Taste of Research Scholarships

Project Title:	Characterization and Exploration of Online Social Networks such as Twitter

Name of Supervisor:	Dr. Sebastien Ardon

Email of Supervisor:	sebastien.ardon@nicta.com.au

Name of Joint/Co-Supervisor:	Dr. Anirban Mahanti

Email of Joint/Co-Supervisor:	anirban.mahanti@nicta.com.au

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	NICTA Project

Faculty Research Area (Theme):	Embedded Systems and Communications

School Research Area:	Data and Mobile Networks

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

Abstract:	In the past few years, a discipline referred to as the “new science of networks” [Watts 2004] has emerged. This discipline is concerned with the study of complex systems such as the Internet, the Web, electrical power grids, and online social networks, as a graph [Watts 2004]. Research in this area has focussed on characterizing the network structure because structure bears functionality implications [Strogatz 2001, Mislove 2007]. For example, the topology of the Twitter social network, as defined by denoting users as nodes and relationships between users as edges in the graph, affects how information propagates in this social network. Questions such as, “how quickly and how far does information travel in the network?” can be answered by studying the structure of the social network. This project will uncover interesting facets of online social network usage. The overarching goal guiding this work is the need for models that capture how social networks are used and how they evolve. Example questions of interest include the following: i) how do users form links in these networks?, ii) Are all links equal?, iii) How do relationships govern dissemination of information?, iv) How far and how quickly does information propagate in a network?, and v) Do “famous users” (e.g., users with many friends or followers) become even more famous over time?

Research Environment:	The successful candidate will have the opportunity to work within the Network Systems group at the Australian Technology Park premises of NICTA (http://www.nicta.com.au/), within a small group comprised of junior and senior research staff, and PhD students. IT support and resources will be provided by NICTA. Team of more than one student will be considered for this project (pending the successful admission in the Taste of Research Scholarship program) Required background, skills, and expectations: Willingness to learn and scientific curiosity is essential. The successful candidate will be expected to read and summarize a few research articles from different fields. Students with strong analytic and/or programming skills are encouraged to apply. Background in algorithms, data structures, and computer networking will be helpful. Knowledge of at least one high-level programming language (such as Python, or Ruby) will also be helpful.

Novelty and Contribution:	Applications of this work, apart from the contribution to knowledge and understanding, include the design of smart content distribution networks.

Expected Outcomes:	- Design an appropriate data / meta-data collection methodology which leverages APIs provided by services such as Twitter - Design and Develop data collection tools, which use those API. - Design and develop data analysis tools, which may include data visualization. Data analysis and visualization tools should be able to cope with the large amounts of data they will have to churn to gather meaningful information - Extract salient features of the resulting data set, by answering one or more of the research questions described above. - Write a report describing the main findings from the analysis.

Reference Material Links:	[Watts 2004] Duncan J. Watts. The “New” Science of Networks, Annu. Rev. Socio., 2004, 30:243-70. [Strogatz 2001] Steven H. Strogatz. Exploring Complex Networks, Nature, Vol. 410, March 2001. [Mislove 2007] A. Mislove, M. Marcon, K. Gummadi, P. Druschel, and B. Bhattacharjee, Measurement and Analysis of Social Networks, Proceeding of ACM Internet Measurement Conference, San Diego, USA, October 2007.

Project Title:	Content Distribution for the National Broadband Network

Name of Supervisor:	Dr Tim Moors

Email of Supervisor:	t.moors AT unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Embedded Systems and Communications

School Research Area:	Data and Mobile Networks

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

Abstract:	The recently announced $43B National Broadband Network promises to revolutionise Australian telecommunications. Deploying fibre to 90% of Australian premises will entail a massive engineering effort, even if much of it will be routine digging of trenches and installation of equipment. However, the implications of 100Mb/s to 90% of the population have not yet been fully considered. For example, how can a content provider match tens of Mb/s download speeds sought by thousands of concurrent users? Particularly when the users need not be synchronised in downloading the same content at the same time as we are accustomed to with broadcast media. This project will investigate how content distribution systems (similar to services provided by companies such as Akamai) might be adapted to allow content providers to match the capacity of customer download that the NBN promises to provide.

Research Environment:	You will work with senior researchers and have access to a comprehensive laboratory of network equipment (e.g. routers, switches, and test equipment).

Novelty and Contribution:	The NBN promises to drastically change Australian telecommunications and this project will contribute to that.

Expected Outcomes:	You will gain experience installing CDN software on lab computers, and using CDN services that already exist. You will also review state of the art research into CDNs and consider how it could be applied to distribute content across a NBN. This project will extend your knowledge of network protocols, and will equip you with skills for future research projects in the field of networking as well as participation in the massive NBN project.

Reference Material Links:	This project will build upon skills/knowledge that you have gained from networking courses such as UNSW's TELE3118. Lots of material (web sites & papers) is avilable if you search for "content distribution"; some easy-reading introductions are C. Deleuze: "Content Networks" Internet Protocol Journal, 7(2):2-11, Jun. 2004 and http://mags.acm.org/queue/200901/?pg=3D8

Project Title:	Developing Novel WiMAX Applications in Test Beds

Name of Supervisor:	A/Prof Jinhong Yuan

Email of Supervisor:	J.Yuan@unsw.edu.au

Name of Joint/Co-Supervisor:	Dr Wei Zhang

Email of Joint/Co-Supervisor:	w.zhang@unsw.edu.au

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Embedded Systems and Communications

School Research Area:	Data and Mobile Networks

Applicable to other Engineering schools/disciplines:

Abstract:	Next generation wireless and mobile communications require transmitting and receiving multimedia information with high quality and throughput. WiMAX, the Worldwide Interoperability for Microwave Access, is an advanced communications technology that provides wireless data in a variety of ways, from point-to-point links to full mobile cellular type access. The technology is based on the IEEE 802.16 standard, which is also called WirelessMAN. The name "WiMAX" was created by the WiMAX Forum, which was formed in June 2001 to promote conformance and interoperability of the standard. The school of Electrical Enginnering and Telecommunications at University of New South Wales has a set of software/hardware development kit for WiMAX transmitter and receiver, including transmission and receiving boards, software packages, advanced signal processsing board. The project is proposed to develop novel applications based on the kit. The candidate will work with a senior researcher and postgraduate research students. The work could include transmission and receiving algorithm design, system debug, interface design, etc. Programming is required to implement the designed schemes. Further enquiry, please contact A/Prof. Jinhong Yuan at 9385 4244 or Jinhong@ee.unsw.edu.au.

Research Environment:	The candidate will work with senior researchers, engineers, and postgraduate research students to develop advanced technologies.

Novelty and Contribution:	The key novelty of the project is developing cutting edge applications based on the current Wireless (WiMAX) standards.

Expected Outcomes:	The candidates are expected to demonstrate the designed WiMAX applications on the test bed of the mobile lab.

Reference Material Links:	Please consult with the project supervisor

Project Title:	Experiments on the NICTA NetFPGA platform

Name of Supervisor:	Vijay Sivaraman

Email of Supervisor:	vijay@unsw.edu.au

Name of Joint/Co-Supervisor:	Arun Vishwanath

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

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	NICTA Project

Faculty Research Area (Theme):	Embedded Systems and Communications

School Research Area:	Data and Mobile Networks

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

Abstract:	The NetFPGA platform, developed at Stanford University, implements a full IP router on an FPGA board. The entire source code is available, which allows researchers to modify components inside a router or develop their own routing modules. Through NICTA we have access to 10 such platforms at UNSW. The aim of this project is to develop novel components for this router that can then be used for the experimental testing of routing strategies, buffer sizing studies, firewalling capabilities, etc. It is expected that the candidate will work closely with PhD students to augment their research.

Research Environment:	The candidate will work closely with a research group including PhD students. The primary aim of the candidate is to prototype reserach ideas on the NetFPGA platform and run experiments to profile their performance.

Novelty and Contribution:	It is expected that prototyping new networking ideas on a Gigabit line-speed IP router will lead to working demosntrations and technical papers. One good venue for presenting such work is the NetFPGA workshop held at Stanford, see http://netfpga.org/wordpress/netfpga-developers-workshop/

Expected Outcomes:	The expected outcomes include working prototypes for demonstration to the networking community as well as technical papers in venues such as the aforementioned workshop.

Reference Material Links:	Comprehensive information on the NetFPGA platform is available at http://netfpga.org/. The suitable candidate is expected to have a high level of comfort with either hardware description languages such as Verilog or software languages such as C.

Project Title:	How can middleboxes (e.g. firewalls) tell end-users what policies block their network access?

Name of Supervisor:	Dr Tim Moors

Email of Supervisor:	t.moors AT unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Embedded Systems and Communications

School Research Area:	Data and Mobile Networks

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

Abstract:	Many network middleboxes, such as firewalls, can block user's access to the Internet in order to protect them from phishing (fake web sites that users unwittingly supply with their real credentials) or to enforce acceptable use policies (e.g. blocking access to recreational web sites from a business premises). Conventional firewalls silently discard traffic that violates policies, without notifying the source of the traffic. While this behaviour is appropriate for externally originating traffic, if applied to internally originating traffic, users may mis-attribute the obstructed network access to network unreliability (diminishing their view of the quality of network service) and re-attempt access from another network (e.g at home) and by so doing be exposed to phishing attacks in any case. This project will investigate how middleboxes can signal to their users the reason for blocking access, when appropriate.

Research Environment:	You will work with senior researchers and have access to a comprehensive laboratory of network equipment (e.g. routers, switches, and test equipment).

Novelty and Contribution:	Existing middleboxes respond to policy violations with either silence or sending generic error signals (ICMP Host Unreachable or TCP reset). This project will contribute new signalling mechanisms that are more informative and better inform users of the reason for blocking access.

Expected Outcomes:	After reviewing middlebox systems you will installing open-source firewall software on multi-port lab PCs and then extend that software (likely in C/C++) to expand the signalling capacity of such middleboxes. You will likely extend existing protocols (e.g. ICMP error codes) and may document and publicise those extensions as an IETF Internet Draft and write a research paper suitable for publication in a conference. This project will extend your knowledge of network protocols and security, and will equip you with skills for future research projects in the field of networking.

Reference Material Links:	This project will build upon skills/knowledge that you have gained from networking courses such as UNSW's TELE3118 and security courses such as UNSW's TELE3119.

Project Title:	Removing private information from packets captured on networks

Name of Supervisor:	Dr Tim Moors

Email of Supervisor:	t.moors AT unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Embedded Systems and Communications

School Research Area:	Data and Mobile Networks

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

Abstract:	Packets that carry information across networks, such as the Internet, can be captured by sniffing tools such as Wireshark, tcpdump and network analyzers. Such captured packets are useful for research purposes (e.g. characterising network traffic and replaying to apply realistic workloads to new network systems) as well as to allow network administrators to troubleshoot network problems. However, they may contain private information (such as passwords, cookies, and names/addresses of machines, information and humans) that users would prefer not be recorded and that are often unnecessary for the intended purposes of the packet capture. The aim of this project is to research and implement an automated packet sanitization system that can eliminate private information that is stored deep within packets, while preserving information needed for research and troubleshooting purposes.

Research Environment:	You will work with senior researchers and have access to a comprehensive laboratory of network equipment (e.g. routers, switches, and test equipment).

Novelty and Contribution:	Most existing packet sanitisation systems are limited to lower layers of the protocol stack (e.g. network layer), whereas this project will investigate deep packet inspection so that information carried deep within packets (e.g. HTTP headers and even within HTTP objects) can also be sanitized. The top US research funding body, the NSF, currently sponsors research into this topic http://www.ists.dartmouth.edu/projects/NetSANI.html . Challenges include preserving enough information to allow use of the captured packets while still censoring private info, e.g. censoring IP addresses may make TCP/UDP checksums appear invalid when they weren't in the original packet & some troubleshooting analyses don't need to know the value of a cookie but do need to know whether the value CHANGED between successive HTTP requests.

Expected Outcomes:	After reviewing existing packet sanitization systems, you will develop software (likely in C/C++ for ease of interface to capture libraries such as winpcap/libpcap) to sanitize network packets and write a research paper suitable for publication in a conference. This project will extend your knowledge of network protocols and equip you with skills for future research projects in the field of networking.

Reference Material Links:	This project will build upon skills/knowledge that you have gained from networking courses such as UNSW's TELE3118. Web searching for packet sanitization/anonymisation (with US/English variants of spelling) will reveal related work, e.g. Tcpdpriv, sanitize, scrub-tcpdump, tcpurify and PktAnon Search IEEE Xplore & ACM Digital library to find papers like: M. Bishop et al: “Some Problems in Sanitizing Network Data”, Proc. IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, (WETICE), pp. 307-12, Jun. 2006

Energy Systems

Project Title:	Ultra high frequency detection of partial discharges

Name of Supervisor:	Dr Toan Phung

Email of Supervisor:	toan.phung@unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Signal Processing & Control

School Research Area:	Energy Systems

Applicable to other Engineering schools/disciplines:

Abstract:	The electrical insulation used in high-voltage power system equipment (e.g. high-voltage transformers, rotating machines, cables, switchgear, etc) is subjected to severe operating electrical stress. Its breakdown is catastrophic and thus it is important to prevent this from happening. This can be achieved by monitoring the condition of the insulation. Because of the high stress during normal operation, there will be some partial discharges (PD) which is expected. The extent of the PD activity in terms of its magnitude and repetition rate is indicative of the insulation condition. PDs emit electromagnetic waves over a wide frequency range which can be detected with a suitable antenna. The aim of this project is to work together with another Ph.D student to design and construct a UHF antenna, set up various types of discharge sources inside a transformer tank, and conduct high-voltage experiment to detect PDs using the UHF antenna. At the end of the project, the student will have gained knowledge in UHF antenna design via the use of CAD software and its fabrication, and practical skills in high-voltage testing.

Research Environment:	This project forms part of a range of research activities conducted in the high-voltage laboratory in the area of condition monitoring of power system equipment. The laboratory is well equipped with facilities for high-voltage generation, testing and measurement. Some of the research work involves on-site testing in the utility substations. Currently, there are four Ph.D students.

Novelty and Contribution:	PD detection in the UHF frequency range is a new diagnostic technique. This project will help to determine the viability of this technique as applied to condition monitoring of power system equipment.

Expected Outcomes:	A prototype of a UHF detection system (antenna, amplifier, analyser) that is able to detect low level partial discharges.

Reference Material Links:	IEEE Xplore digital library: http://ieeexplore.ieee.org/Xplore/guesthome.jsp J. Lopez-Roldan, T. Tang and M. Gaskin, “Optimization of a Sensor for Onsite Detection of Partial Discharges in Power Transformers by the UHF Method”, IEEE Trans. on Dielectrics and Electrical Insulation, Vol.15, No.6, December 2008, pp.1634-1639.

Microelectronics and Quantum Computing

Project Title:	Design and Modelling of Silicon Spin Qubits for Quantum Computing

Name of Supervisor:	Professor Andrew Dzurak

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

Name of Joint/Co-Supervisor:	Dr Andrea Morello

Email of Joint/Co-Supervisor:	a.morello@unsw.edu.au

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	School Project

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

School Research Area:	Microelectronics and Quantum Computing

Applicable to other Engineering schools/disciplines:	Computer Science & Engineering Photovoltaic and Renewable Energy Engineering

Abstract:	Silicon spin-based qubits have enormous potential for the development of powerful quantum computer processors of the future. This research project will focus on the development of computer models for prototype quantum computer devices, using the commercial software ISE TCAD on our Centre’s linux cluster. It will involve liaison with other students and researchers to identify a specific modelling problem releavnt to a central spin qubit device under investigation in the Centre, followed by a period of model development and calculation of results.

Research Environment:	The student will work in a friendly team environment at the Centre for Quantum Computer Technology amongst PhD students, postdoctoral researchers and academic staff. The Centre for Quantum Computer Technology (see www.qcaustralia.org) is focused on the fundamental physics and technology of fabricating a revolutionary silicon based solid state quantum computer prototype. Quantum computers represent the next generation technology in computing and electronics. Through manipulation of quantum states, they offer parallel processing power and capacity in applications of commercial and national significance.

Novelty and Contribution:	This research project is part of a major worldwide effort to develop spin-based qubits. As such, it is of immediate interest to the international research community.

Expected Outcomes:	The project will involve liaison with other students and researchers to identify specific modelling problems and then a period of model development, followed by a written report on the project.

Reference Material Links:

Project Title:	Microelectronic Circuit Design for Biomedical Implants

Name of Supervisor:	Dr Torsten Lehmann

Email of Supervisor:	tlehmann@unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Embedded Systems and Communications

School Research Area:	Microelectronics and Quantum Computing

Applicable to other Engineering schools/disciplines:

Abstract:	The aim of this project is to design a low-power CMOS integrated circuit for use in biomedical implants such as a bionic eye. Researchers at UNSW has been working on the bionic eye for many years and the actual circuit to be designed in this project depends on the current state of the research as well as the interests of the students involved; examples include neural response amplifiers, high-efficiency power supplies, and accurate AD/DA converters. The project consists of three parts: 1) familiarisation with integrated circuit design techniques, 2) design specification, and 3) design of the circuit using professional CAD software. The project will be carried out in the microsystems computer laboratory working with senior researchers in the field. At the end of the project, the participants will have strengthened their skills in microelectronic circuit design and the use of CAD tools, and will have developed research skills in preparation for their thesis work.

Research Environment:	It is preferred that two students should jointly undertake this project, however it can be tailored for a single student if necessary.

Novelty and Contribution:

Expected Outcomes:

Reference Material Links:

Project Title:	Microelectronic Circuit Design for Quantum Computing

Name of Supervisor:	Dr Torsten Lehmann

Email of Supervisor:	tlehmann@unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Embedded Systems and Communications

School Research Area:	Microelectronics and Quantum Computing

Applicable to other Engineering schools/disciplines:

Abstract:	The aim of this project is to design a low-power, low-temperature CMOS integrated interface circuit for silicon quantum computers. CMOS interface circuits for quantum computers is an emerging field of research at UNSW and the actual circuit to be designed in this project depends on the current state of the research as well as the interests of the students involved; examples include pulse generators, low-noise amplifiers, fast AD/DA converters. The project consists of three parts: 1) familiarisation with integrated circuit design techniques, 2) design specification, and 3) design of the circuit using professional CAD software. The project will be carried out in the microsystems computer laboratory working with senior researchers in the field. At the end of the project, the participants will have strengthened their skills in microelectronic circuit design and the use of CAD tools, and will have developed research skills in preparation for their thesis work.

Research Environment:	It is preferred that two students should jointly undertake this project, however it can be tailored for a single student if necessary.

Novelty and Contribution:

Expected Outcomes:

Reference Material Links:

Project Title:	RF MEMS

Name of Supervisor:	Rodica Ramer

Email of Supervisor:	ror@unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	School Project

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

School Research Area:	Microelectronics and Quantum Computing

Applicable to other Engineering schools/disciplines:

Abstract:	Current and future wireless appliances require increased functionality, frequency of operation, and component integration along with reduced manufacturing costs, size, weight, and power consumption. RF MEMS is the the technology that can satisfy the requirements of modern communication systems and is compatible with existing integrated circuits and monolithic microwave integrated circuits.

Research Environment:	The applicants will work with senior researchers, engineers and postgraduate research students to design simulate and fabricate simple RF MEMS.

Novelty and Contribution:	The novelty of this project consists in the develpment of performant devices part of a modern communication system.

Expected Outcomes:	The applicants are expected to learn high frequency simulation software and fabrication techniques and ultimately design simple RF MEMS structures.

Reference Material Links:	G. Rebeiz - RF MEMS Theory, Design and Technology, J. Wiley 2005

Photonics

Project Title:	Diamond-base photonics for quantum communication

Name of Supervisor:	A/Prof François Ladouceur

Email of Supervisor:	f.ladouceur@unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	School Project

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

School Research Area:	Photonics

Applicable to other Engineering schools/disciplines:	Photovoltaic and Renewable Energy Engineering

Abstract:	Diamond-based photonics is currently being developed by many research groups worldwide because of its ability to support quantum key distribution schemes by incorporating single photon sources. The Photonics Group at UNSW, in collaboration with Physics at University of Melbourne has developed a scalable fabrication process enabling the production of all-diamond waveguides and devices. This technology is still in its refinement stage and much work still needs to be done for it to reach maturity. In particular, the characterisation of the waveguides and devices still needs to be completed. The project looks at working with the device designers and set-up characterisation experiments for our first generation of diamond-based devices. Of particular relevance would be the determination of linear loss and wavelength responses.

Research Environment:	The candidate will work within a friendly team consisting of senior researchers, engineers, and postgraduate research students.

Novelty and Contribution:	The outcome of this project would contribute to the development of a technology that will support new quantum communication systems and also, potentially, quantum computer architectures based on linear-optics.

Expected Outcomes:	The project will involve collaboration with Physics at UNSW and at UoM concerning characterisation techniques, followed by a written report.

Reference Material Links:

Project Title:	Electro-active gels for display application

Name of Supervisor:	A/Prof François Ladouceur

Email of Supervisor:	f.ladouceur@unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Advanced Materials

School Research Area:	Photonics

Applicable to other Engineering schools/disciplines:	Chemical Sciences and Engineering Photovoltaic and Renewable Energy Engineering

Abstract:	The display industry has yet to settle on a specific technology to produce the next generation of flexible (conformal) displays. Much research is now being done on developing was has been referred to a e-ink, or electronic ink. A class of material know as hygrogels exhibit very sharp phase transition between its liquid and gel states. In the process, the optical properties of the materials change abruptly through the supra-molecular rearrangement of the gel monomers. This could then form the basis for a black and white pixel. This research project would look at the characterisation of the phase transition and in particular at the effect of the electric field on so-called electro-active hydrogels.

Research Environment:	The candidate will work within a friendly team consisting of senior researchers, engineers, and postgraduate research students in collaboration with Chemical Engineering and Chemistry.

Novelty and Contribution:	Positive outcomes to this project could realistically contribute to the creation of new e-paper technologies as this field is still evolving rapidly.

Expected Outcomes:	Experimental characterisation of new classes of hydrogels and in particular a better understanding of the dynamics of their phase transition.

Reference Material Links:

Project Title:	Modelling of phase transition in electro-active hygrogels

Name of Supervisor:	A/Prof François Ladouceur

Email of Supervisor:	f.ladouceur@unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Advanced Materials

School Research Area:	Photonics

Applicable to other Engineering schools/disciplines:	Chemical Sciences and Engineering Computer Science & Engineering Photovoltaic and Renewable Energy Engineering

Abstract:	A class of material known as hygrogels exhibits a very sharp phase transition between its liquid and gel states. In the process, the optical properties of the materials change abruptly through the supra-molecular rearrangement of the gel monomers. One of the many applications of such class of materials would be the development of electronic ink, or e-ink, for the next generation of flexible (conformal) display. The project consist in the development of a thermodynamic theoretical framework together with simulation software to study the dynamics of the phase transition. Of particular importance would be the speak of gelation and the its sensitivity around the phase-transition point in terms of temperature and other external influences.

Research Environment:	The candidate will work within a friendly team consisting of senior researchers, engineers, and postgraduate research students in collaboration with Chemical Engineering and Chemistry.

Novelty and Contribution:	Development of theoretical framework and simulation software for the study of phase transition in electro-active hydrogels.

Expected Outcomes:	Positive outcomes to this project could realistically contribute to the creation of new e-paper technologies as this field is still evolving rapidly.

Reference Material Links:

Signal Processing

Project Title:	Analysis of similar voices for speaker recognition

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:	Computer Science & Engineering

Abstract:	Biometric identity verification systems play an important role in our daily lives. Applications include Automatic Teller Machines (ATM), banking and share information retrieval, and personal verification for credit cards. Among the biometric techniques, authentication of speakers by his/her voice is of great importance, since it employs a non-invasive approach and is the only available modality in many applications. However, the performance of Automatic Speaker Recognition (ASR) systems degrades significantly in the cases of speakers with similar voices since they cannot be distinguished by the spectral features with linear speech analysis. The objective of this research is to identify discriminative features for similar voices. Knowledge of signal processing and programming skills in MATLAB is required.

Research Environment:	This project will be conducted as part of ongoing research efforts by the speech processing research group. Members of this group are enthusiastic, cooperative and knowledgeable, and should impart a good sense of how research can contribute to the state-of-the-art systems

Novelty and Contribution:	At present, the speaker recognition studies are largely focused on the compensation of the variations of the records from the same speaker which sound differently due to the environmental or intra-speaker artifacts and much less research has been done to discriminate the records of different speakers with similar voices. The student will contribute towards development of new features to address this issue.

Expected Outcomes:	An implementation of a speaker recognition system making use of the developed features. 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] D. Reynolds, “An overview of automatic speaker recognition technology,” IEEE Proc. of International Conference on Acoustics, Speech and Signal Processing, vol. 4, pp. 4072_4075, 2002. [2] J.P.Campbell,”Speaker recognition: a tutorial,” Proceedings of the IEEE, Vol. 85, No. 9. (1997), pp. 1437-1462.

Project Title:	Cognitive Load Analysis in Pen Interaction

Name of Supervisor:	Dr. Fang Chen

Email of Supervisor:	fang.chen@nicta.com.au

Name of Joint/Co-Supervisor:	Dr. Julien Epps

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

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	NICTA Project

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Signal Processing

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

Abstract:	Cognitive load is variously described as the level of perceived mental effort associated with thinking and reasoning in a human. Tasks performed under stressful conditions, or requiring complex hand/eye coordination, can markedly increase cognitive load, and thus potentially interfere with other thought processes and consequently reduce overall task performance. Feasibility and efficiency of pen interaction is an interesting problem that has been extensively studied in the scenario of multimodal interfaces. However, little research exists investigating how pen gestures and handwriting inputs can be used to automatically estimate the cognitive load of a user in real-time. The electronic map, as an information intensive media, provides many possibilities of pen-based interaction, where pen gestures could include clicking, drawing, tracking or much more complex traces. However, as the information organised on a map resides in different levels of a hierarchy, we need to evaluate whether pen gestures are simple enough to memorize, how much cognitive load will change as we increase the size of the gesture corpora, and in which contexts specified pen gestures should be disabled to keep the cognitive load at an acceptable level. The incorporation of pen gestures and handwriting inputs for cognitive load measurement is particularly relevant to the context of emergency services operators. This project aims to explore new methods to measurement cognitive load in non-intrusive way. Initial focus will be placed on experiment design and potential feature extraction. Later on in the project will also be researching the use of statistical algorithms or classification models to investigate the correlations between cognitive load and pen interaction features.

Research Environment:	The selected student will work with a team of senior researchers in the field of signal processing and human-computer interaction, at the NICTA premise inside the Australian Technology Park.

Novelty and Contribution:	Cognitive load measurement via pen interaction is a brand new and promising field of research. This work will have big impact on the research agenda of objective cognitive load mesaurement. It will enable researchers and developers to build intelligent human-machine systems that are adaptive to a user’s cognitive load.

Expected Outcomes:	The main research is to design experiments to capture potential pen-based features for cognitive load measurement and to analysis the data set to validate some of the features. You will work with a team of researchers on developing experiments on capturing pen gesture changes as cognitive load increases. Some literature review will be required to learn the basics of pen gesture production. Signal processing and statistical analysis will also be required to extract features and investigate correlations between cognitive load and pen interaction behaviour. You may have the opportunity to publish the results if they are of good quality.

Reference Material Links:	There is very little work specifically in this area, but there is plenty of related literature. Here are some references to get started: http://www.csis.pace.edu/~ctappert/dps/pdf/pen-tappert.pdf http://www.ee.bgu.ac.il/~dinstein/stip2002/HandwritingRecognitionSurveyPAMI.pdf The book "Can stress be measured by handwriting analysis? The effectiveness of the analytic method" is also relevant, but needs to be sourced using inter-library loan.

Project Title:	Cognitive Load Measurement via Speech

Name of Supervisor:	Dr. Eric Choi

Email of Supervisor:	eric.choi@nicta.com.au

Name of Joint/Co-Supervisor:	Dr. Julien Epps

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

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	NICTA Project

Faculty Research Area (Theme):	Signal Processing & Control

School Research Area:	Signal Processing

Applicable to other Engineering schools/disciplines:

Abstract:	Cognitive load is variously described as the level of perceived mental effort associated with thinking and reasoning in a human. Tasks performed under stressful conditions, or requiring complex hand/eye coordination can markedly increase cognitive load, and thus potentially interfere with other thought processes and consequently reduce overall task performance. If a user’s existing level of cognitive load can be assessed in real time, an intelligent system could potentially adapt its behaviours to implement strategies that reduce the cognitive load and correspondingly increase the task performance. While the concept of cognitive load itself may be more a theoretical construct, its measurement can lead to numerous real-world applications, e.g. in call centres for gathering customer emotional intelligence, and in electronic gaming for tailoring the plots according to a gamer’s cognitive and emotional states. We have been researching a non-intrusive and online speech based method for measuring cognitive load by analysing speech data gradually over a series of tasks. This project aims to research on various noise robust techniques to significantly improve the effectiveness of a measurement algorithm. Initial focus will be placed on robust feature extraction. Later on in the project will also be researching the use of probabilistic fusion to integrate information from multiple feature representations and classification models.

Research Environment:	The selected student will work with a team of senior researchers in the field of signal processing and human-computer interaction, at the NICTA premises inside the Australian Technology Park.

Novelty and Contribution:	Cognitive load measurement via speech is a novel and promising field of research. This measurement method will provide a direct and objective measurement that overcomes many of the shortcomings of other indirect and subjective methods. It will enable researchers and developers to build intelligent systems that are adaptive to a user’s cognitive load.

Expected Outcomes:	The student should have knowledge in signal processing and pattern recognition. Good programming skills in Java/C/C++ and Matlab would be desirable. It is expected that methods and software will be developed to extract robust speech features and perform probabilistic fusion. Objective evaluation of the various algorithms/methods will be required.

Reference Material Links:	• Yap, T. F., Ambikairajah, E., Choi, E. and Chen, F., “Phase Based Features for Cognitive Load Measurement System”, Proc. IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP’09), Taipei, Taiwan, April 2009, pp. 4825-4828. • Yin, B., Chen, F., Ruiz, N. and Ambikairajah, E., "Speech-based Cognitive Load Monitoring System", Proc. IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP'08), Las Vegas, March/April 2008, pp. 2041-2044. • Decision Support for Incident Management (http://www.nicta.com.au/dsim). • ORANGE: a component-based machine learning/data mining software (http://www.ailab.si/orange/).

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:	Reliability, safety, and control of autonomous ground vehicles

Name of Supervisor:	Dr Ray Eaton

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

Name of Joint/Co-Supervisor:	A/Prof Jayantha Katupitiya

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

School:	School of Electrical Engineering and Telecommunications

For CSE and EET Projects:	School Project

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Signal Processing

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

Abstract:	In developing autonomous vehicles, it is not only important that the control design and implementation is successful, but also that the vehicle is reliable and that safety issues have been addressed for both the vehicle and the surroundings. The Autonomous Systems Group currently has several vehicles which will be used for autonomous operation, with primary application to precision autonomous farming. These include a small tractor and a small footprint weeding vehicle called GreenWeeder, both fully instrumented and autonomous, as well as other vehicles being instrumented for autonomous operation now. These vehicles are required to perform precisely and reliably in the field with an emphasis placed on safety. This project will work on two fronts: Firstly, the identification of hazards and potential faults will be required. This will be followed by the development of appropriate software/hardware to detect and tolerate the faults, and prevent and manage any hazards. Secondly, the implementation and testing of novel high-level trajectory tracking controllers will be required in order to keep this work at the forefront of autonomous vehicles research. This project is for a single student.

Research Environment:	The researcher will be working along side postgraduate research students, as well as academics in the autonomous systems group, gaining exposure to real vehicles and a realistic and typical research environment.

Novelty and Contribution:	This project will contribute to the ongoing and state of the art research into precise ground vehicle guidance. In particular, the research seeks to develop novel control algorithms for this purpose which take into account realistic operating conditions.

Expected Outcomes:	* The deployment of a system ensuring safe and reliable autonomous vehicle operation. * The testing of novel high-level controllers for precise trajectory tracking. * Gaining of knowledge and appreciation of the real-time control and operation of autonomous vehicles in a real environment, as well as the importance of safety and reliability. * Exposure to real-time operating systems and controller coding in C, as well as practices in fault detection and tolerance.

Reference Material Links:	For approrpiate links, please contact the project supervisor.

Project Title:	Signal processing in bioinformatics: Periodicity analysis of DNA sequence data

Name of Supervisor:	Dr Julien Epps

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

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

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:	Computer Science & Engineering Sciences – Maths, Physics, Chemistry

Abstract:	With the advent of increasingly faster and more accurate technologies for sequencing and characterizing genomic sequences such as DNA, the search to understand the functions of genomic sequence data is gaining momentum. One tool for uncovering structure is periodicity analysis. Signal processing has a rich history of spectrum estimation methods, however these are (i) intended for numerical data; (ii) often linear in frequency rather than in period; and (iii) rarely considered in terms of the statistical significance of dominant periods (frequencies). Genomic data are often symbolic, and in many cases only integer periods are of interest (these periods have physical significance). Further, a dominant period may only be of interest to a biologist if the extent of its significance (dominance) can be meaningfully quantified. This topic compares different signal processing methods for periodicity analysis of genomic sequence data, in terms of dominant period detection, multiple period detection, and periodicity profiling. New methods for period estimation, based on information-theoretic principles, will also be considered. Methods for assessing statistical significance of various methods will also be sourced and applied. Finally, these methods will be applied to real data, for example characterizing nucleosome periodicity in real DNA data. This research is conducted jointly with the ANU John Curtin School of Medical Research.

Research Environment:	Working alongside postgraduate signal processing students, and joining the research group for regular seminars, you will be exposed to a typical reearch environment. The postgrad students in the signal processing lab have a wealth of knowledge and experience to share, which will help to guide you in your project and your aspirations beyond undergraduate studies.

Novelty and Contribution:	There are a tiny handful of researchers working on related areas worldwide, but a very large community of biologists and computational genomics researchers who use periodicity analysis tools routinely. What is needed is not only the development of new, more accurate tools, but a deeper understanding of the limitations of periodicity analysis in the contexts in which it has been applied. Results of this project will be immediately used by computational genomics researchers for structure discovery within DNA sequences.

Expected Outcomes:	Development of new periodicity analysis algorithms, coded in MATLAB, together with documentation. Evaluations of periodicity estimation techniques on various databases. Validation of statistical methods for determining the significance of dominant periodicities.

Reference Material Links:	Knowledge from undergrad signal processing (ELEC3104) and maths courses would be helpful. http://www.hindawi.com/getpdf.aspx?doi=10.1155/2009/924601 (example method and evaluation) http://bioinformatics.oxfordjournals.org/cgi/reprint/15/3/187.pdf (example application) Background knowledge from ELEC3104 and maths courses will be helpful for this topic.

Systems & Control and Biomedical Systems

Project Title:	Tele – Rehabilitation for patients with chronic lung disease

Name of Supervisor:	Branko Celler

Email of Supervisor:	b.celler@unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

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:

Abstract:	Chronic obstructive pulmonary disease (COPD) affects 1.2 million Australians and cost our health system almost one billion dollars in 2008. Pulmonary Rehabilitation (PR) is an effective treatment which reduces symptoms, disability and hospitalisations. However less than 1% of Australians with COPD receive PR each year. Reasons include few PR programs in rural and regional areas, a shortage of health professionals and poor access to existing programs for disabled patients. Tele-Rehabilitation will allow remotely located and socially isolated COPD patients to undertake PR safely in their homes. Tele-Rehabilitation will replicate the two crucial aspects of PR: (1) real time physiological monitoring of oxygen levels and heart rate; and (2) video links to clinicians and other patients for peer support. A recent patent search found no existing technology with this capability. We anticipate that Tele-Rehabilitation will have application in all chronic diseases where rehabilitation is a cornerstone of care, including heart disease and diabetes.

Research Environment:	The Biomedical Systems Laboratory (BSL) and Laboratory of Health Telematics in the School of Electrical Engineering and Telecommunications, and collaboration with clinicians at the Alfred Hospital and La Trobe University. Supervision by Prof. Branko Celler and A/Prof. Anne Holland.

Novelty and Contribution:	Remote at home rehabilitation for Chronic Obstructive Pulmonary Disease (COPD) has not previously been attempted. The project will leverage the engineering and research capability of the BSL with the clinical experience and understanding of the problem from experienced clinicians to deliver a practical and effective clinical outcome.

Expected Outcomes:	Work progressing towards a working prototype that can be clinically tested in a laboratory environment with young fit individuals. The prototype will include wireless LAN communications, real time monitoring of key physiological parameters and simultaneous video conferencing.

Reference Material Links:	A detailed technical specification is available to the successful candidate. The requirements for the remote site to be able to view simultaneously multiple individuals exercising alone in their own homes is quite a complex task and may not be able to be implemented in the first stage of this project

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

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

Project Title:	Application of Monte Carlo estimation techniques for estimation/inference problems in engineering

Name of Supervisor:	Guivant jose

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

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Mechanical and Manufacturing Engineering

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Air and ground vehicles

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

Abstract:	In many applications in engineering we need to control machines and complex systems. In order to perform control we need to know the internal state of the controlled system. As we are usually no able to measure all the states and because there is uncertainty in the measurements of the systems’ outputs we need to perform estimation. From the diversity of methods, some of the most powerful are based on Monte Carlo techniques, i.e. Particle Filters. Very non-linear systems polluted with the more extravagant noises are feasible to be treated with particle filters. We are not talking just about systems such as mechanical, electronic or robots, we are even talking about biological, chemical, financial and many other “non-engineering” areas. All of them involve solving of complex estimation problems. Skills: Student that feel confident working with math (e.g. for modeling systems), software programming (Matlab or C/C++) . Note: The student is allowed to flavor the project to be more theoretical or practical oriented in according to his/her interests.

Research Environment:	The student will work under supervision and collaboration of researchers that are expert in the area of sensing and data fusion.

Novelty and Contribution:	Application of Monte Carlo techniques for data fusion is a top area of research and applications.

Expected Outcomes:	The most important outcome is the valuable knowledge and skills the student will get after this taste of Research project. This knowledge can be applied on a diversity of areas of research and application.

Reference Material Links:	To be discussed with the supervisor. Those will be specific books and research papers.

Project Title:	Intelligent Coordination of Multiple Autonomous Vehicles

Name of Supervisor:	Dr Ngai Ming Kwok

Email of Supervisor:	nmkwok@unsw.edu.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Mechanical and Manufacturing Engineering

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Air and ground vehicles

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

Abstract:	In a variety of tasks performed by autonomous vehicles such as in transportation and automated agriculture, coordinated operations of multi-vehicles are foreshadowed to outperform the deployment of a single vehicle in terms of increased capacity and flexibility. This project aims at the development of a swarm intelligence based algorithm to derive drive commands, speed and turning, for the vehicles such that they are steered into and maintained in desirable formations according to an assigned task. The algorithm should feature implementation simplicity and relaxing the need for analytical system models. To this end, the coordination of vehicles is posed as an optimization problem minimizing the translational and angular errors between the current vehicle positions and their corresponding targets. Inter-vehicle collisions should be avoided, in this work, by employing a behavioural-based reactive scheme together with a dynamical rescheduling procedure. Simulations for coordinated multi-vehicle motions, in benchmark formation patterns, should be included to demonstrate the effectiveness of the proposed approach.

Research Environment:	The autonomous systems research group in the School of Mechanical and Manufacturing Engineering maintains several autonomous vehicle test platforms they are readily deployable in this research project. Academic group members are active researchers in the area of autonomous systems. Current and on-going projects cover the development of automated agriculture robots, unmanned air vehicles, sensing and perception fusion. A number of postgraduate research students are also working in the group providing an enriched environment for the student undertaking this project.

Novelty and Contribution:	The novelty of this project rests on tackling the vehicle coordination problem from a nature inspired perspective, namely, swarm intelligence. Deterministic or predefined control strategies might not be a suitable candidate due to the large system dimension in association with the number of vehicles employed in the coordinated task. Individual vehicles are embedded with self-contained intelligence and are expected to react autonomously to dynamic environments. Thus, the success of this project is anticipated to contribute to the area of distributed control and computation.

Expected Outcomes:	The student is expected to carry out studies in swarm intelligence, vehicle motion model, and coordination schemes. In addition, it is anticipated that the student undertaking the project could develop a coordination algorithm which is implementable on the test platforms.

Reference Material Links:	M. Parent, “Advanced urban transport: automation is on the way,” IEEE Intelligent Systems, Vol. 22, No. 2, pp. 9-11, 2007. F. Wang, M. Yang and R. Yang, “The intelligent vehicle coordination of the cybernetic transport system,” Intl. Journal of Advanced Robotic Systems, Vol. 6, No.1, pp. 53-58, 2009.

Project Title:	Interfacing of the Control Computer for the Wall Climbing Robot

Name of Supervisor:	A/Prof. Jay Katupitiya

Email of Supervisor:	J.Katupitiya@unsw.edu.au

Name of Joint/Co-Supervisor:	Dr. Ray Eaton

Email of Joint/Co-Supervisor:	R.Eaton@unsw.edu.au

School:	School of Mechanical and Manufacturing Engineering

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Air and ground vehicles

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

Abstract:	Within this academic year the building of MK-II wall climbing robot will be completed. This robot is a articulated small machine with 7 joints and two feet. It has on board vacuum generators to create a vacuum to enable it to hold on to a wall. The next phase is to interface the Gumstix(tm) based control computer to the joint control hardware of the wall climbing robot. The version of Gumstix used is based on a Verdex mother board and has a robostix daughter boards and Wi-Fi boards. Having interfaced the Gumstix based system, it must be programmed to actuate the joints based on commands received from a remote computer system.

Research Environment:	This is a hand on project where a hardware platform is available. The majority of the work relates to programming a small computer system that comes pre-loaded with Linux to implement parallel servo control of 7 on-board motors. In addition the control systems to be implemented must be designed and programmed.

Novelty and Contribution:	A literature search indicated that there are no bi-ped wall climbing machines of this sophistication is operational out there. The mechanical design is carried out provide sufficient payload capacity to carry all sensors and actuators on board without the need of an umbilical chord. The sophistication of the mechanical design is such that it has the full capability to transfer from one surface to another regardless of orientation difference.

Expected Outcomes:	The ToR scholar will gain a sound understanding of the implementation of control systems in autonomous machines. A thorough understanding of programming to achieve wireless communication based data transfer between an autonomous system and a command centre may also be achieved. It is expected that the student will bring the joints of the wall climbing robot under stable computer control.

Reference Material Links:	Search library's Compendex e-reqource under the key words "crawling robots", "crawlers" and "Wall climbing robots" for information about the state of the art.

Project Title:	Joystick based control of the VTAV aircraft

Name of Supervisor:	A/Prof. Jay Katupitiya

Email of Supervisor:	J.Katupitiya@unsw.edu.au

Name of Joint/Co-Supervisor:	Dr. Jose Guivant

Email of Joint/Co-Supervisor:	J.Guivant@unsw.edu.au

School:	School of Mechanical and Manufacturing Engineering

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Air and ground vehicles

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

Abstract:	The School of Mech. & Manf. Engineering has just completed the construction of a Vectored Thrust Aerial Vehicle (VTAV) powered by 3 ducted fans. One of these ducted fans is rigidly fixed to the frame while the other two are free to rotate about a horizontal axis. Given that it is a 3 rotor system, torque cancellation cannot be achieved. Hence the control problem is very complex, however, the configuration has far superior maneuverability. This project is about developing a control interface for the manual control of the aircraft. The aircraft has an on-board computer that can be commanded through Wi-Fi or serial modems. The ToR scholar is expected to program the USB based joystick system that is attached to a laptop computer which will act as the base station that will send commands to the VTAV. The operation of VTAV can be checked through remote operation while the VTAV is tethered.

Research Environment:	The system has a number of control inputs, namely; independent tilt control of two of the rotors, the rotors speeds of all three rotors. A careful study is needed to partition the control inputs to achieve roll, pitch, yaw and lift control. This will be followed by developing a control architecture for the interface's operation.

Novelty and Contribution:	Controlling a non-torque canceling aircraft poses a unique and challenging control problem. However, the tri-rotor geometry gives excellent directional properties for the aircraft.

Expected Outcomes:	Understanding the control of an aircraft. Exposure to the complexities of controlling an air vehicle in which the the rotor torques do not cancel each other.

Reference Material Links:	A description of the system by way of an undergraduate thesis and the results of dynamic modeling and its control in a simulation will be available towards the end of the year 2009.

Project Title:	Research of techniques for video feature extraction for applications in autonomous systes

Name of Supervisor:	Guivant jose

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

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Mechanical and Manufacturing Engineering

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Air and ground vehicles

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

Abstract:	Vision is one of the main sources information (i.e. sensor) for animals to “operate” in the context of life. However, a video camera produce just images, i.e. raw data. There is a process (or a combination of processes) that performs “perception”. The interpretation of the images in order to infer and perform control and decision making is called perception. In many cases the first stage of the perception process involves feature extraction, i.e. detection and segmentation of parts of the image that contain information that can be useful for the higher level processes that perform perception. The objective of this Taste of Research project is the research and understanding of some known and feasible state of the art methods for feature extraction. The project involves the theoretical part and the implementation of some of the approaches

Research Environment:	The student will work under supervision and collaboration of researchers that are expert in the area of sensing and data fusion. Equipment for real-time and off-line experiments is available for real experiments.

Novelty and Contribution:	Although vision is an area of intense research in robotic and other research communities, there is a diversity of challenges, still not solved, in the implementation of robust and efficient vision algorithms for machine vision applied to autonomous systems.

Expected Outcomes:	Initially the student is expected to get informed about some standard methods for feature extraction from video images. As a final outcome we expect the student will implement one of the standard approaches and possibly improve it. A remarkable outcome will be the student having learnt some powerful techniques in the area of data fusion.

Reference Material Links:	To be discussed with the supervisor. Those will be specific books and research papers.

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:	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:	Optimal control of an oscillating hydrofoil based tidal current renewable energy power converter

Name of Supervisor:	Guivant Jose

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

Name of Joint/Co-Supervisor:	Gerold Kloos (external)

Email of Joint/Co-Supervisor:	gkloos@biopowersystems.com

School:	School of Mechanical and Manufacturing Engineering

Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable

School Research Area:	Design and Analysis

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

Abstract:	The aim of this project is to develop a control algorithm to maximize power conversion of a tidal current energy power converter, which is based on an oscillating hydrofoil. Oscillating hydrofoils typically employ a wing like profile. As the tidal stream flows over the profile it is angled at an angle of attack. This in turn creates a lift force, which is typically used to drive an arm to which some sort of power take-off is attached. The power take-off converts the mechanical motion of the arm into electricity. Several factors need to be taken into account when trying to maximize net power delivery of a hydrofoil based renewable energy converter. The main ones are actuation cost of the hydrofoil itself needs to be considered, as well as the fact that the device environment shows stochastic characteristics as the tidal stream exhibits local variations in current speed, which the device will encounter. Furthermore it is desirable to impose as little stresses as possible on the device to prolong the lifetime and service intervals of the device.

Research Environment:	This project is undertaken in conjunction with an industry partner. The majority of the work will be carried out in the laboratory of the School of Mechanical and Manufacturing Engineering. A Matlab/Simulink simulator of the hydrofoil-based device will be provided by the industry partner and can be used as a starting point. In order to enhance the project outcomes and receive real-world confirmation, the successful student will have the opportunity to receive regular feedback from the industry partner.

Novelty and Contribution:	This is a very new area of research.

Expected Outcomes:	The Student is free to explore any type of control algorithm, compare them and come up with a suggested best algorithm to maximize power conversion under the given constraints. As such, the control algorithms may be based on deterministic formulations, stochastic algorithms, neural networks or any other suitable form of algorithm. It is expected that the student investigates the factors affecting power conversion and power maximization, develops at least two different kinds of control algorithms, assesses the algorithms and compares the performance.

Reference Material Links:	Reference material regarding power maximization for oscillating hydrofoil is not available, as this is a very new area of research. Nevertheless, interested students should have read and understood the basic operational principle of oscillating hydrofoils. Material for this can be found on the Internet. Furthermore, it essential that the student has good knowledge in more than one area of control algorithms (e.g. stochastic algorithms, neural networks, …).

Project Title:	Covert Channels - building infrastructure to steal secrets.

Name of Supervisor:	Kevin Elphinstone

Email of Supervisor:	kevine@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:	NICTA Project

Faculty Research Area (Theme):	Embedded Systems and Communications

School Research Area:	Embedded, Real Time & Operating Systems

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

Abstract:	Many operating systems are explicitly designed to keep secrets exactly that - secret. They actively seek to prevent data from leaking from one data clasification (e.g. top secret) to another lower clasification - even if the user actively attempts to violate system security. Your mission, should you choose to accept it, is to develop software to bypass system security to leak secrets using covert channels. The project would be fairly open for the student to survey communications techniques includling signal processing and error correction to develop tools capable of sending secret messages to a collaborator.

Research Environment:	The project will be conducted within the ERTOS group at NICTA at Kensington (L5 building). ERTOS is one of the world's leading operating-systems research groups. It is a stimulating environment that combines cutting-edge research with building systems that are deployed in real products. ERTOS is a team environment that enables and values contributions from all its members.

Novelty and Contribution:

Expected Outcomes:

Reference Material Links:

Project Title:	Locking tradeoffs for multiprocessor kernels

Name of Supervisor:	Gernot Heiser

Email of Supervisor:	gernot@unsw.edu.au

Name of Joint/Co-Supervisor:	Kevin Elphinstone

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

School:	School of Computer Science and Engineering

For CSE and EET Projects:	NICTA Project

Faculty Research Area (Theme):	Embedded Systems and Communications

School Research Area:	Embedded, Real Time & Operating Systems

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

Abstract:	Multi-threaded (SMT) processors are characterised by shared caches and very low (single-cycle) communication latencies between execution contexts. Consequently, treads on such a system are scheduled from a single scheduling queue, and other kernel data structures are also shared. This makes fine-grained locking hard and potentially expensive. For a kernel where average latencies of kernel operations are very short, a global kernel lock (i.e., single-threaded kernel) could be an appropriate approach. This project is to investigate global vs fine-grained kernel locking on the OKL4 microkernel running on a highly (>4) multi-threaded processor core. This is likely to lead to publishable results.

Research Environment:	Operating systems (ERTOS) group at NICTA NRL

Novelty and Contribution:	Re-assessment of an old issue with new constraints: microkernel with very short in-kernel execution paths and modern multi-threaded hardware

Expected Outcomes:	Research report analysing tradeoffs and quantifying their performance implications

Reference Material Links:	http:ertos/nicta.com/au

Project Title:	Lottery Scheduling for Embedded Systems

Name of Supervisor:	Kevin Elphinstone

Email of Supervisor:	kevine@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:	NICTA Project

Faculty Research Area (Theme):	Embedded Systems and Communications

School Research Area:	Embedded, Real Time & Operating Systems

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

Abstract:	A flexible CPU scheduling mechanism should be able to accommodate many application domains with only configuration of the scheduler being required. One such scheduler that might fulfill the role is a lottery scheduler. A lottery scheduler randomly draws a winner to determine which task runs next. The distribution of tickets to tasks is used to affect the scheduling behaviour. This project aims to develop an efficient lottery scheduler and an API for managing the ticket distribution.

Research Environment:	The project will be conducted within the ERTOS group at NICTA at Kensington (L5 building). ERTOS is one of the world's leading operating-systems research groups. It is a stimulating environment that combines cutting-edge research with building systems that are deployed in real products. ERTOS is a team environment that enables and values contributions from all its members.

Novelty and Contribution:

Expected Outcomes:

Reference Material Links:

Project Title:	Networks for Reconfigurable Systems on a Chip

Name of Supervisor:	Oliver Diessel

Email of Supervisor:	odiessel@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):	Embedded Systems and Communications

School Research Area:	Embedded, Real Time & Operating Systems

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

Abstract:	Networks on chip for reconfigurable systems on chip need to be tuned according to prevailing application requirements. This need creates a demand for new approaches and methods for dynamically optimizing on-chip communication structures. In this ToR project you will build on your knowledge of digital systems, FPGA and network technology to assist a small, diverse team of researchers develop a simple demonstration of on-the-fly network-on-chip adaptation.

Research Environment:	You will work with a team comprising two masters students and the supervisor using a variety of FPGA boards and design workstations available in the School. The International Conference on Field-Programmable Technology will be hosted by the University during the project period. You will be encouraged to assist and participate in the proceedings.

Novelty and Contribution:	The project provides the opportunity to quickly become exposed to and participate in leading edge reconfigurable systems research. The project serves the valuable purposes of tying together several threads of research currently under exploration, and providing a proof of concept necessary to obtain wider support for the work.

Expected Outcomes:	A demonstration of the efficient reconfiguration of an on-chip communication network necessary to maintain quality of service when application requirements change.

Reference Material Links:	NoCs for reconfigurable SoCs: * Marescaux et al., Run-time support for heterogeneous multitasking on reconfigurable SoCs * Bjerregaard et al., A survey of research and practices of network-on-chip * Warkentin, NoC support for dynamic FPGA pages Dynamic reconfiguration of FPGAs: * Ullmann et al., An FPGA run-time system for dynamical on-demand reconfiguration * Lai, A new API for internal reconfiguration of Xilinx FPGAs

Project Title:	Optimising memory management in a high-performance secure microkernel

Name of Supervisor:	Kevin Elphinstone

Email of Supervisor:	kevine@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:	NICTA Project

Faculty Research Area (Theme):	Embedded Systems and Communications

School Research Area:	Embedded, Real Time & Operating Systems

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

Abstract:	The seL4 microkernel is designed to give unprecedented reliability and security via a formal proof of correctness. However, up until now, seL4's design has focused on correctness, amenability to proof, and security. Limited attention has been given to optimising memory management. This project involves identifying, proposing, and exploring solutions to memory management issues in a modern microkernel. It will give the opportunity for a clever student to have real impact on the evolution of leading-edge operating system technology.

Research Environment:	The project will be conducted within the ERTOS group at NICTA at Kensington (L5 building). ERTOS is one of the world's leading operating-systems research groups. It is a stimulating environment that combines cutting-edge research with building systems that are deployed in real products. ERTOS is a team environment that enables and values contributions from all its members.

Novelty and Contribution:

Expected Outcomes:

Reference Material Links:

Project Title:	Performance limits of IPC fastpath implemented in C

Name of Supervisor:	Gernot Heiser

Email of Supervisor:	gernot@unsw.edu.au

Name of Joint/Co-Supervisor:	Kevin Elphinstone

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

School:	School of Computer Science and Engineering

For CSE and EET Projects:	NICTA Project

Faculty Research Area (Theme):	Embedded Systems and Communications

School Research Area:	Embedded, Real Time & Operating Systems

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

Abstract:	High-performance L4 microkernels use traditionally an assembler “fastpath” to overcome the performance problems resulting from C code in the critical IPC system call path. Relying on assembler code has a high engineering and maintenance cost, and makes formal verification harder. Eliminating the need for assembler code, or, at least, reducing the amount needed, would have significant practical benefits. This thesis is to analyse the IPC path in order to understand why C compilers do not do a better job on it. It will analyse possible (semantically-invariant) modifications of the C code, in order to investigate how far C performance can be pushed, and whether it can be made competitive with the assembler implementation. This study is to be done on at least the ARM architecture (x86 optional) and using several compilers (gcc, RVCT, maybe Green Hills).

Research Environment:	Operating systems (ERTOS) group at NICTA NRL

Novelty and Contribution:	Thorough investigation of performance limits of C code for low-level high-erformance OS kernel code

Expected Outcomes:	A thorough analysis of the code and its optimisability. Potentially publishable.

Reference Material Links:	http:ertos/nicta.com/au

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:	An improved face detector

Name of Supervisor:	Getian Ye

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

Name of Joint/Co-Supervisor:	Yang Wang, Bang Zhang

Email of Joint/Co-Supervisor:	Yang.Wang@nicta.com.au, Bang.Zhang@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:	Electrical Engineering & Telecommunications

Abstract:	Detecting faces in an image is a fundamental task for many promising applications, e.g., visual surveillance, content-based image retrieval, and robotics. Viola and Jones’ work is regarded as the latest milestone in the research of face detection. They developed an accurate frontal face detection system that employs Haar features and AdaBoost learning process. Following their work, many improved solutions have been proposed. The aim of this project is to develop and implement an improved face detector that is recently proposed by researchers at NICTA. The successful candidate will improve skill in problem solving and gain basic knowledge of image processing and the state-of-the-art face detection methods.

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 face detection using discriminative visual features. 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:	http://en.wikipedia.org/wiki/Face_detection

Project Title:	Computer simulation of road traffic geometry

Name of Supervisor:	Yang Wang

Email of Supervisor:	yang.wang@nicta.com.au

Name of Joint/Co-Supervisor:	Getian Ye

Email of Joint/Co-Supervisor:	getian.ye@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:	Electrical Engineering & Telecommunications

Abstract:	In camera based visual surveillance/traffic monitoring, objects within a 3-D scene together with their spatial relationships are projected onto a sequence of 2-D camera images. This work is to simulate the geometry relationship between the 3-D real scene and the 2-D camera image for road traffic monitoring. Clearly, both the structure of the 3-D scene and the projection from 3-D to 2-D (or image formation) will influence the result of geometry analysis. The geometry relationship can be studied with simplifications through computer simulation.

Research Environment:	The successful candidate will work with researchers and postgraduate students at NICTA’s Kensington Laboratory.

Novelty and Contribution:	The project will deal with the geometry analysis for the cases such as curved road and camera shake. The research outcome will contribute to NICTA’s Smart Transport and Roads (STaR) project.

Expected Outcomes:	Software implementation and demo.

Reference Material Links:	http://www.nicta.com.au/research/projects/smart_transport_and_roads/star_projects

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:	Object detection using stereo camera

Name of Supervisor:	Yang Wang

Email of Supervisor:	yang.wang@nicta.com.au

Name of Joint/Co-Supervisor:	Getian Ye

Email of Joint/Co-Supervisor:	getian.ye@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:	Electrical Engineering & Telecommunications

Abstract:	Object detection is an important image understanding task with many potential applications. For example, in camera based visual surveillance/traffic monitoring systems, faces/vehicles are usually required to be robustly detected under various circumstances. Stereo camera can simultaneously capture a pair of images of the same scene and provide information for distinguishing the objects at different ranges in the scene. This project is to develop and implement an object detection algorithm using stereo camera.

Research Environment:	The successful candidate will work with researchers and postgraduate students at NICTA’s Kensington Laboratory.

Novelty and Contribution:	Stereo camera helps to improve the robustness of object detection under varying illumination and weather conditions. The research outcome will contribute to NICTA’s Smart Transport and Roads (STaR) project.

Expected Outcomes:	Software implementation and demo.

Reference Material Links:	http://www.videredesign.com/vision/svs_images.htm

Project Title:	Pedestrian Detection Using a Cascade of Boosted Classifiers

Name of Supervisor:	Jian Zhang

Email of Supervisor:	jzhang@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:	NICTA Project

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Image Processing

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

Abstract:	This project aims provide an implementation, verification and evaluation of a pedestrian detection framework proposed by Viola-Jones which is fast and robust under varying conditions by incorporating both motion and appearance in training a cascade classifiers. It is hoped that this project will serve as a foundation for other applications such as vehicle and cyclist detection; and incorporated with other image processing techniques such as tracking which would improve robustness and computational efficiency. The nature of this work will focus on software implementation based on state-of-art international conference papers.

Research Environment:	The novelty lies on the new approach for image categorization based on feature extraction. You will access the current state-of-art image/video processing in-house-made tools. Your work will contribute to NICTA’s project – Video analysis and content management for surveillance (VACMS).

Novelty and Contribution:	The project will be carried out in multimedia & video communication research group in National ICT Australia (NICTA) laboratories at Kensington. You will work directly with many talent researchers in a high standard image/video group and will be given clear guidance for work that could link to your future career development

Expected Outcomes:	Algorithm software implementation and some test results.

Reference Material Links:	Paul Viola, Michael J. Jones, Daniel Snow. "Detecting Pedestrians using Patterns of Motion and Appearance", ICCV 2003. November 27 http://www.merl.com/papers/docs/TR2003-90.pdf

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:	A Real-time and Robust Object Tracking System for the Video Surveillance.

Name of Supervisor:	Jian Zhang

Email of Supervisor:	jian.zhang@nicta.com.au

Name of Joint/Co-Supervisor:	Sijun Lu

Email of Joint/Co-Supervisor:	sijun.lu@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:	Multimedia & Visual Communication

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

Abstract:	The advance of fast and affordable computing device has provided the power to track moving objects from a video camera in real-time. Many algorithms have been developed for object tracking in the past, using various image/video features, such edge, color, spatial-temporal information, or using Kalman filtering and hypothesis testing techniques. The nature of this work is to implement the existing object tracking techniques and choose one suitable for the video surveillance domain and the real-time purpose. Research challenge of this task is to achieve robust tracking under the disturbing environment such as occlusion and lighting changes. The knowledge you learned from the computer and engineering courses, such as signal processing, graphic theory and computer vision will gain plenty practice from this project. Your programming skills will also be developed as you will develop a demo in software. This project is an absolute opportunity for a ToR student to test his/her research capabilities for the PhD study.

Research Environment:	The project will be carried out in multimedia & video communication research group in National ICT Australia (NICTA) laboratories at Kensintong. You will work directly with many talent researchers in a high standard image/video group and will be given clear guidance for work that could link to your future career development.

Novelty and Contribution:	The novelty lies on the new approach to tracking objects without implementing background subtraction. You will access the current state-of-art image/video processing in-house-made tools. Your work will contribute to NICTA’s Strategic Project – Smart Transport and Road (STaR).

Expected Outcomes:	Algorithm software implementation and some test results.

Reference Material Links:	[1] T. Kanade, R. Collins, A. Lipton, P. Burt, and L. Wixson, "Advances in Cooperative Multi-Sensor Video Surveillance",Darpa Image Understanding Workshop, Morgan Kaufmann, November, 1998, pp. 3-24 [2]R. Collins, A. Lipton, H. Fujiyoshi, and T. Kanade, "Algorithms for cooperative multisensor surveillance", Proceedings of the IEEE, Vol. 89, No. 10, October, 2001, pp. 1456 - 1477. [3] Tao Xiong and Christian Debrunner, “Stochastic Car Tracking With Line- and Color-Based Features”, IEEE Transactions on Intelligent Transportation Systems, VOL. 5, NO. 4, December 2004 [4] Yue Zhou and Hai Tao, “A Background Layer Model for Object Tracking through Occlusion”, ICCV 2003

Project Title:	Appearance-based re-identification of objects in video

Name of Supervisor:	Jian Zhang

Email of Supervisor:	jian.zhang@nicta.com.au

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Computer Science and Engineering

For CSE and EET Projects:	NICTA Project

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

School Research Area:	Multimedia & Visual Communication

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

Abstract:	This research is of prime importance in many video content management applications including video surveillance, image/video retrieval and event detection in video. The premise of this task is that given an object of the video how can we re-identify the same object based on the appearance. This is a very challenge research in the video content analysis. Student will develop their academic research skills in the area of image processing/computer vision, pattern recognition and machine learning. The theme of the research project is called “use inspired research”. The PhD project will be potentially connected to the industrial research groups for future collaborations.

Research Environment:	The project will be carried out in multimedia & video communication research group in National ICT Australia (NICTA) laboratories at Kensington. You will work directly with many talent researchers in a high standard image/video group and will be given clear guidance for work that could link to your future career development.

Novelty and Contribution:	The novelty lies on the new approach for key feature detection, objects classification and multi-camera data fusion. You will access the current state-of-art image/video processing in-house-made tools. Your work will contribute to NICTA’s project in video analysis and event detection.

Expected Outcomes:	Algorithm software implementation and some test results.

Reference Material Links:	[1] "Background-Subtraction in Thermal Imagery Using Contour Saliency" J. Davis and V. Sharma International Journal of Computer Vision, Vol. 71, No. 2, 2007, pp. 161-181. (online version June 2006) [2] "A Two-Stage Template Approach to Person Detection in Thermal Imagery" J. Davis and M. Keck Workshop on Applications of Computer Vision, Breckenridge, Colorado, January 5-7, 2005. [3] Sakrapee (Paul) Paisitkriangkra, Chunhua Shen and Jian Zhang, “Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features”, IEEE Trans. on Circuits and Systems for Video Technology, 18(8), pages 1140-1151, August 2008

Project Title:	Objects Classification and Event Detection in Surveillance Video

Name of Supervisor:	Jian Zhang

Email of Supervisor:	jian.zhang@nicta.com.au

Name of Joint/Co-Supervisor:	Sijun Lu

Email of Joint/Co-Supervisor:	sijun.lu@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:	Multimedia & Visual Communication

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

Abstract:	Object classification is a further step to object detection and tracking in understanding of video. Without object classification, we can only know something is moving in the video sequences, but don’t know what it is. With the information from object tracking and classification, it may become possible to understand the activities and events happening in the video sequences. The nature of the work is software implementation to investigate the state-of-art object classification technologies for categorizing moving vehicles or human detected from the surveillance videos. The knowledge you learned from the courses of computer vision, neural network, pattern recognition and machine learning can all find their utilization in this project. Your programming skills will also be developed as you will develop a demo in software. This project is an absolute opportunity for a ToR student to test his/her research capabilities for the PhD study.

Research Environment:	The project will be carried out in multimedia & video communication research group in National ICT Australia (NICTA) laboratories at Kensington. You will work directly with many talent researchers in a high standard image/video group and will be given clear guidance for work that could link to your future career development.

Novelty and Contribution:	The novelty lies on the new approach for objects classification and statistical feature clustering. You will access the current state-of-art image/video processing in-house-made tools. Your work will contribute to NICTA’s Strategic Project – Smart Transport and Road (STaR).

Expected Outcomes:	Algorithm software implementation and some test results.

Reference Material Links:	[1] T. Kanade, R. Collins, A. Lipton, P. Burt, and L. Wixson, "Advances in Cooperative Multi-Sensor Video Surveillance",Darpa Image Understanding Workshop, Morgan Kaufmann, November, 1998, pp. 3-24 [2]R. Collins, A. Lipton, H. Fujiyoshi, and T. Kanade, "Algorithms for cooperative multisensor surveillance", Proceedings of the IEEE, Vol. 89, No. 10, October, 2001, pp. 1456 - 1477. [3] Wei Niu, Jiao Long, Dan Han and Yuan-Fang Wang, “Human Activity Detection and Recognition for Video Surveillance”, ICME 2004 [4] Surendra Gupte, Osama Masoud, Robert F. K. Martin, and Nikolaos P. Papanikolopoulos, “Detection and Classification of Vehicles”, IEEE Transactions on Intelligent Transportation Systems, VOL. 3, NO. 1, December 2002

Project Title:	“Cell ID” WiFi Algorithms for Location

Name of Supervisor:	Andrew Dempster

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

Name of Joint/Co-Supervisor:	Binghao Li

Email of Joint/Co-Supervisor:	b.li@unsw.edu.au

School:	School of Surveying and Spatial Information Systems

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

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

Abstract:	A number of different, highly sophisticated algorithms have been used to try and position clients in 802.11 wireless LAN (WiFi) networks. These methods include statistical techniques and “fingerprinting”. There seems to be no perfect algorithm and the sophisticated algorithms seem not to deliver the accuracy their sophistication promises. This project aims to employ much simpler algorithms, starting with the simple “cell ID” algorithm used in mobile phone positioning, and building up more accurate algorithms using the same idea. The work will be both practical and theoretical. Initially, a literature survey will identify any algorithms existing of this type, and the relevant algorithms to compare results with. An experiment will be designed to gather the data required to test the cell ID algorithms. Different layers of complexity can be added to the algorithms and the advantages analysed.

Research Environment:	The School has many different devices and software for measuring WiFi signal strength. These are looked after by researcher Dr Binghao Li, who will guide the student on a day-to-day basis. The WiFi positioning team is quite small, led by A/Prof Dempster, and including Dr Li and a PhD student, all of whom are available to help the scholar.

Novelty and Contribution:	The team is established as one of the world’s best in the area of fingerprinting. The new algorithms will be compared to these results to see what they have to offer.

Expected Outcomes:	The expected outcome is a series of algorithms that are both simple and accurate in locating a WiFi device.

Reference Material Links:	B Li papers downloadable from http://www.gmat.unsw.edu.au/snap/about/publications_author.htm

Project Title:	Antenna Collection of Solar Energy

Name of Supervisor:	Richard Corkish

Email of Supervisor:	r.corkish@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):	Energy Systems, Renewable and Non-Renewable

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

Abstract:	Applicable to PHYSICS students. This theoretical project is concerned with the possibility to convert sunlight to electricity using devices based on the wave, rather than the particle, nature of light. Technological advance is increasing the feasibility of constructing such devices in the appropriate scale but significant theoretical questions remain. Challenges include the incoherence, the non-polarisation and the wide spectrum of sunlight. Students considering this project should have a strong interest and background in physics and mathematics, and especially in the wave and particle models of light. An interest in antenna theory would be advantageous. A similar Taste of Research project was done in 2005/06 which resulted in the discovery and correction of an error in published work, the acceptance of a paper at a major international conference and the establishment of a framework for progress in the field. This project is expected to satisfy at least five of the objectives of Industrial Training, which is normally sufficient.

Research Environment:	Leading silicon photovoltaics research group globally.

Novelty and Contribution:	Opportunity to resolve issues that remain controversial and poorly understood in the literature.

Expected Outcomes:	Clarification of controversial solar energy collection theory. Publication in leading applied physics journal.

Reference Material Links:	R. Corkish, M. A. Green, T. Puzzer, and T. Humphrey, "Efficiency of antenna solar collection," Proceedings of 3rd World Conference on Photovoltaic Solar Energy Conversion, 2003, Osaka. (http://unsworks.unsw.edu.au/vital/access/manager/Repository?query=corkish) I. M. Sokolov, "On the energetics of a nonlinear system rectifying thermal fluctuations," Europhysics Letters, 44, 278 (1998). Y. Wang, K. Kempa, B. Kimball, J. B. Carlson, G. Benham, W. Z. Li, T. Kempa, J. Rybczynski, A. Herczynski, and Z. F. Ren, "Receiving and transmitting light-like radio waves: Antenna effect in arrays of aligned carbon nanotubes," Applied Physics Letters, vol. 85, pp. 2607-2609, 2004. P. Muhlschlegel, H.-J. Eisler, O. J. F. Martin, B. Hecht, and D. W. Pohl, "Resonant Optical Antennas," Science, vol. 308, pp. 1607-1609, 2005.

Project Title:	Development of an Unmanned Aerial Vehicle Platform

Name of Supervisor:	Yong Li

Email of Supervisor:	yong.li@unsw.edu.au

Name of Joint/Co-Supervisor:	Shahzad Ahmad Malik

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

School:	School of Surveying and Spatial Information Systems

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

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

Abstract:	The Global Positioning System (GPS) can get you from your home to your friend’s place for that party you would never want to miss. But can GPS guide robotics or unmanned aerial vehicles (UAV)… to deliver Pizzas? This project aims to ultimately build such a system, which will be a guidance and control platform for a UAV helicopter using GPS and micro-electro-mechanical inertial sensors. This platform would stabilise the helicopter’s pitch and roll and yaw during flight and bring it back safely without any control from the ground. Such a prototype system could also be used in a range of applications such as aerial photography, terrain mapping and imaging. A compact UAV development platform will be used in the project comprising a dsPIC30F4011 microcontroller with a clock speed up to 120MHz, an MMA7260 triple-axis accelerometer, 3 LISY300AL single-axe gyros and a connection for the 20-Channel EM-406A SiRF III GPS Receiver with a built-in patch antenna, as well as pins for servos and radio control. It comes with self-testing firmware that serves as a starting point for developing custom control and navigation firmware.

Research Environment:	This is an opportunity to work with researchers in the Satellite Navigation and Positioning (SNAP) Lab at SSIS. There are a range of GPS receivers, INS and software tools in the SNAP Lab to help in getting you started in this project. The multisensor integration team has successfully built a FPGA-based GPS/INS system for land vehicle navigation and aerial mapping applications. We also have developed software packages to support multisensor integration.

Novelty and Contribution:	Inertial Navigation Systems (INS) have been widely used in aircraft guidance and control systems. The expensive cost and heavy weight of an INS is the major barrier to limit its use for civil applications. Micro-electro-mechanical systems (MEMS) are increasingly transforming the technology landscape by providing a range of sensors that are very small, consume a fraction of the power and cost orders of magnitude less than their predecessors. These advances have made it easier to integrate INS with GPS to develop UAV controllers for fixed wing (plane, biplane) or rotary wing (helicopter) aircrafts.

Expected Outcomes:	The outcome of the project is a guidance and control platform for a UAV helicopter using GPS and MEMS inertial sensors which can stabilise the UAV during flight and land it safely. The work includes the following tasks; 1. Embedded development with Microchip’s 16-bit dsPIC “digital signal controllers”. 2. Interface embedded GPS modules and MEMS sensors with a microcontroller. 3. Development of the custom control and navigation firmware. 4. Test the platform with a scale-model RC helicopter.

Reference Material Links:

Project Title:	Experimental studies on autonomous control systems for plantwide processes

Name of Supervisor:	A/Prof Jie Bao

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

Name of Joint/Co-Supervisor:

Email of Joint/Co-Supervisor:

School:	School of Chemical Sciences and Engineering

Faculty Research Area (Theme):	Intelligent & Autonomous Systems

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

Abstract:	Modern process plants are becoming increasingly complex, e.g., bio-chemical processes, reaction networks and renewable energy networks. Many of these plants have more than a hundred nonlinear process units and thousands of control loops. The wide use of material recycles and heat integration (with recycle and bypass streams) profoundly alters plantwide process dynamics and further increases their complexity to an extent that cannot be effectively managed by existing process modelling and control techniques: the traditional decentralized control approach cannot guarantee the plantwide stability/performance, while the hierarchical multivariable control approach is becoming infeasible due to the unprecedented plant complexity. A new plantwide control approach is being developed by the Computer Process Control Group. The idea is to model the plantwide process as a network of process units connected via physical mass and energy flow and control the network of process unit via a network of autonomous controllers which communicate with each other. In this project, you will conduct experimental studies on this new control approach.

Research Environment:	In this project, you will be supervised by A/Prof. Jie Bao and Mr. Shichao Xu from the Computer Process Control Group, Chemicals Sciences and Engineering and conduct experimental research in the Process Control Lab.

Novelty and Contribution:	This taste of research project is part of an on-going project that aims to develop a new paradigm in plantwide process control: (1) This approach represents a new philosophy to deal with the complexity of process systems – breaking down the complex processes into interconnected simple sub-systems and shift the complexity from the plantwide process dynamics (as a single system) to the network topology; (2) The physics of process units and their interactions are explicitly incorporated in both the modelling and control approaches to enable feasible implementation of such control philosophy based on the concept of dissipative systems.

Expected Outcomes:	In this project, you will implement the control algorithms developed by the Group (in individual autonomous controllers) to control a laboratory scale highly interactive multi-unit process (using National Instruments Compact-Rio controllers and Armfield process units). The expected outcomes include the analysis of the effectiveness of the proposed networked control approach and its possible weakness. The effects of delay or loss of communications between controllers will be studied and compared with theoretical results.

Reference Material Links:	[1] Rojas O.J.; Bao J. and Lee P.L. (2008) On Dissipativity Passivity and Dynamic Operability of Nonlinear Processes. J. Process Control 18 (5): 515–526 [2] Xu S.C. and Bao J. (2008) Interaction Analysis for Decentralized Control Based on Dissipativity. Asia-Pac. J. Chem. Eng. 3(6): 656-666. [3] Rojas O.J.; Setiawan R.; Bao J. and Lee P.L. (2009) Dynamic operability analysis of nonlinear process networks based on dissipativity. AIChE J. 55(4): 963-982 [4] Xu S.C. and Bao J. (2009) Distributed Control of Plantwide Chemical Processes. J. Process Control (in press)

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:	Open-Source Assisted GPS Client

Name of Supervisor:	Andrew Dempster

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

Name of Joint/Co-Supervisor:	Binghao Li

Email of Joint/Co-Supervisor:	b.li@unsw.edu.au

School:	School of Surveying and Spatial Information Systems

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

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

Abstract:	Mobile phones need assistance to work out their location using GPS when indoors. The school of Surveying and Spatial Information Systems (SSIS) has developed an open-source GPS/GNSS server (OSGRS) that can provide this assistance. This project is to create the software client to enable a standard mobile platform (iPhone, gPhone, Nokia) to accept and use this assistance. The client software will also be open-source. The work will be both practical and theoretical. An initial study will define the requirements for the client, followed by implementation.

Research Environment:	The School has several different devices (mobile phones and GPS receivers) that support assisted-GPS (AGPS). These are looked after by researcher Dr Binghao Li, who will guide the student on a day-to-day basis. The indoor positioning team is led by A/Prof Dempster, and including Dr Li, Mr. Peter Mumford and PhD students, all of whom are available to help the scholar.

Novelty and Contribution:	The team has been working on AGPS for several years and have done experiments with some of the industry leaders such as Andrew Corporation (based in Wollongong). At the moment, the SSIS OSGRS is the only open source AGPS server in operation and the client software will also be the first of its kind.

Expected Outcomes:	The expected outcome is a working system that allows GPS to operate indoors using a system that is available for free to anyone willing to operate the server and relevant clients.

Reference Material Links:	B Li papers downloadable from http://www.gmat.unsw.edu.au/snap/about/publications_author.htm

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:	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

Project Title:	Business-Driven Management of Web Services

Name of Supervisor:	Vladimir Tosic

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

Name of Joint/Co-Supervisor:	Jacky Wai Keung

Email of Joint/Co-Supervisor:	Jacky.Keung@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:	Web Services, E-Commerce, and other Web Technologies

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

Abstract:	Web services are distributed computing application components that use XML-based (Extensible Markup Language) technologies to implement the service-oriented architecture (SOA). They enable collaboration of heterogeneous computing systems distributed over the Internet, across corporate boundaries. They are already embedded in various products and services of all major computing companies. Management of information technology (IT) systems (including service-oriented systems) is the process of their monitoring and control to ensure correct operation, discover and fix problems (e.g., faults, performance problems), and achieve maximal benefits from the IT systems. Ideally, IT systems should be self-managing, or at least be managed with minimal human intervention. This is one of the goals of autonomic computing (effort lead by IBM). The current management solutions for service-oriented systems and business processes focus on optimizing technical quality of service (QoS, e.g., response time and availability), but provide inadequate treatment for optimizing business metrics, such as profit and customer retention. Business-driven IT management is an active area of research (lead by HP and IBM). Students working on this project will design, implement in Java, and test extensions of existing autonomic Web service management software with additional original support (new algorithms and data structures) for maximization of business metrics.

Research Environment:	The students will work closely with researchers at NICTA (http://www.nicta.com.au) in a friendly mixed-gender and multicultural team environment comprised of senior researchers and postgraduate students. For example, two female Ph.D. students with strong academic background and international industrial experience will be able to provide expertise and additional technical guidance, when needed.

Novelty and Contribution:	We have previously developed several Web service management tools with support for autonomic computing and/or business-driven management. This project will extend some of the existing software into a new autonomic business-driven management system for Web services. Before implementation can be done, some research questions should be answered. The developed system will have significant advantages over its competition, because in some aspects (maximisation of business values and enactment of business strategies) it will provide features that no current IT system management system has. Some of the potential application areas are finance and e-government. The developed solutions might lead to a patent.

Expected Outcomes:	- Software architecture that extends an existing Web service management system with additional modules relevant for maximisation of business value metrics relevant for company’s business strategy. - Detailed design of the extension modules. - Original algorithms that enable Web service management software to make management decisions that maximise business value metrics in complex situations, with minimal human involvement. - Implementation of these extensions in Java. - Experiments (collection of experimental data and their analysis) evaluating feasibility, usefulness, and performance characteristics of the implemented extensions.

Reference Material Links:	- http://www.w3schools.com/webservices/ws_intro.asp - http://www.cse.unsw.edu.au/~cs9322 - http://www.service-architecture.com/articles/index.html - http://www.research.ibm.com/autonomic/overview/ - http://www.nicta.com.au/people/tosicv/publications - Tosic, V. "On Modeling and Maximizing Business Value for Autonomic Service-Oriented Systems", keynote with invited paper in Proc. of Business Process Management (BPM) 2008 Workshops, Milan, Italy, September 1, 2008, Springer, Lecture Notes in Business Information Processing (LNBIP), No. 17, pp. 407-418. Preprint at: http://nicta.com.au/__data/assets/pdf_file/0007/17962/Tosic-invited-QSWS-08-FinalForLNBIP.pdf - For further information, email Dr. Vladimir Tosic (‘vtosic’ at the CSE e-mail system) with Subject line “UNSW Summer Scholars”.

Electrical Engineering & Telecommunications Research Areas

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