsw.edu.au

Name of Joint/Co-Supervisor:	Mark Staples

Email of Joint/Co-Supervisor:	Mark.Staples@nicta.com.au

School:	School of Computer Science and Engineering

For CSE and EET Projects:	NICTA Project

Faculty Research Area (Theme):	Management

School Research Area:	Miscellaneous

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Chemical Sciences and Engineering Civil & Environmental Engineering Electrical Engineering & Telecommunications Mechanical & Manufacturing Engineering Mining Engineering Petroleum Engineering Photovoltaic and Renewable Energy Engineering Surveying & Spatial Information Systems Sciences – Maths, Physics, Chemistry

Abstract:	To protect its intellectual property, it is often necessary for a company to patent its inventions. Patents are legally enforceable rights for exclusive commercial exploitation of inventions. Before patenting, patent search and analysis can uncover important facts relevant for strategic decisions about company’s intellectual property and research and development activities in general. Various software tools support patent search and analysis, from relatively simple free tools and Web sites to more powerful commercial products (e.g., for determining and visualizing various dependencies). In this research project, students will help develop novel software for advanced patent analysis, based on a new patent analysis methodology. The methodology is currently supported by software that manages patent information in Excel and uses macros for processing and visualization of patent analyses. The first aspect of this project is to support querying and analysis of patent information stored in a relational database. The second aspect of the project is to implement additional advanced patent analysis procedures. The third aspect of this project involves search and analysis of a number of real patents, determining their characteristics, and storing and managing this information using the developed software tool, to evaluate the tool’s correctness and usefulness.

Research Environment:	The students will work closely with researchers at NICTA (http://www.nicta.com.au) in a friendly mixed-gender and multicultural environment comprised of senior researchers and postgraduate students.

Novelty and Contribution:	The main novelty is the support for a unique and new patent analysis methodology. Since some aspects of the new patent procedures have not been implemented previously in other systems, non-trivial research questions (e.g., how to categorize patents in terms of relevance for company’s business strategy) will have to be considered. These patent analysis procedures will enable better decision making about a company’s patent portfolio. Another contribution is the testing process, which will result in conclusions about real patents from one market area (e.g., implant systems, business-driven IT systems management, or another area of mutual interest).

Expected Outcomes:	- Architecture of a software system that stores patent information, processes this information (e.g., to determine various dependencies), and visualizes results. - Detailed design of modules of this software architecture. - Design of database for storing patent information. - Original patent analysis procedures, which query and process the stored patent information. - Implementation of the above-mentioned designs. - Design and implementation of a simple (possibly Web) interface into the system. - Population of the database with patent information for a number of real patents from the same scientific area. - Experiments evaluating correctness and usefulness of the implemented software.

Reference Material Links:	- http://en.wikipedia.org/wiki/Patent - http://www.ipaustralia.gov.au/patents/what_index.shtml - http://www.google.com/patents - http://www.patentlawlinks.com/patsearc.htm - http://www.infovis.net/printMag.php?lang=2&num=167 - D. Hunt, L. Nguyen, M. Rodgers (Eds.) “Patent Searching: Tools & Techniques”, Wiley, 2007 - J.L. Davis, S.S. Harrison “Edison in the Boardroom: How Leading Companies Realize Value from Their Intellectual Assets”, Wiley, 2001 - Course COMP9311 “Database Systems” (http://www.cse.unsw.edu.au/~cs9311) - http://www.edumax.com/database-basics-chapter-2-the-er-model-and-database-design.html - http://www.w3schools.com/SQl/default.asp - Course COMP9321 “Web Applications Engineering” (http://www.cse.unsw.edu.au/~cs9321) - For further information, email Dr. Vladimir Tosic (‘vtosic’ at the CSE e-mail system) with Subject line “UNSW Summer Scholars”.

Project Title:	Computer simulation of cracked structures

Name of Supervisor:	Chongmin SONG

Email of Supervisor:	c.song@unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Civil and Environmental Engineering

Faculty Research Area (Theme):	Solids and Applied Dynamics

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Mechanical & Manufacturing Engineering Mining Engineering Petroleum Engineering Sciences – Maths, Physics, Chemistry

Abstract:	Under service condition, cracks develop in many structures, especially those that have passed or are approaching their design life. The crack tips are often the weakest points of a structure. For safe and cost-effective management of cracked structures, the stability of cracks needs to be evaluated. This project will be built on recent work of the group on computational mechanics to study cracked structures. The focus will be on the development of criteria for crack propagation. Strong analysis and computing skills are required for this project.

Research Environment:	The student will work with a group of active researchers on computer simulation of civil structures.

Novelty and Contribution:	It is observed experimentally that existing criteria for crack propagation depends on the size of the specimens (the so-called size effect), which hinders the application of computer simulation in the analysis of civil engineering structures. This research seeks to develop a unified criterion that does not suffer from the size effect.

Expected Outcomes:	The result will be a crack propagation criterion that can be employed to predict the stability of cracks in structure components of various dimensions.

Reference Material Links:	http://www.civeng.unsw.edu.au/staff/chongmin_song/

Project Title:	Core-shell nanoparticles for drug delivery

Name of Supervisor:	Martina Stenzel

Email of Supervisor:	M.Stenzel@unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Chemical Sciences and Engineering

Faculty Research Area (Theme):	MEMS, Micro & Nano Technologies

Applicable to other Engineering schools/disciplines:	Biomedical Engineering

Abstract:	Core-shell nanoparticles composed from amphiphilic block copolymers are widely proposed for the delivery of drugs over an extended period of time. Their size (typically below 100 nm) in combination with their hydrophilic water soluble shell and their hydrophobic core makes them a perfect carrier for a range of anticancer drugs. Aim of this project is to develop a nanocarrier for albendazole- an anticancer drug. The student will be involved in hands on polymer synthesis (RAFT polymerization) to prepare block copolymers. The student will learn how nanoparticles are prepared and characterized (e.g. measuring the diameter of the sphere), but will also learn to use a transmission electron microscope (TEM) to visualize the nanoparticles prepared. Part of the work will also be the investigation of the loading of the drug and the drug release in vitro.

Research Environment:	The student will be part of a big reasearch group with several PhD students and postdoctoral researchers. The main research theme of the research group is the development of nanoparticles for drug delivery. The student will therefore find a stimulating envrionment and a lot of support from co-workers

Novelty and Contribution:	So far, there is no drug carrier available for the drug albendazol. The studenty will take part in optimisation of the drug carrier. By changing the molecular weight of the underlying polymers, nanoparticles with different sizes and shapes can be created. Furthermore, the nature of the polymer determines the drug loading.

Expected Outcomes:	Aim of this work is to establish a correlation between the polymer structure and the properties of the drug carrier (size, loading and release)

Reference Material Links:

Project Title:	Enhancing Surface Segregation of Anti-fouling Additives for Membranes

Name of Supervisor:	Vicki Chen

Email of Supervisor:	v.chen@unsw.edu.au

Name of Joint/Co-Supervisor:	Jaleh Mansouri

Email of Joint/Co-Supervisor:	j.mansouri@unsw.edu.au

School:	School of Chemical Sciences and Engineering

Faculty Research Area (Theme):	Advanced Manufacturing and Processing Technologies

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Sciences – Maths, Physics, Chemistry

Abstract:	A major challenge in membrane filtration for water treatment is fouling by proteins and other biomolecules in the feed stream that leads to the flux decline. Frequent physical and chemical cleaning imposes economic and environmental costs and eventually leads to replacement of the membrane modules due to stresses on the membranes. Thus membranes that are less prone to fouling are therefore in high demand. The use of amphiphilic additives is a highly desirable approach when the chemical structure and fabrication conditions lead to preferential migration and surface enrichment of low fouling additives without detrimental changes to the membrane structure. These compounds can reduce adsorption or encourage detachment of potential foulants on membrane surface by containing polymeric block copolymers with either hydrophilic or low surface energy segments such as pluronic (PEO-PPO-PEO) and polysiloxane surfactants. The objective of this project is to characterize and manipulate the phase behaviour for a given polymer/solvent/non-solvent /additive system during the membrane casting process to enhance surface enrichment of these additives. Membranes fabricated based on those conditions will be characterized by a range of techniques including field emission scanning electron microscopy, contact angle, x-ray photon spectroscopy and protein fouling experiments.

Research Environment:	The UNESCO Centre for Membrane Science and Technology is a world leading membrane research group with extensive facilities for membrane fabrication and characterization.

Novelty and Contribution:	The proposed project will explore the potential of enhancing surface aggregation of low cost polymer additives by manipulating the membrane fabrication process.

Expected Outcomes:	The potential outcomes of this project are low fouling membranes with reduced energy consumption and lower chemical discharge to the environment during their operating life. Enhanced membrane performance and lifetime using a cost effective fabrication process will be highly attractive to many industrial applications for membrane filtration.

Reference Material Links:	http://www.membrane.unsw.edu.au/

Project Title:	Evaluating the hydrological cycle from space

Name of Supervisor:	Matthew McCabe

Email of Supervisor:	mmccabe@unsw.edu.au

Name of Joint/Co-Supervisor:	Jason Evans

Email of Joint/Co-Supervisor:	jason.evans@unsw.edu.au

School:	School of Civil and Environmental Engineering

Faculty Research Area (Theme):	Spatial Information Systems and Positioning

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Surveying & Spatial Information Systems

Abstract:	This project will assess estimates of the hydrological cycle derived from satellite remote sensing - including soil moisture, rainfall, evaporation and atmospheric water vapor - for their hydrological consistency. That is, the project will determine the extent to which independent observations of water cycle components reflect the expectations of the natural water cycle (i.e. if it rains, does the soil moisture increase - if the soil moisture increases, does evaporation respond etc..). The level of agreement in these data will be explored using novel statistical techniques and comparison with land surface output.

Research Environment:	The research will be undertaken within a group of young researchers and academics undertaking investigations into hydro-climate related projects. See http://www.civeng.unsw.edu.au/staff/matthew_mccabe/ for further details. There may also be an opportunity for field work in regional NSW where in-situ measurements of hydrological variables can be used for remote sensing validation.

Novelty and Contribution:	Examining the degree of closure within the hydrological cycle as observed from satellite observations is an emerging field of research. Combining these exciting and innovative approaches with existing land surface model output, we will be able to identify water cycle behavior in unprecedented detail.

Expected Outcomes:	An analysis of the hydrological cycle at continental scales, potentially examining the degree of water balance closure.

Reference Material Links:	http://nasascience.nasa.gov/earth-science/water-and-energy-cycle

Project Title:	Exploring the Use of Magnetic Nano-Gold for Efficient Gene Delivery into Mammalian Cells

Name of Supervisor:	Rose Amal

Email of Supervisor:	r.amal@unsw.edu.au

Name of Joint/Co-Supervisor:	May Lim

Email of Joint/Co-Supervisor:	m.lim@unsw.edu.au

School:	School of Chemical Sciences and Engineering

Faculty Research Area (Theme):	Health & Medical Technologies

Applicable to other Engineering schools/disciplines:	Biomedical Engineering

Abstract:	This project will explore the use of composite nanoparticles consisting of a 50 nm magnetic iron oxide (Fe3O4) core with 3 nm gold particles attached on the surface as gene therapy delivery agents. The key aspects of this work will broadly revolve around attaching DNA molecules onto the particles, delivering the particles containing DNA into cells, tracking the localization of particles and DNA inside the cell, and determining the rate of successful expression of the DNA in the target cells. The student will also utilize a range of analytical tools including transmission electron microscopy (TEM), inductively coupled plasma (ICP) spectroscopy, optical/fluorescence microscope, and dynamic light scattering (DLS) to characterize the nanoparticles and the cells.

Research Environment:	The student undertaking this project will be working within the ARC Centre of Excellence for Functional Nanomaterials in the School of Chemical Sciences and Engineering, under the guidance of postdoctoral research staff. This project will require a level of effort of about 25 hours per week of laboratory work. For more details, please contact Professor Rose Amal (r.amal@unsw.edu.au) or Dr May Lim (m.lim@unsw.edu.au).

Novelty and Contribution:	Novel nanoscale particles have become the subject of significant interest in medical and biological research because their unique size and chemical properties make these nanoparticles suitable for the delivery of therapeutic agents. A major potential medical application for nanoparticles is the delivery of therapeutic deoxyribonucleic acid (DNA) into mammalian cells in a process known as gene therapy Gene therapy is an emerging technology to treat genetically mutated disease such as cancer. Nanoparticles are ideal candidates to replace traditional viral delivery agents in gene therapy because they are cheaper to produce and have less potential side effects.

Expected Outcomes:	This exciting project will allow the student to gain experience in the emerging field of bionanotechnology as well as an understanding of the fundamentals of nanoparticle synthesis and gene therapy.

Reference Material Links:	a. Goon IY, Lai LMH, Lim M, Munroe P, Gooding JJ, Amal R. Fabrication and Dispersion of Gold-Shell-Protected Magnetite Nanoparticles: Systematic Control Using Polyethyleneimine. Chem. Mater 2009; 21(4): 673-81. b. Plank C, Schillinger U, Scherer F, Bergemann C, Remy JS, Krotz F, et al. The magnetofection method: using magnetic force to enhance gene delivery. Biol Chem 2003; 384(5): 737-47. c. http://en.wikipedia.org/wiki/Gene_delivery

Project Title:	Investigate different metals to improve the contact on materials for 3rd Gen PV devices

Name of Supervisor:	Ivan Perez-Wurfl

Email of Supervisor:	ivanpw@unsw.edu.au

Name of Joint/Co-Supervisor:	Gavin Conibeer

Email of Joint/Co-Supervisor:	g.conibeer@unsw.edu.au

School:	School of Photovoltaic and Renewable Energy Engineering

Faculty Research Area (Theme):	Advanced Materials

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Electrical Engineering & Telecommunications Sciences – Maths, Physics, Chemistry

Abstract:	Investigate different metals to improve the contact on materials for Third Generation photovoltaic devices. The successful applicant will test a range of metals that she will deposit using thin film deposition techniques such as evaporation and sputtering. The student will also learn different lithographic techniques to define structures for evaluating the quality of the metal contacts. The student will learn how to do electrical testing using a probe station to evaluate the type of contact obtained on silicon based thin films for third generation solar cells.

Research Environment:	Student will be working alongside a senior researcher and junior staff in the exciting field of Third Generation photovoltaic materials and devices.

Novelty and Contribution:	The optimized metal contact will be used in Third Generation Photovoltaic Devices and is expected to improve their efficiency.

Expected Outcomes:	The ultimate aim of the project is for the student to learn how to analyse his measurements and be able to determine which metal gives the least resistive contact on the silicon based thin films of interest.

Reference Material Links:	Basic knowledge of solid state materials and devices would be beneficial but it is not essential.

Project Title:	Measuring Australia's Water Use from Space

Name of Supervisor:	Matthew McCabe

Email of Supervisor:	mmccabe@unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Civil and Environmental Engineering

Faculty Research Area (Theme):	Water and Wastewater Engineering

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Surveying & Spatial Information Systems

Abstract:	Evapotranspiration is one of the key variables of interest in water cycle studies, providing information on catchment scale water use, water demand and assessment of irrigation based applications. It is of major interest to the Australian Government, who are investing significant resources to develop techniques to quantify its spatial and temporal variability. Satellite observations provide an ideal tool to allow spatial and temporal patterns of evapotranspiration to be predicted – something that is not able to be achieved using in-situ measurements alone. This project will use data from a number of satellite platforms to estimate evapotranspiration over continental Australia. To assess the satellite based estimates, output from land surface models and simplified techniques such as the Penman-Monteith or Priestley-Taylor will be used as methods for comparison.

Research Environment:	The student will work with a strong group of researchers engaged in developing remote sensing observations and land surface modelling solutions for hydro-climate related studies. See www.civeng.unsw.edu.au/staff/matthew_mccabe for more details. There may also be an opportunity for field work in regional NSW where in-situ measurements of hydrological variables can be used for remote sensing validation.

Novelty and Contribution:	Will provide some of the first satellite based estimates of these hydrological variables over Australia

Expected Outcomes:	Maps of water use for irrigated regions - development of a climatology of evaporation over the Australian mainland.

Reference Material Links:	http://nasascience.nasa.gov/earth-science/water-and-energy-cycle http://www.civeng.unsw.edu.au/staff/matthew_mccabe/

Project Title:	Novel sensors based on polymer coated magnetic materials

Name of Supervisor:	Dr. Anthony Granville

Email of Supervisor:	a.granville@unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Chemical Sciences and Engineering

Faculty Research Area (Theme):	MEMS, Micro & Nano Technologies

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Sciences – Maths, Physics, Chemistry

Abstract:	Magnetoelastic materials are iron-based alloys that vibrate when subjected to a magnetic field, and these vibrations induce a magnetic field dependent on the dimensions and mass of/on the material. Thus, by coating these alloys with polymers that selectively bind to proteins, enzymes, and chemicals, these materials can be utilised as cheap, effective sensors without the need for electrical leads – making them ideal for biological applications. The student will be involved in the surface modification of these materials using a wide range of polymerisation techniques (Reversible Addition-Fragmentation chain Transfer polymerisation and Atom Transfer Radical Polymerisation) as well as “click” chemistry techniques. The student will also be involved in the testing of these materials, using our Helmholtz coil electromagnet reader, for binding and release studies to determine the sensitivity of the produced sensors – critical of biological studies.

Research Environment:	The student will be an integral in a growing research group in the CAMD research centre. The group comprises Ph.D., postdoctoral, and visiting practicum students all with expertise in polymer chemistry. With the support of the entire research centre, and access to newly renovated labs as well as cutting-edge equipment, the student will be exposed to a stimulating and enjoyable research environment.

Novelty and Contribution:	This is a new and expanding research area, with an opportunity to contribute both research-wise as well as creatively to the scientific field. Initial studies have been with glucose-binding, however this work will begin to expand further into the realm of binding to other proteins through the use of glycopolymers on the surface.

Expected Outcomes:	The aim of this work is to determine the maximum binding to the sensor as related to polymer brush molecular weight and grafting density on the sensor surface. Theoretically, the higher the molecular weight of the polymer is, the more functional groups present on the surface, thus resulting in more binding sites. Depending on the grafting density, some of these sites may be inaccessible, which this work will determine.

Reference Material Links:	Several papers from our research group are in the process of being submitted/published, however, interested students should contact Dr. Granville to obtain reference materials in this area.

Project Title:	Nutrient impacts in environmental assessment of products

Name of Supervisor:	Greg Peters

Email of Supervisor:	g.peters@unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Civil and Environmental Engineering

Faculty Research Area (Theme):	Spatial Information Systems and Positioning

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Surveying & Spatial Information Systems

Abstract:	This Scholarship will enable the students to work with a small team on campus and find out what a career in research could be like. The student could improve the way Australians assess whether one product is better for the environment than another. Nitrogen and phosphorus emissions can cause impacts on aquatic environments – these impacts are collectively called “eutrophication”. If an engineer is deciding whether to select one technology or another, such impacts can be important, so environmental life cycle assessment (LCA) often includes an assessment of eutrophication potential. Unfortunately, approaches to doing this assessment in Australia are relatively primitive. We basically rely on a consideration of the average ratio of nutrients in organic matter. Overseas researchers have combined this with a simple transport model to produce more sophisticated potential impact assessment factors for overseas conditions. We need this in Australia.

Research Environment:	Simple air pollution dispersion models might significantly improve Australian eutrophication equivalence factors. Preliminary work on this topic has already been completed by an honours student. The scholar will update a review of international approaches to eutrophication in LCA, and work in a GIS environment to enhance modelling to suit Australian environmental conditions and emission sources. This project will suit a student with good maths and computer skills.

Novelty and Contribution:	Simple air pollution dispersion models might significantly improve Australian eutrophication equivalence factors. Preliminary work on this topic has already been completed by an honours student. The scholar will update a review of international approaches to eutrophication in LCA, and work on enhancing the modelling to suit Australian environmental conditions and emission sources. This project will suit a student with good maths and computer skills.

Expected Outcomes:	the results will be a set of geographically specific characterisation factors for environmental assessment of products.

Reference Material Links:	Norris G. A., 2003, ‘Impact characterization in the tool for the reduction and assessment of chemical and other environmental impacts: Methods for acidification, eutrophication, and ozone formation’, Journal of Industrial Ecology, vol. 6, no. 3-4, pp. 79-101. Seppala J., Posch M., Johansson M., Hettelingh J., 2006, ‘Country-dependent characterization factors for acidification and terrestrial eutrophication based on accumulated exceedance as an impact category indicator’, International Journal of Life Cycle Assessment, vol. 11, no. 6, pp. 403-416.

Project Title:	Responsive Gold Nanoparticles For Use In Nanomedicine

Name of Supervisor:	Professor Tom Davis

Email of Supervisor:	t.davis@unsw.edu.au

Name of Joint/Co-Supervisor:	Dr Michael Whittaker

Email of Joint/Co-Supervisor:	mikey.whittaker@unsw.edu.au

School:	School of Chemical Sciences and Engineering

Faculty Research Area (Theme):	Health & Medical Technologies

Applicable to other Engineering schools/disciplines:	Biomedical Engineering

Abstract:	The project will involve the modification of gold nanoparticle surfaces with polymers. the polymers serve a number of functions including stabilization in serum (biofluids), targeting to specific tissues and the delivery of therapeutic molecules such as siRNA. This is part of an on-going and successful project, with previous taste-of-research particpants getting authorship on significant publications.

Research Environment:	The Centre for Advanced Macromolecular Design (CAMD) is one of the foremest research Centres at UNSW. There is abundant high quality equipment and a strongly supportive research community.

Novelty and Contribution:	The student will be part of a small team, fuctionalizing the surfaces of gold nanoparticles with chemical groups (or peptides) that are known to help the cell-uptake of the nanoparticles.

Expected Outcomes:	As mentioned last year the taste-of-reseacrh student helped produce work that showed how to create non-biofouling gold nanoparticles that proved to be thermo-responsive. In this case we expect the creation of novel bioactive gold nanoparticles.

Reference Material Links:	This is best received from the supervisors if you are interested (our papers are currnelty in press)

Project Title:	Setting up a module for measuring light induced current in 3rd Gen light detectors

Name of Supervisor:	Ivan Perez-Wurfl

Email of Supervisor:	ivanpw@unsw.edu.au

Name of Joint/Co-Supervisor:	Gavin Conibeer

Email of Joint/Co-Supervisor:	g.conibeer@unsw.edu.au

School:	School of Photovoltaic and Renewable Energy Engineering

Faculty Research Area (Theme):	Advanced Materials

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Computer Science & Engineering Electrical Engineering & Telecommunications Sciences – Maths, Physics, Chemistry

Abstract:	Setting up a module for measuring light induced current in third generation light detectors when illuminated under different colours of light. The successful applicant will help a graduate student to automate a spectral response system to measure photocurrent as a function of the wavelength of the incident light. The student will learn how fabricate simple coplanar photo-detectors to test in the equipment setup. The student will learn how to interpret the measurements and use this to determine the quality of the material under investigation.

Research Environment:	We are looking for a student that will be working alongside a senior researcher and a PhD student in the exciting field of Third Generation photovoltaic materials and devices.

Novelty and Contribution:	The measurements of the characteristics of films using this setup will make it possible to optimize material quality with the possibility of avoiding the time consuming process of fabricating devices. A quick characterization method will enable significant advances in the optimization of the material required for third generation photo-voltaic devices.

Expected Outcomes:	The aim of this project is to build a setup to be able to use the Constant Photocurrent Method on thin films.

Reference Material Links:	Knowledge of LabView programming is essential.

Project Title:	Toxicity Assessment of Nanoparticles in Human Cell Lines In Vitro

Name of Supervisor:	Professor Rose Amal

Email of Supervisor:	r.amal@unsw.edu.au

Name of Joint/Co-Supervisor:	Dr May Lim

Email of Joint/Co-Supervisor:	m.lim@unsw.edu.au

School:	School of Chemical Sciences and Engineering

Faculty Research Area (Theme):	Health & Medical Technologies

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Sciences – Maths, Physics, Chemistry

Abstract:	The toxicology of titanium dioxide (TiO2) nanoparticles is an emerging concern due to the wide spread use of TiO2, such as in self-cleaning coatings, air sanitizers, cosmetic and sunscreens, and in dye sensitized solar cells. There is a potential for the nano-sized particles to be more hazardous than the bulk material, due to their larger surface area to mass ratio, and hence higher specific chemical reactivity and biological impacts. The extremely small size of these particles also allows the particles to be easily transported across cell membrane, and evasion from capture by the human body’s immune system. In this project, one of the major exposure routes of nanomaterials in human, inhalation will be investigated using in vitro model. The aim of the project is to study the relationship between the crystal phase and surface chemistry of TiO2 nanoparticles with the toxicity to human lung cell lines.

Research Environment:	The student who undertaking this project will be working in laboratories at the ARC Centre of Excellence for Functional Nanomaterials, School of Chemical Sciences and Engineering, and the School of Biotechnology and Bimolecular Sciences, under the guidance of postdoctoral and postgraduate research staff.

Novelty and Contribution:	This project will suit a student interested in exploring and contributing to the area of nanoscience and biotechnology, particularly the design and development of nanoparticles that are harmless to humans and the environment whilst retaining the beneficial characteristics of the particles.

Expected Outcomes:	Expected outcomes of this research will be a deeper understanding on the effect of the particle crystalinity and modify the particle surface chemistry to alter its toxicity to humans and the environment. It will lead to the design and development of TiO2 nanoparticles that are harmless to humans and the environment whilst retaining the beneficial characteristics of the particles.

Reference Material Links:	http://en.wikipedia.org/wiki/Nanotoxicology

Project Title:	Use of Zinc (II) Species to Control Bacterial Nitrification in Chloraminated Water Supplies

Name of Supervisor:	Professor Rose Amal

Email of Supervisor:	r.amal@unsw.edu.au

Name of Joint/Co-Supervisor:	Dr Sanly Liu

Email of Joint/Co-Supervisor:	z3015913@student.unsw.edu.au

School:	School of Chemical Sciences and Engineering

Faculty Research Area (Theme):	Water and Wastewater Engineering

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Civil & Environmental Engineering Sciences – Maths, Physics, Chemistry

Abstract:	The growth of nitrifying bacteria has been identified as a potential problem in chloraminated water supplies. These bacteria derive energy by the oxidation of ammonia to nitrite or of nitrite to nitrate. Bacterial nitrification can lead to rapid decays of chloramines in the distribution system, which may increase the public health risk as a result of inadequate treatment of microbiologically contaminated water. In addition, nitrification can lead to an increased in nitrate and nitrite, which may exceed the limit set in the Australian Drinking Water Guideline. The primary goal of this study is to investigate the use of various zinc species to inhibit bacterial nitrification. Zinc is often added to water supplies in conjunction with phosphorus corrosion inhibitors, and its potential role in the growth of nitrifying bacteria is therefore of high interest. Inhibition will be investigated using nitrite and nitrate generation rate measurements.

Research Environment:	Student undertaking this project will be working at the ARC Centre of Excellence for Functional Nanomaterials, School of Chemical Sciences and Engineering, under the guidance of postdoctoral research staff (Dr Sanly Liu and Dr. May Lim). The project would allow student to gain a multitude of experience in flow injection analysis and water treatment microbiology (bacteria culture and isolation technique). For more details, please contact Professor Rose Amal at r.amal@unsw.edu.au.

Novelty and Contribution:	The development of zinc based disinfectants is envisaged to lead to improved efficiency of nitrification control. This approach may present some advantages over the current industrial practice of dosing with copper, which has been shown to be inadequate in the control of nitrification.

Expected Outcomes:	Student involved in this project can expect to gain a multitude of experience in water research and development, and exposure to Australian water industries in Western Australia and South Australia.

Reference Material Links:	a. Cunliffe, D. A. (1991) Bacterial nitrification in chloraminated water supplies. Applied and Environmental Microbiology, Nov. 1991, p. 3399-3402. b. Zhang, Y., Love, N., Edwards, M. (2009) Nitrification in drinking water systems. Critical Reviews in Environmental Science and Technology, 39:3, 153-208.

Project Title:	Real Time Anger Recognition From Speech

Name of Supervisor:	Dr.H.Nosratighods

Email of Supervisor:	hadis@unsw.edu.au

Name of Joint/Co-Supervisor:	Prof.E.Ambikairajah

Email of Joint/Co-Supervisor:	ambi@ee.unsw.edu.au

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Signal Processing & Control

School Research Area:	Signal Processing

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Computer Science & Engineering

Abstract:	Researchers have been studying speech recognition and speaker identification for a number of years and now in recent years have also turned their attention to emotion recognition due to a broad range of potential applications The aim of this project is to research and implement an "anger detection" system the enables unmanned call centre services to automatically transfer angry customers to human operators. Anger detection relies on tracking prosodic and spectral features such as volume, pitch, speech rate, etc. Classifiers such as neural networks, hidden Markov models, Gaussian mixture models and decision trees are then used to detect the emotions. The system should track various parameters pertaining to the caller’s voice in the first few seconds and make an estimate of how much it deviates from neutral speech (how angry the caller is). An emotion detection database is available for development of the system. However, the final project should include a near real time system implemented in MATLAB. At the completion of the project, the student will have strengthened his/her signal processing knowledge, MATLAB skills and technical/research skills.

Research Environment:	The student(s) will be working with the speech research group of the School of Electrical Engineering and Telecommunications. This group has 6 PhD students working on speaker verification, language identification, speaker recognition, emotion detection and speech enhancement.

Novelty and Contribution:	The student will work as part of the speech research team and contribute towards the development of new feature extraction algorithm in order to classify emotions. If developed successfully, this system holds potential for commercialisation.

Expected Outcomes:	A near real time system for identifying anger from speech, implemented in MATLAB. Note: Even though this project is designed for two students to work as a team, it is also possible for a single student to carry out this project.

Reference Material Links:	[1] Yacoub, S., Simske, S., Lin, X., and Burns, J., “Recognition of Emotions in Interactive Voice Response systems”, in Proc. EUROSPEECH, pp. 729-732, 2003. [2] Bhatti, M. W., Wang, Y., and Guan, L., “A neural network approach for human emotion recognition in speech,” in Proc. IEEE ISCAS, vol. 2, pp. II- 181-184, 2004.

Project Title:	Integrating mashp technologies with Web-based social network applications

Name of Supervisor:	Jenny Liu

Email of Supervisor:	jenny.liu@nicta.com.au

Name of Joint/Co-Supervisor:	Liming Zhu

Email of Joint/Co-Supervisor:	liming.zhu@nicta.com.au

School:	School of Computer Science and Engineering

For CSE and EET Projects:	NICTA Project

Faculty Research Area (Theme):	eResearch (Knowledge and Services Engineering)

School Research Area:	Software Engineering

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Surveying & Spatial Information Systems

Abstract:	Mashup provides a way of forming new applications from existing Web content using APIs provided by different Web sites. Such a nature makes mashup a promising technology to deliver Web based social network application with rich information of various themes, such as participants' distribution, the categories of their preferences/information and etc. However, most mashup app only cover a single theme with a simple source of data format. Social network applications can contain large amount of information with complex connections with each individual. It is a challenging issue to present multiple themes of social applications. This project aims to deliver an integration solution to smartly retrieve and mashup information from different types of social network applications.

Research Environment:	TOR scholars will participate in NICTA's use-inspired research activities. The main and associated supervisors are quite experienced researchers with TOR projects. Since 2004, we have supervised two TOR projects every year. In 2008, one project won the best project award at NICTA ATP Laboratory. Students also have the opportunities in publishing outstanding research results out of the TOR project. We have several successful stories that TOR scholars published their work at top international conferences/journals and followed it up in their thesis projects. This project is hosted by NICTA Managing Complexity Group at Australian Technology Park. A dedicated computing lab with a cluster of high capacity computers are available for this project. Desk, workstation and computing facilities will be provided.

Novelty and Contribution:	Mashup and social network applications are the hottest topics on Web and service engineering. This project identifies a very challenging yet practical problem and aims to provide a novel solution for real world usage.

Expected Outcomes:	A number of information retrieval and mashup techniques will be developed using social network Web API and Google mashup libraries. A demo app will be produced.

Reference Material Links:	Contact supervisor

Project Title:	Ultrasound-based patient tracking for the unobtrusive estimation of falls risk

Name of Supervisor:	Dr. Stephen Redmond

Email of Supervisor:	s.redmond@unsw.edu.au

Name of Joint/Co-Supervisor:	Prof. Nigel Lovell

Email of Joint/Co-Supervisor:	n.lovell@unsw.edu.au

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Health & Medical Technologies

School Research Area:	Systems & Control and Biomedical Systems

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Surveying & Spatial Information Systems

Abstract:	This project aims to develop an ultrasound-based patient tracking system to aid in the unobtrusive estimation of falls risk among the elderly community. The successful student will develop an ultrasound module to augment an existing wireless body-worn accelerometer sensor, to characterise the movements of the elderly subject and hence estimate their risk of falling in the near future. Once those at risk of falling are identified, a preventative strategy implemented by allied healthcare providers may be initiated.

Research Environment:	In 1981, Dr. Branko Celler joined the School of Electrical Engineering and soon afterwards established the Biomedical Systems Laboratory (BSL), which he still leads today with Prof. Nigel Lovell, his first PhD student, as co-Director. The BSL aims to promote at-home and free-living health assessment through the development of new technologies, which are built on a strong background in physiology, electronic instrumentation and biosignal processing. Through the use of intelligent algorithms, applied to non-invasively measured signals, we strive to determine robust indicators of health which may be incorporated into relatively low-cost systems for at-home and free-living use. Such systems can reliably provide regular monitoring of a patient in the long-term and pre-empt the onset of a deterioration in health.

Novelty and Contribution:	The advantage of employing wireless sensor networks in the home, or a residential care facility, is that a significantly larger population may be regularly screened, at remote locations. While many have attempted to characterise body movement using accelerometry, and its relationship to falls risk, never before has the association between accelerometry been examined in the context of location within the environment. The proposed methodology will provide real-time insight into the efficacy of administered interventions, in those previously identified as being at high risk of falling.

Expected Outcomes:	The triaxial accelerometer (TA) device modules have been previously developed at the Biomedical Systems Laboratory, UNSW. The student will implement the technology for localisation using ultrasound. Location beacons, with unique signatures, will be placed throughout the environment. The body-worn device will infer its location by associating with the ‘loudest’ beacon. The body-worn device will capture all TA and location information using a microprocessor and return this information to a database server via a Wi-Fi connection module mounted on the sensor board, for later analysis using existing analysis software.

Reference Material Links:	http://www.bsl.unsw.edu.au/ http://www.bsl.unsw.edu.au/docs/2008/A%20Wearable%20Triaxial%20Accelerometry%20System%20for%20Longitudinal%20Assessment%20of%20Falls%20Risk%20Revision%201.pdf

Project Title:	Experimental Study of Haemodialysis – Vascular Access Model

Name of Supervisor:	Tracie Barber

Email of Supervisor:	t.barber@unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Mechanical and Manufacturing Engineering

Faculty Research Area (Theme):	Health & Medical Technologies

School Research Area:	Thermofluids

Applicable to other Engineering schools/disciplines:	Biomedical Engineering

Abstract:	The average end stage renal disease patient will undergo haemodialysis (HD) treatment, in which the blood from the patient is cleaned of waste products via an external system, three or four times a week for 4 to 5 hours per session. Any minor imperfection in the extracorporeal system may become significant in the treatment of these patients due to the cumulative exposure time. Microbubbles in the system have been linked to lung injury and damage to the brain in chronic HD patients. In this project, we are trying to determine if it is likely that there will be microbubble formation at the vascular access point, by simulating a simplified AV fistula access point and catheter. You will be developing the experimental rig and taking some initial measurements, for comparison with CFD work we are also completing.

Research Environment:	You will be working in the Advanced Fluid Dynamics Laboratory, with Tracie, PhD students, and Joe (laboratory officer).

Novelty and Contribution:

Expected Outcomes:

Reference Material Links:

Project Title:	Flow visualization in a stenosed artery

Name of Supervisor:	Tracie Barber

Email of Supervisor:	t.barber@unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Mechanical and Manufacturing Engineering

Faculty Research Area (Theme):	Health & Medical Technologies

School Research Area:	Thermofluids

Applicable to other Engineering schools/disciplines:	Biomedical Engineering

Abstract:	Previous studies of stenosed arteries have shown evidence of potentially damaging “jetting” phenomena occurring during the diastolic stage of the flow cycle. We are currently developing computational models to study this effect, but experimental data is also needed. In order to conduct vascular fluid dynamics experiments in the Advanced Fluid Dynamics Laboratory, a pulsatile flow rig will be used. The experimental apparatus in this laboratory includes Lasers for flow visualization, Laser Doppler Anemometry and Particle Image Velocimetry systems and you will need to work out the best way to obtain images of the flow.

Research Environment:	You will be working in our Advanced Fluid Dynamics Laboratory, with Tracie (supervisor), PhD students, and Joe (Laboratory Officer).

Novelty and Contribution:

Expected Outcomes:

Reference Material Links:

Project Title:	Study of the fluid mechanics of micro/nano particle-pore interactions

Name of Supervisor:	Gary Rosengarten

Email of Supervisor:	g.rosengarten@unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:	g.rosengarten@unsw.edu.au

School:	School of Mechanical and Manufacturing Engineering

Faculty Research Area (Theme):	Water and Wastewater Engineering

School Research Area:	Thermofluids

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Chemical Sciences and Engineering Civil & Environmental Engineering Petroleum Engineering Sciences – Maths, Physics, Chemistry

Abstract:	All membranes, be them biological or synthetic, involve the interaction of small particles with pores. The selectivity of the membrane depends on hydrodynamics of the particle as it approaches and moves through the pore. In this project the student will carry out fully coupled computational fluid dynamics simulations of a single particle approaching a pore under a variety of conditions.

Research Environment:	The student will work in a team in the computational fluid mechanics laboratory. They will be associated also with the experimental group and simulations will be compared to experimental results at regular meetings.

Novelty and Contribution:	This research fits into the new area of biomimetics where we are trying to learn how nature sorts particles. Results will not only help in a fundamental understanding of particle pore interactions but also have applications in the design of more efficient membranes.

Expected Outcomes:	The expected outcomes of this project are the implementation of a fully coupled particle fluid model with brownian motion into current commercial software and the analysis of results under a variety of conditions including particle size and pore shape. If all goes well we would like to be able to write the results into into a journal article.

Reference Material Links:	Contact Dr. Rosengarten: g.rosengarten@unsw.edu.au

Project Title:	An Empirical Evaluation of RDF Stores

Name of Supervisor:	Sherif Sakr

Email of Supervisor:	ssakr@cse.unsw.edu.au

Name of Joint/Co-Supervisor:	Boualem Benatallah

Email of Joint/Co-Supervisor:	boualem@cse.unsw.edu.au

School:	School of Computer Science and Engineering

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Programming Languages and Software Engineering

School Research Area:	Web Services, E-Commerce, and other Web Technologies

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Surveying & Spatial Information Systems

Abstract:	The vision of the Semantic Web has brought about new challenges at the intersection of web research and data management. One fundamental research issue at this intersection is the storage of the Resource Description Framework (RDF) data. The RDF data model has been designed as a flexible representation of schema-relaxable or even schema-free information. In RDF, all data items are represented in the form of (subject, predicate, object) triples, also known as (subject, property, value) triples. Several research eorts have proposed different techniques for storing and querying RDF datasets. These techniques can be broadly classied into two main classes: 1) Native RDF data stores 2) Relational stores for RDF data. The target of the project is to achieve an experimental comparison, analysis and benchmarking of the state-of-the-art of the stores. This experimental analysis could lead to identifying the strengths and weaknesses of each proposed approach and identify a set of metrics which could be used as a right indicator for determining the suitable storage technique for each dataset and its query workload.

Research Environment:	The student will work in an international group of PhD students, researchers and senior researchers in the Service Oriented Computing Research Group. Some literature review will be required to learn the basics of RDF and SPARQL query processing. Experimental analysis skills will also be acquired during the project activities. Project results will have a very good chance to be published in a good venue.

Novelty and Contribution:	- Benchmarking the-state-of-the-art of RDF stores. - An analysis of the strengths and weakness of the dierent techniques of RDF stores. - Identifying a candidate set of metrics for recommending the suitable storage schema of the RDF dataset and its query workload.

Expected Outcomes:	- This project will involve experimental analysis and benchmarking of the state-of-the-art of RDS query processors. - Literature scan of RDF storing and querying bibliography.

Reference Material Links:	- RDF Processing Bibliography: http://www.cse.unsw.edu.au/ssakr/RDFBiblio.htm - An introduction to RDF and SPARQL http://www.dajobe.org/talks/200603-sparql-stanford/ http://research.talis.com/2005/rdf-intro/ http://www.rdfabout.com/quickintro.xpd

Project Title:	Flexible Modeling and Data Extraction of System Artifacts

Name of Supervisor:	Sherif Sakr

Email of Supervisor:	ssakr@cse.unsw.edu.au

Name of Joint/Co-Supervisor:	Boualem Benatallah

Email of Joint/Co-Supervisor:	boualem@cse.unsw.edu.au

School:	School of Computer Science and Engineering

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	eResearch (Knowledge and Services Engineering)

School Research Area:	Web Services, E-Commerce, and other Web Technologies

Applicable to other Engineering schools/disciplines:	Biomedical Engineering Surveying & Spatial Information Systems

Abstract:	Today every person has to manage a growing amount of artifacts. These data can be personal artifacts or business artifacts or combination of them. These artifacts can be: oce documents, emails, calendar data, pictures, database records, XML data,...,etc. Moreover, these data can be distributed over a huge range of storage devices like desktop computers, mobile phones, email servers, relational databases and WWW. The RDF data model has been designed as a flexible representation of schema-relaxable or even schema-free information. In RDF, all data items are represented in the form of (subject, predicate, object) triples, also known as (subject, property, value) triples. The target of this project is to design a flexible representation of system artifacts based on the RDF data model. The output of this project will be a tool which can generate the RDF data representation of system artifacts using a collection of wrappers. Given the data model of an artifact type X, the wrapper should extract the dened meta-data, data and relationships of artifacts of the type X and generates the RDF triples with respect to the artifact data model. The tool should be flexible enough to enable the end users to adjust these wrapper or even develop his own wrappers using a light-weight tools.

Research Environment:	The student will work in an international group of PhD students, researchers and senior researchers in the Service Oriented Computing Research Group. Some literature review will be required to learn the basics of RDF and data management of heterogenous artifacts. Data analysis skills will also be acquired during the project activities. Project results will have a very good chance to be published in a good venue.

Novelty and Contribution:	- Providing a flexible tool of dening and managing data models of heterogeneous artifacts. - A set of wrapper to extract the data from the system artifacts and generate RDF triples.

Expected Outcomes:	- This project will involve developing flexible wrappers to extract metadata and data from heterogenous artifacts. - Literature scan of modeling and managing heterogonous artifacts.

Reference Material Links:	Contact supervisor

Biomedical Engineering Research Areas

Biomedical Engineering Projects

Biomaterials and Tissue Engineering

Physiological Measurement, Modelling and Neurostimulation

Projects offered by other Engineering Schools that may be of interest are: