Student Projects:

Advanced Materials and Manufacturing - 5 projects

Show Projects...

What makes for a cradle-to-cradle building?

Project Code:AMM_01

Supervisor:

Dr Haley Jones

Outline:

The cradle-to-cradle paradigm considers the whole-of-life design of products and processes with the four basic tenets:

1. "less bad" is no good
2. eco-effectiveness (rather than efficiency)
3. waste equals food
4. respect diversity

Goals of this project

1.Learn to do molecular dynamics on the IAS model using the existing software.
2.Compute thermal conductivity or other physical properties of the IAS model of a Zr-based metallic glass.

Contact:

E: zbigniew.stachurski@anu.edu.au
W: http://engnet.anu.edu.au/DEpeople/Zbigniew.Stachurski

Magnetic and dielectric properties of metallic glassy alloys

Project Code:AMM_05

Supervisor:

Dr Zbigniew Stachurski

Outline:

Goals of this project

1.Learn to do molecular dynamics on the IAS model using the existing software.
2.Compute thermal conductivity or other physical properties of the IAS model of a Zr-based metallic glass.

Contact:

E: zbigniew.stachurski@anu.edu.au
W: http://engnet.anu.edu.au/DEpeople/Zbigniew.Stachurski

Applied Signals and Telecommunications - 16 projects

Show Projects...

Software Development for a Mobile Communications System

Project Code:AS&T_01

Supervisor:

Dr Leif Hanlen

Outline:

Inertial measurement units (IMU's) use "dead-reckoning" to estimate the location and movement of an object. Typically IMU's use a single 3D accelerometer (measures acceleration in x,y,z directions) a gyroscope (measures angular velocity) and other sensors. Typically, IMU's have one of each type of sensor: the iPoD may be considered a simple IMU since it has a single 3D-accelerometer.
Estimating position and direction using these IMU's is difficult, and noisy since the sensors are co-located. There has been some recent work on using multiple accelerometers. The accelerometers can be spatially separated by a small distance in x,y,z directions, and using 6 or more simple accelerometers, most other sensors may be replaced. There are a number of questions to be addressed:

Given a geometric arrangement, how should the sensor information be combined?
How many accelerometers really help? (should we use more or less than 6?)
How far apart should they be?
What happens with noise?

Requirements/Prerequisites

The project will require understanding of Matlab, for simulation focused results. Some simple control- or comms- theory will help but is not essential. Hardware engineers are available for discussion in regard to the capabilities of the sensing devices.
web ref: http://www.mycoordinates.org/april09/cali.php">multiple accelerometers
The student should be familiar with MATLAB programming. Hardware development is not necessary
If you are the successful applicant for this project, you will be located off campus at NICTA's Canberra Research Lab.

Contact:

E: leif.hanlen@nicta.com.au

Cloaking Devices

Project Code:AS&T_06

Supervisor:

Dr Leif Hanlen

Outline:

Is it really Science-Fiction? Active noise headphones are available off-the-shelf (at a price). These devices essentially work by detecting a wavefield (a sound wave) and applying a negative wave so that when the original sound and the negative wave enter the ear canal, the user does not detect the original sound. Can the principle be applied to a remote region - so that the "active headphone" cancels out sounds "over there" rather than only inside the ear?
Theory says yes - since we can reconstruct a soundfield remotely using beam-forming and we can generate sounds remotely using directional speakers (also beam-forming). The problems are:

(1) How do we get the sound cancelled where we want it without being horribly loud somewhere else and

(2) How do we cope with noise?

Requirements/Prerequisites

The project will require understanding of Matlab, for simulation focussed results. We also have a number of Texas-Instruments signal processing boards and a hardware design lab if students wish to develop a hardware demonstrator.
The student should be familiar with MATLAB programming. Hardware development is not necessary.
If you are the successful applicant for this project, you will be located off campus at NICTA's Canberra Research Lab.

Contact:

E: leif.hanlen@nicta.com.au

Independence of physical measurements for swimming

Project Code:AS&T_07

Supervisor:

Dr Leif Hanlen

Outline:

NICTA currently has over 100hours of swimming data obtained from inertial measurement devices. The inertial devices include acceleration and gyroscope as well as a number of other type of sensor. This project will evaluate the independence of the inertial data over different time scales. we will examine common measures of independence such as correlation, and look at less common measures such as mutual information and differential entropy.

Goals of this project

Advanced students may be able to use the material to develop an activity prediction and/or recognition algorithm based on the sensor data.Students will have access to data gathered from Olympic swimmers as well as a wider interaction with members of the Human Performance Improvement project team. Details of the HPI project are given on the web.

http: // nicta. com. au/ research/ projects/ human performance improvement

Requirements/Prerequisites

A NICTA assignment of IP and a NICTA confidentiality agreement will be required for this project
If you are the successful applicant for this project, you will be located off campus at NICTA's Canberra Research Lab.

Background Literature

Owen Thomas, Gideon Dror, Matthew Robards, Alex J. Smola, and Peter Sunehag. Wearable-sensor activity analysis using semi-Markov models with a grammar. Technical report, NICTA, 2008.
Matthew Robards and Peter Sunehag. Activity recognition and physiological monitoring using semi-markov nearest centroid methods. Technical report, NICTA, 2008.
Owen Thomas, Peter Sunehag, Gideon Dror, S. Yun, S. Kim, Matthew Robards, Alex J. Smola, Daniel Greene, and Philo Saunders. Wearable-sensor activity analysis using semi-Markov models with a grammar. submitted to Elsevier, 2008.

Related NICTA project:Human Performance Improvement

Contact:

E: leif.hanlen@nicta.com.au

Correlation of Perceived Performance, and Physical Measurements

Project Code:AS&T_08

Supervisor:

Dr Leif Hanlen

Outline:

There is a loose relationship between the physical performance of an Olympic athlete and their perceived performance [1, 2, 3]. A number of rating systems exist, in particular, the Relative Perceived Effort (RPE) score. The score rates (on a scale of 0-10) the effort involved in a given task: 0 denotes no effort at all, while 10 denotes near collapse.
Currently, at the Australian Institute of Sport, Olympic basket-ballers measure the effort involved in training via the RPE score. Similar schemes are used around the world [4]. The basket-ballers complete a training exercise and fill in a form of their perceived effort.
Is there a relationship between RPE and physical effort? NICTA and the AIS have recorded heart-rate levels and accelerometer readings from a number of activities, and the athletes have put in their perceived effort [5].
The objective of this project is to evaluate the the conjecture that some as yet unknown combination of accelerometer data and heart-rate data might be used to predict the RPE score of an athlete.

Goals of this project

The objective of this project is to determine if there is a relationship between the two types of data: a subjective performance measure and physical (acceleration and heart-rate) measurement.
Students will have access to data gathered from Olympic basketballers as well as a wider interaction with members of the Human Performance Improvement project team. Details of the HPI project are given on the web.

http: // nicta. com. au/ research/ projects/ human performance improvement

Requirements/Prerequisites

This project is ideally suited to students who have an interest in mathematics and statistics. Some Matlab experience will be helpful. A student who has completed 2nd-year mathematics will have sufficient skill to complete the tasks.
If you are the successful applicant for this project, you will be located off campus at NICTA's Canberra Research Lab.

Background Literature

Gunnar Borg. Borg's Perceived Exertion and Pain Scales. 1998.
T. Busso, R. Candau, and J.-R. Lacour. Fatigue and fitness modelled from the effects of training on performance. EURASIP Journal of Applied Physiology, 69:50-54, 1994.
Michael J. Chen, Xitao Fan, and Sandra T. Moe. Criterion-related validity of the Borg ratings of perceived exertion scale in healthy individuals: a meta-analysis. Journal of Sports Sciences, November 2002.
Lawrence Cheng, Steve Hailes, Denis Leung, Fu-Yi Fan, Yang Yang, and Zhen Cheng. Poster abstract: An experimental study on a motion sensing system for sports training. In European Conference on Wireless Sensor Networks, Bologna, Italy, 2008.
Leif W. Hanlen. Estimating workload and effort based on wearable sensors. Technical Report CRL-1615, NICTA, May 2008.

Related NICTA project:Human Performance Improvement

Contact:

E: leif.hanlen@nicta.com.au

Lognormal-Rayleigh combination model for human shadowing of MIMO radio signals

Project Code:AS&T_09

Supervisor:

Dr Leif Hanlen

Outline:

There has been some conjecture in the communication theory research world as to whether or not radio signals around the human body can be statistically characterised by combining two well known statistical distributions:

Log-normal: a typical model used for long-term shadowing - such as is experienced by mobile phones when cars pass through trees.
Rayleigh: a typical radio model for indoor environments an WiFi communications.

Can the two models be combined to give shadowed-non-line-of-sight radio
model for people?

Requirements/Prerequisites

A NICTA assignment of IP and a NICTA confidentiality agreement will be required for this project
Students will have access to data gathered from several wireless network measurement campaigns as well as a wider interaction with members of the Human Performance Improvement project team. Details of the HPI project are given
on the web.
http: // nicta. com. au/ research/ projects/ human performance improvement
This project will require some basic wireless communications knowledge and statistics. Matlab programming knowledge will be a significant help.
If you are the successful applicant for this project, you will be located off campus at NICTA's Canberra Research Lab.

Background Literature

Jian (Andrew) Zhang, David B. Smith, Leif W. Hanlen, Dino Miniutti, David Rodda, and Ben Gilbert. Stability of narrowband dynamic body area channel. IEEE Antennas and Wireless Propagation Letters, page to appear, 2009.
David B. Smith, Leif W. Hanlen, Jian (Andrew) Zhang, Dino Miniutti, David Rodda, and Ben Gilbert. Characterization of the dynamic narrowband on-body to off-body area channel. In IEEE International Conference on Communications, ICC, page to appear, Dresden, Germany, June 2009.
David B. Smith, Dino Miniutti, Leif W. Hanlen, Jian (Andrew) Zhang, David Rodda, and Ben Gilbert. IEEE P802.15-09-0187-00-0006 power delay profiles for dynamic narrowband body area network channels. IEEE submission, March 2009.
David B. Smith, Dino Miniutti, Leif W. Hanlen, Jian (Andrew) Zhang, David Rodda, and Ben Gilbert. IEEE P802.15-09-0186-00-0006: Dynamic channel measurements around 400MHz for body area networks. IEEE submission, March 2009.
Dino Miniutti, Leif W. Hanlen, David B. Smith, Jian (Andrew) Zhang, Daniel Lewis, David Rodda, and Ben Gilbert. IEEE 802-15-08-0421-00-0006 narrowband channel characterization for body area networks [IEEE-802.15.08.0421.00.0006]. Technical report, IEEE802.15.6, June 2008.
Dino Miniutti, Leif W. Hanlen, David B. Smith, Jian (Andrew) Zhang, Daniel Lewis, David Rodda, and Ben Gilbert. IEEE P802.15-08-492-00-0006: Summary of NICTA channel measurements. IEEE submission, July 2008.
David B. Smith, Dino Miniutti, Jian (Andrew) Zhang, and Leif W. Hanlen. IEEE P802.15-08-0850-00-0006: Matlab code for generating BAN fading profile [IEEE P802.15-08-0850-00-0006]. IEEE submission, November 2008.
David B. Smith, Leif W. Hanlen, Dino Miniutti, Jian (Andrew) Zhang, David Rodda, and Ben Gilbert. Statistical characterization of the dynamic narrowband body area channel. In ISABEL, International Symposium on Applied Sciences in Bio-Medical and Communication Technologies, Aalborg, Denmark, October 2008.
Dino Miniutti, Leif W. Hanlen, David B. Smith, Jian (Andrew) Zhang, Daniel Lewis, David Rodda, and Ben Gilbert. IEEE 802.15-08-354-01-0006 dynamic narrowband channel measurements around 2.4GHz for body area networks IEEE 802.15-08-354-01-0006. IEEE submission, May 2008.

Related NICTA project:Human Performance Improvement

Contact:

E: leif.hanlen@nicta.com.au

3D Space-Time model for wireless body area networks

Project Code:AS&T_10

Supervisor:

Dr Leif Hanlen

Outline:

Body Area Networks (IEEE 802.15.6) represent the next generation wireless personal area networks. They will provide networking for small smart sensors, with up to 10 piconets per person, and 256 nodes per net (ie, up to 2000 nodes per person).
The technical requirements of the new standard are given online [1]. Details of the channel modelling campaigns may be found in: [2, 3, 4] and the complete wireless channel model given by [5].
So far, there are no statistical models for the propagation of a wireless signal around the human body from any point to any other point.

Goals of this project

This project will develop a model, in 3D space and over time to give a spatial Jakes-model [6, 7, 8] for point-to-point communication around the human body.

Requirements/Prerequisites

This project will require some basic wireless communications knowledge and statistics. Matlab programming knowledge will be a significant help.
Students will have access to data gathered from several wireless network measurement campaigns as well as a wider interaction with members of the Human Performance Improvement project team. Details of the HPI project are given
on the web. http: // nicta. com. au/ research/ projects/ human performance improvement
A NICTA assignment of IP and a NICTA confidentiality agreement will be required for this project
If you are the successful applicant for this project, you will be located off campus at NICTA's Canberra Research Lab.

Background Literature

Bin Zhen, Maulin Patel, SungHyup Lee, EunTae Won, and Arthur Astrin. TG6 technical requirements document (TRD) IEEE P802.15-08-0644-09-0006. https://mentor.ieee.org/802.15, September 2008.
Hind Chebbo. Literature review of energy efficient MAC in WSN/BAN [IEEE-15-08-0331-00-0006], May 2008.
Benjamin Chang, James Hou, Dae-Ki Cho, and Mario Gerla. Minimizing 802.11 interference on Zigbee medical sensors. In SIG Bodynets, Los Angeles, CA, April 2009.
ShihHeng Cheng and ChingYao Huang. Network merging: Design strategies of an ultra low power and high reliability MAC. IEEE submission, Doc.ID. IEEE 802.15-09-0227-00-0006, March 2009.
Kamya Yekeh Yazdandoost and Kamran Sayrafian-Pour. Channel model for body area network (BAN). IEEE submission, Doc.ID: IEEE-802.15-08-0033-00-0006, November 2008.
Xuanming Dong. Effect of Slow Fading and Adaptive Modulation on TCP/UDP Performance of High-Speed Packet Wireless Networks. PhD thesis, University of California at Berkeley, 2006.
Z. Wu. Model of independent Rayleigh faders. IEE Electronics Letters, 40(15):949-951, July 2004.
W. C. Jakes, editor. Microwave Mobile Communication. IEEE Press, New York, USA, 1974.

Related NICTA project:Human Performance Improvement

Contact:

E: leif.hanlen@nicta.com.au

Automatic Fuseball playing system

Project Code:AS&T_11

Supervisor:

Dr Leif Hanlen

Outline:

Fuseball is the table-top equivalent of soccer (football). This project will develop a computer player to challenge human fuseball players. The project will appeal to students who are interested in robotics and embedded system design. There are a large number of independent components to this project which may be undertaken by separate individuals. Topics of note are the visual system to track the status of the table in real time, a control system to drive the computer player's team and a strategic system to make the computer unbeatable! It is expected the project can be developed to a demonstrable state in 3 months by a team of 6-10 dedicated students.
This project will be an excellent introduction to the complex world of real-time embedded system design and should also be great fun!
This is a project for approximately 6-10 summer scholar students.
In an extension we would like to incorporate the sensor glove into the game, so that the computer player may "learn" good strategies. This will require students who understand signal processing - to extract the relevant information form the glove - and students who understand online machine learning (how to use the data to make a better player).
Realistically, the sky is the limit for what you can do with this project.

Requirements/Prerequisites

Depending upon the particular aspect of the project students will need to be familiar with one or more:

mechanical systems design and implementation (constructing drive mechanisms for the fuseball actuators)
machine vision (tracking a ball using a high-speed cameras, and tracking players using a high-speed camera)
control systems (implementing PID controllers and state-space controllers)
machine-game algorithms for strategy (eg. 2nd year computer science for implementing a chess-playing algorithm)
multi-dimensional signal processing (to estimate the current game state and to generate feedback for the controllers)

DSP programming, Graphics processing unit programming and FPGA design may be helpful, but are not essential.

Contact:

E: leif.hanlen@nicta.com.au

Wireless Modem Realisation

Project Code:AS&T_12

Supervisor:

Dr Mark Reed

Outline:

Goals of this project

The goal of the project is to build a modem that can be tested and validated for potential use within a much bigger project.

Requirements/Prerequisites

The student is expected to have used MATLAB and have some understanding and desire to work on communication systems.

Student Gain

The student will work with a number of researchers to perform the task and get a much better idea what is involved in real communication systems design.

Contact:

E: mark.reed@nicta.com.au
W: http://users.rsise.anu.edu.au/~mreed/

GPU Implementation of Important Decoding Algorithms in Communication Systems

Project Code:AS&T_13

Supervisor:

Dr Parastoo Sadeghi

Outline:

Reliable transmission of infomation over communication channels often requires sophisticated algorithms to be implemented at the transmitter and receiver ends. Fast simulation of such algorithms during design stages of a communication system is vital for a successful practical implementation. The new generation of powerful and general-purpose graphic processing units (GPU) provides new opportunities for very efficient communication system evaluation and analysis.
For example, to combat channel noise, powerful channel error connection codes are used at the transmitter. The receiver needs to accordingly employ appropriate decoders to choose the 'most likely' transmitted signal. It is important to understand how a coding/decoding technique performs in a given channel condition. Two other main tasks of a receiver (especially in wireline ADSL and wireless fading channels) are to combat inter-symbol interference (ISI) and also possible time variations in the channel.

Goals of this project

In this project, the student will gain a good understanding of important and widely used coding and decoding techniques, such as convolutional codes and Viterbi decoders by first implementing and testing them on the CPU. Meanwhile he/she will aquire programming skills on NVIDIA CUDA platform. The next step is to devise the best strategy to make the best use of parallel processing capabilities of GPU and to transform the code onto the GPU. The code will then be tested and compared with standard CPU implementation. If the time permits, other major decoding algorithms such as Turbo decoders and even Turbo equalizers will also be investigated.
The project will provide an excellent opportunity for the student to work in a research team with researchers in the school of Engineering at the ANU. It is expected that the outcome of the research be submitted as a technical paper to a national or international conference for publication.

Requirements/Prerequisites

A good understanding of C programming language is required. Familiarity with digital communications and Metlab programming will be a benefit.

Background Literature

J.G.Proakis, Digital Communications, Fourth Edition, McGraw Hill, 2000
Compute Unified Device Architecture (CUDA) Programming Guide, version 2.2, NVIDIA, http://developer.nvidia.com/object/cuda.html, 2009

Contact:

E: parastoo.sadeghi@rsise.anu.edu.au
W: http://users.rsise.anu.edu.au/~parastoo/

Optimizing Channel Estimation for Higher Data Rate in Wireless Communication Systems

Project Code:AS&T_14

Supervisor:

Dr Parastoo Sadeghi
Dr Tharaka Lamahewa

Outline:

In wireless communications, the signal transmitted through the wireless environment (i.e. the channel) experiences distortion in its amplitude and phase. The receiver needs to have an accurate estimation of what has been done by the channel in order to decode the signal. Conventional wireless networks usually spend a considerable amount of transmit resource on channel estimation. For example, GSM system sacrifices about 22% of the data transmission time to perform channel estimation. However, this results in a direct reduction in the actual data rate. Therefore, optimizing the amount of resource spent on channel estimation is crucial for high data rate transmission, which has been an active area of research in recent years.
In this project, the student will gain a good understanding on transmission design and performance analysis for future wireless communication systems. After an introduction to the topic and relevant background studies, the student will choose either of the following options:

a. Extending the existing optimal design for time-vary single-input single-output (SISO) channels to multiple-input multiple-output (MIMO) channels.
b. Investigating the optimal design in relay and cooperative communication networks.

Requirements/Prerequisites

Familiarity with wireless communications and Matlab will be a benefit.

Student Gain

The project will provide an opportunity for the student to work in a research team with researchers in the School of Engineering at the ANU. It is expected that the outcome of the research be submitted as a technical paper to a national or international conference for publication.

Background Literature

Pilot symbol transmission for time-varying fading channels: an information-theoretic optimization," Proc. Int. Conf. on Signal Processing and Commun. Syst., Dec. 2007. W:http://users.rsise.anu.edu.au/~parastoo/papers/2007/ICSPCS_2007.pdf

Contact:

E: parastoo.sadeghi@rsise.anu.edu.au
W: http://users.rsise.anu.edu.au/~parastoo/
E: tharaka.lamahewa@anu.edu.au
W: http://users.rsise.anu.edu.au/~tharaka/

Performance Evaluation of Wireless Communication Systems in a Temporally Correlated Channel Environment

Project Code:AS&T_15

Supervisor:

Dr Parastoo Sadeghi
Dr Tharaka Lamahewa

Outline:

Due to the nature of the wireless channel, the design of the wireless systems fundamentally differs from wired system designs. The wireless channel is much more unpredictable than the wired channel because of factors such as multipath, mobility of the user, mobility of the objects in the environment and delays arising from multipaths. In this project we particularly aim to investigate/evaluate the performance measures such as packet error rate (PER) and packet error density (PED) of a wireless communication system when the receiver is moving (mobile) and the environment surrounding the receiver is non-uniform.
In this project, the student will be exposed to following research areas: wireless channel modelling, finite state Markov channel (FSMC) modelling and, PER and PED calculations.

Requirements/Prerequisites

Familiarity with wireless communications and Matlab will be a benefit.

Student Gain

Background Literature

1. "Finite-State Markov Modeling of Fading Channels", IEEE Signal Processing Mag., 2008, W: http://users.rsise.anu.edu.au/~parastoo/papers/2008/SPM_2008.pdf

Contact:

E: parastoo.sadeghi@rsise.anu.edu.au
W: http://users.rsise.anu.edu.au/~parastoo/
E: tharaka.lamahewa@anu.edu.au
W: http://users.rsise.anu.edu.au/~tharaka/

Wireless tweeting

Project Code:AS&T_16

Supervisor:

Dr Leif Hanlen

Outline:

We have developed a number of wireless signal transmitters and have a number of iPods (with developer access). We are looking to develop a twitter app which wirelessly tweets details of the subject's activities through the day.

Simple examples are: based on a number of wireless locators, tweet the where-abouts of the subject in the office every N (eg. 30) minutes.
Include the accelerometer app to tweet the activity level of the subject

Requirements/Prerequisites

This project will require a solid ability to program C++ and app's in iPod as well as develop custom PHP scripts to access the tweeting facility. You will have a support with firmware engineers, but will need to be self motivated for the coding side.

Contact:

E: leif.hanlen@nicta.com.au

Artificial Intelligence - 17 projects

Show Projects...

Universal Artificial Intelligence

Project Code:AI_01

Supervisor:

Dr Marcus Hutter

Outline:

The dream of creating artificial devices that reach or outperform human intelligence is an old one. Most AI research is bottom-up, extending existing ideas and algorithms beyond their limited domain of applicability. The information-theoretic top-down approach pursued in [Hut05] justifies, formalizes, investigates, and approximates the core of intelligence: the ability to succeed in a wide range of environments [LH07]. All other properties are emergent.

Goals of this project

The fundamentals of UAI are already laid out, but there are literally hundreds of open questions (see the exercises in [Hut05]) in this approach that have not yet been answered. The complexity ranges from suitable-for-short-projects to full PhD theses and beyond.

Requirements/Prerequisites

background in AI or ML or statistics or information theory.
good math skills
good writing skills

Student Gain

getting acquainted with the most comprehensive theory of rational intelligence to date.
,li>getting experience in writing a literature survey (if well done may be publishable), -or-
learn how to prove non-trivial theorems.

Background Literature

[Hut05] M. Hutter. Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability Springer, Berlin, 2005.
[LH07] S. Legg and M. Hutter. Universal intelligence: A definition of machine intelligence. Minds & Machines, 17(4):391--444, 2007.

Contact:

E: marcus.hutter@anu.edu.au
W: http://www.hutter1.de/rsise/index.htm

Human Knowledge Compression Contest

Project Code:AI_02

Supervisor:

Dr Marcus Hutter

Outline:

Being able to compress well is closely related to intelligence as explained below. While intelligence is a slippery concept, file sizes are hard numbers. Wikipedia is an extensive snapshot of Human Knowledge. If you can compress the first 100MB of Wikipedia better than your predecessors, your (de)compressor likely has to be smart(er). The intention of the Human Knowledge Compression Prize [Hut06] is to encourage development of intelligent compressors/programs.

Goals of this project

Some of the following four subgoals shall be addressed:

Get acquainted with the current state of the art compressor and in particular with the prize winning paq8hpX series, and write a brief comparative survey.
Test some novel compression ideas. If successful,
Integrate them into one of the state-of-the-art compressors.
Investigate some alternative performance measures that take the compressor more seriously into account (rather than only the decompressor).

Requirements/Prerequisites

good programming skills
experience in understanding and extending existing code
good writing skills
performing (computer) experiments and analyzing results

Student Gain

getting acquainted with state-of-the-art compression methodologies.
winning a prize (but don't count on it, it's going to be tough).

Background Literature

[Hut06] M. Hutter. Human knowledge compression prize, 2006. open ended, http://prize.hutter1.net/

Contact:

E: marcus.hutter@anu.edu.au
W: http://www.hutter1.de/rsise/index.htm

On the Foundations of Inductive Reasoning

Project Code:AI_03

Supervisor:

Dr Marcus Hutter

Outline:

Humans and many other intelligent systems (have to) learn from experience, build models of the environment from the acquired knowledge, and use these models for prediction. In philosophy this is called inductive inference, in statistics it is called estimation and prediction, and in computer science it is addressed by machine learning. The problem of how we (should) do inductive inference is of utmost importance in science and beyond. There are many apparently open problems regarding induction, the confirmation problem (Black raven paradox), the zero p(oste)rior problem, reparametrization invariance, the old-evidence and updating problems, to mention just a few. Solomonoff's theory of universal induction based on Occam's and Epicurus' principles, Bayesian probability theory, and Turing's universal machine [Hut05], presents a theoretical solution [Hut07].

Goals of this project

Elaborate on some of the solutions presented in [Hut07].
Present them in a generally accessible form, illustrating them with lots of examples.
Address some other open induction problem.

Requirements/Prerequisites

being able to think sharply
reasonable to good math skills
good writing skills
interest in the philosophical foundations of induction

Student Gain

getting acquainted with Bayesian reasoning and algorithmic information theory.
interdisciplinary work between philosophy, computer science, and statistics
writing a small paper (e.g. for a philosophy journal)

Background Literature

[Hut07] M. Hutter. On universal prediction and Bayesian confirmation. Theoretical Computer Science, 384(1):33--48, 2007.
[Hut05] M. Hutter. Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability Springer, Berlin, 2005.

Contact:

E: marcus.hutter@anu.edu.au
W: http://www.hutter1.de/rsise/index.htm

Universal Induction versus No Free Lunch

Project Code:AI_04

Supervisor:

Dr Marcus Hutter

Outline:

Solomonoff's theory based on Occam's razor provides a rigorous and formal method for inductive inference, prediction, and time series forecasting, by essentially uniquely specifying a universal model class and prior. This model seems to solve the long outstanding (philosophical) problem of induction and many other deep statistical questions [Hut07]. On the other hand, the No-Free-Lunch theorem(s) [WM97] state that all optimization or search algorithms are on average equally good/bad, if a uniform average over the space of all functions is taken. So NFL believers conclude that we need a prior, biased to our particular problem class at hand, that is, there is no universal (problem independent) solution to the induction problem. There is an ongoing battle between believers in Occam's razor and believers in no-free-lunches [Sto01,SH02].

Goals of this project

do a literature search on NFL and Solomonoff induction
compare the statements and show that there is no contradiction
write up a survey paper comparing and clarifying the two positions
argue that NFL is true but irrelevant.

Requirements/Prerequisites

interest in philosophical foundations
ability to clearly interpret the meaning of mathematical theorems
excellent writing skills

Student Gain

Getting acquainted with one of the most profound problems in the philosophy of science from a rigorous mathematical perspective
If the report is well-written it could develop into a (well-cited) paper

Background Literature

[Hut07] M. Hutter. On universal prediction and Bayesian confirmation. Theoretical Computer Science, 384(1):33--48, 2007.
[WM97] D. H. Wolpert and W. G. Macready. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1):67-82, 1997.
[SH02] J. Schmidhuber and M. Hutter. Universal learning algorithms and optimal search. NIPS 2001 Workshop, 2002. http://www.hutter1.net/idsia/nipsws.htm.
[Sto01] D. Stork. Foundations of Occam's razor and parsimony in learning. NIPS 2001 Workshop, 2001. http://www.rii.ricoh.com/~stork/OccamWorkshop.html.

Contact:

E: marcus.hutter@anu.edu.au
W: http://www.hutter1.de/rsise/index.htm

Projects in Automated Planning

Project Code:AI_05

Supervisor:

Dr Patrik Haslum

Outline:

Planning is a branch of AI concerned with automating the reasoning that goes on in formulating plans, in the sense of a series of steps to be taken, each of which has some effect on the state of (a highly abstract model of) the world, so as to bring about some desired end state. As a prototypical example, one may take single-player games ("puzzles") such as the Freecell card game or the old Rubik's Cube. Possible applications of automated planning methods (aside from solving puzzles) are many and diverse: examples include airport ground traffic control, computing genome edit distances, testing protocols for logical flaws, or controlling a printer.

Goals of this project

Requirements/Prerequisites

basic background in machine learning or natural language processing
good programming skills
experience in understanding and extending existing code
performing (computer) experiments and analyzing results

Background Literature

Software discussed at http://en.wikipedia.org/wiki/Named_entity_recognition

Contact:

E: wray.buntine@nicta.com.au

Automated Hierarchical Classification of Web Pages

Project Code:AI_12

Supervisor:

Dr Wray Buntine

Outline:

Classifying web pages according to the ODP Web directory data http://www.dmoz.org/, or alternative according to the hierarchy in the Wikipedia, is a useful task for many kinds
of document analysis. The challenge is in processing the HTML and text content, in applying supervised learning (we at the SML group, http://sml.nicta.com.au/, have many such tools), in scaling to large hierarchies (100's to 1000's) and in scaling to an acceptable performance on a standard server. This is an open ended task that could use theoretical work (methods to address scaling and/or hierachical issues) or applied work (exploring the use of existing techniques), or both.

Requirements/Prerequisites

good programming skills
experience in understanding and extending existing code
performing (computer) experiments and analyzing results

Background Literature

see the challenge web site at http://lshtc.iit.demokritos.gr/, however we wont be entering this, but are motivated by it
see the Elefant website for some of our tools, http://elefant.developer.nicta.com.au/

Contact:

E: wray.buntine@nicta.com.au

Image Search via Kernelised Sorting

Project Code:AI_13

Supervisor:

Dr Wray Buntine

Outline:

Kernelised sorting does visual layout of images according to visual structures such as hierarchical plates, rotating spheres and fish-eye style graphs. We have a basic system developed but wish to further improve or create different visual structures, the subsequent user interface, and then to perform user testing.

Requirements/Prerequisites

basic background in machine learning or optimisation
good web programming skills
experience in understanding and extending existing code
performing user studies and analyzing results

Background Literature

Novi Quadrianto, Le Song, Alex J. Smola.

Kernelized Sorting. Advances in Neural Information Processing Systems NIPS 22, Vancouver, B.C., Canada, 2009.

Contact:

E: wray.buntine@nicta.com.au

Mapping Machine Learning

Project Code:AI_14

Goals of this project

The goal would be to obtain a deeper understanding of the performance of these algorithms such that a more informed choose between them can be made. Also this project would aim to understand how such problems perform in a high level language such as Java. This project would feed into other projects which involve implementing these approaches on novel hardware such as the Cell or GPUs.

Contact:

E: ericm@cs.anu.edu.au
W: http://cs.anu.edu.au/people/Eric.McCreath

e-Government - 1 projects

Show Projects...

Performance Modelling Service Oriented Architecture (SOA) applications on Enterprise Service Buses (ESB)

Project Code:EG_01

Supervisor:

Dr Paul Brebner

Outline:

3 sub-projects: Performance data monitoring approaches, performance modelling application variations, and Virtualization and modelling experiments.

Following the success of our last years summer scholar project (a trial of NICTA's service-oriented performance modelling technology applied to the MULE Enterprise Service Bus (ESB) LoanBroker Application), we propose to extend this work as follows.
Performance data monitoring approaches

Investigation of requirements and approaches for capturing performance data from SOA/ESB infrastructures and applications. This sub-project involves understanding the requirements for capturing performance data for performance modelling, and investigating solutions for capturing data in experimental contexts from one or more open source ESBs and applications. We may trial some open source and commercial SOA/ESB monitoring tools as part of this project.
Performance modelling application variants

Conduct experiments and develop performance models of different architectural styles/variants of a ESB application. This follows on from last years work which was successful in building a model of one variant of the MULE LoanBroker application. This year we aim to build models for a number of variants of the application, and investigate the accuracy and power of the performance models to predict performance and scalability for known and hypothetical variants.
Virtualization and modelling experiments

This sub-project will involve deploying the SOA/ESB infrastructure and applications on virtualized servers and conducting experiments to determine if the performance models developed above correctly predict scalability and performance. We also plan to investigate the ability of virtual servers to isolate service consumers, and provide specified Quality of Service levels (such as maximum service response times) for different consumers.

Requirements/Prerequisites

We require 3 students with interest in, and experience with software engineering, software architectures, web services, SOA, ESB, Java, software performance and scalability.

Background Literature

e-Government project, http://nicta.com.au/research/projects/egov
ePASA technology, http://nicta.com.au/research/projects/egov/epasa

Logical Analysis of Business Rules

Project Code:LC_03

Supervisor:

Dr Peter Baumgartner

Outline:

Business Rules are widely used in industry to specify the conditions or constraints that control the operations of a business. For instance, business rules might reflect legislation governing visa regulations, or conditions under which social benefits are granted.

Goals of this project

The background of this project is a running project between NICTA and a Canberra-based SME. We are building a prototype system that allows to analyze sets of business rules for logical errors, such as inconsistencies and functional dependencies. Spotting such errors is of great importance in the development phase of business rules. The goal is to help with extending our already existing prototype by enhancing its functionality (both the user front-end and the internal reasoning engine).

Requirements/Prerequisites

Good Java programming skills. Some background in mathematical logic and/or constraint processing would be useful.

Student Gain

- Becoming acquainted with business rules and automated reasoning techniques
- Involvement in leading-edge applied research in the intersection of
these areas
- Perspective of leading to a Ph.D. project

Requirements/Prerequisites

A strong background in maths or logic would be extremely useful but is
not necessary. Of more importance is enthusiasm and an interest in the
formal side of AI.

You will need to learn the theory of modal logics and how to automated
them. Then you will need to learn the uses of these logics in AI.
Finally, you will need to use our technology to build the provers that
are needed by AI researchers.

This project will introduce you to an area of theoretical computer
science which mixes theory and practice
and may lead to a publication. It will form a good basis for doing a
Phd, either with us, or at another university.

Contact:

E: rajeev.gore@anu.edu.au
W: http://arp.anu.edu.au/~rpg

Photovoltaic Solar Science and Technologies - 9 projects

Show Projects...

Properties of Flexible Solar Modules

Project Code:PVS_01

Supervisor:

Dr Vernie Everett
Dr Elizabeth Thomsen

Outline:

Micro-modules of flexible silicon solar cells are being developed for applications such as powering small portable electronic devices. The requirements for these modules are that they are highly efficient, lightweight, flexible, and able to operate under a range of light conditions. Monocrystalline silicon is capable of high efficiencies; however at the thicknesses used in most photovoltaic (PV) applications it does not display the necessary flexibility. If the silicon is thinned to less than 50 microns, it can provide high flexibility such that the cells can be wrapped around a finger. Hence, the modules being developed require very thin silicon cells along with flexible connections and packaging. This project involves testing the modules under a range of light conditions, as well as exploring mechanical properties such as flexibility.

E: Dr Elizabeth Thomsen
Dr Vernie Everett

Contact:

E: vernie.everett@anu.edu.au
E: elizabeth.thomsen@anu.edu.au

Iron imaging in multicrystalline silicon wafers for solar cells

Project Code:PVS_02

Requirements/Prerequisites

The student will have experience on C/C++ and Matlab programming. Some knowledge on computer vision and pattern recognition will be a plus. The student will work closely with the supervisor on a day-to-day basis. Also, this project provides opportunities to interact with the excellent researchers and students in RSISE and NICTA.

Background Literature

Smeulders et al., "Content-Based Image Retrieval at the End of the Early Years", IEEE Transactions on PAMI, 2000.
Rui et al. "Relevance feedback: a power tool for interactive
content-based image retrieval", IEEE Transactions on CSVT, 1998.
Csurka et al. "Visual categorization with bags of keypoints", ECCV 2004.

Contact:

E: lei.wang@anu.edu.au
W: http://users.rsise.anu.edu.au/~wanglei/

Patch-based object recognition with discriminative visual codebooks

Project Code:R&CV_05

Supervisor:

Dr Lei Wang

Outline:

Nowadays, computers have been able to easily "see" this world through digital imaging equipments. However, enabling a computer to "understand" what it has seen still remains a difficult problem. Playing a critical role in solving this problem, object recognition aims to recognize the object (for instance, a motorcycle or an airplane) in an image through its appearance. Recent years have witnessed the significant progress of patch-based object recognition. In this approach, a number of small patches are cropped from an image, each of which is characterized by a local invariant feature. Thus, each image is represented by a bag of features. This process can be intuitively understood by viewing an image as a "document" and the small patches as "words". For recognition, a visual codebook (a "dictionary" of visual words) is often needed to be created. With the codebook, each image is represented by the number of occurrence of each visual word in this image. The design of visual codebooks has significant impact on the recognition performance.

Goals of this project

This project involves image patch sampling, local invariant feature extraction and particularly the visual codebook design. Since the ultimate goal of object recognition is to predict the type of the object in a given image as accurately as possible, a discriminative visual codebook will be desired. A good web resource and some recently published papers on patch-based object recognition are listed in the reference part below.

Requirements/Prerequisites

The student will have experience on C/C++ or Matlab programming. Some knowledge on computer vision and pattern recognition will be a plus. The student will work closely with the supervisors and the post-graduates on a day-to-day basis. Also, this project provides opportunities to interact with the excellent researchers and students in RSISE and NICTA.

Background Literature

http://www.vision.caltech.edu/Image_Datasets/Caltech101/Caltech101.html
Sivic and Zisserman, "Video Google: A text retrieval approach to object matching in videos", ICCV 2003
Csurka et al. "Visual categorization with bags of keypoints", ECCV 2004.
Moosmann et al. "Fast Discriminative Visual Codebooks using
Randomized Clustering Forest", NIPS 2006.

Contact:

E: lei.wang@anu.edu.au
W: http://users.rsise.anu.edu.au/~wanglei/

Where am I? ---- Visual Localization on the ANU Campus

Project Code:R&CV_06

Supervisor:

Dr Lei Wang

Outline:

"Where am I" is a question we seldom ask others because usually our eyes can give the answer. However, imagine that it is the first time for you to visit the campus of ANU. Can you find out where you are by simply looking at the buildings that you have never seen before?
Recent development in computer vision technology has equipped modern computers with the capability to "see" and recognize objects. One interesting application of this capability is a visual localization system: the user takes a photo of the environment in which he/she is (for example, with a digital camera or simply a mobile phone), sends the photo to the system and the system will tell the user where you are. To implement such a system, we need a database to store the photos of the buildings on campus. Also, a algorithm is needed to
extract visual information from each photo. Then, given a query image, the system will compare the visual information extracted from the query image with those stored in the database to identify which part of the campus has been captured in the
query image. The question of "where am I" is then answered based on this result.

Goals of this project

This project is composed of several interesting steps such as building the database, visual feature extraction, defining the similarity measure to evaluate whether a buldiing in a query image is same to those in the database, and designing an indexing mechanism supporting efficient search in the database. Plenty of resource for each step can be found from the literature. The student may focus on one of them or build up the whole system.

Requirements/Prerequisites

Background Literature

Paletta, L. Fritz, G. Seifert, C. Luley, P. Aimer, A., "A Mobile Vision Service for Multimedia Tourist Applications in Urban Environments", Intelligent Transportation Systems Conference, 2006.
Yuhang Zhang, Lei Wang, Richard I. Hartley, Hongdong Li: "Where's the Weet-Bix?" ACCV 2007.
Sivic and Zisserman, "Video Google: A text retrieval approach to object matching in videos", ICCV 2003
Csurka et al. "Visual categorization with bags of keypoints", ECCV 2004.

Contact:

E: lei.wang@anu.edu.au
W: http://users.rsise.anu.edu.au/~wanglei/

Optimising computer vision applications for resource constrained devices (3 student projects)

Project Code:R&CV_10

Supervisor:

Dr Lars Petersson

Outline:

Project #1: Design and implement a capture and processing scheduler on the Asus R50A for optimizing application resource utilisation

Project #2: Design and implement a synchronisation manager on the Asus R50A for optimising wireless communication network resource utilisation

Project #3: Design and implement a coordination systems for coordinating the processing activities of multiple Asus R50A platforms

This series of projects deals with a real-world computer vision application to be used in vehicles on the road. The challenge is that the computer vision algorithm to be used is computationally very resource intensive.
When the target deployment platform is resource constrained, for example a handheld device, intelligent decisions need to be made about how to schedule, forward plan and parameterise the computer vision algorithms in order to maximise the useful work performed by the application given the available resources. The goal of these projects is build systems which enable and carry out these decisions.
Development is being broken down into three smaller, but interrelated projects (outlined above). Each is challenging but manageable within the summer scholar program.

Goals of this project

The target platform for our computer vision application is resource constrained. The platform is the Asus R50A which is classed as an Ultra Mobile PC. The specifications and picture of the device are available below.

http://www.asus.com.au/products.aspx?l1=5&l2=25&l3=722

http://www.asus.com.au/999/images/products/2295/R50A_1.jpg

The device is a suitable target platform for a computer vision application given its 2MP (1024 x 768) camera, 3G wireless connectivity and reasonable computing power. In addition it also has an in-built GPS to geo-locate captured video data. The device has a form factor suitable for being deployed on the dashboard of a vehicle and comes with appropriate mounting accessories.

The device has been modified to run a standard Linux variant (Moblin) and all applicable device drivers have been configured. Students will have access to two or three of these devices for prototyping and testing their implementations.

The goal is to design and implement a software architecture within this resource constrained hardware environment which manages the computer vision application. It is part of the task of the summer scholars to define how the overall system architecture will work, how the individual components are best organised and how the three project elements will work together to schedule, forward plan and parameterise the computer vision algorithms to maximise the useful work performed.

Update: Check out the recent ComputerWorld media coverage of the NICTA project related to this summer scholar project: http://www.computerworld.com.au/article/313968/new_linux-based_technology_make_smarter_gps

Requirements/Prerequisites

You need to be well versed in the use of Linux, C++ programming and have general trouble-shooting skills.

The work will involve browsing and interfacing with a large existing code base.

At least one of the 3 students on the project needs to have a driver's license so that field testing of the device can be undertaken.

Student Gain

You will benefit from these projects as follows:
You will learn about

o Computer vision algorithms, planning and scheduling, messaging protocols, SQL-databases and designing complex systems on resource constrained devices
You will work collaboratively with 2 other students

o You will have colleagues to work with on your project

oCollaboratively develop interface specifications, messaging methods and planning and scheduling algorithms.
The results of your project will be put into active use upon successful completion and will be a valued contribution supporting future research and commercialisation activities
In addition to your Academic supervisor, you will have contact with NICTA's business development team

Contact:

E: lars.petersson@nicta.com.au
W: http://nicta.com.au/director/research/programs/asst/people/lars_petersson.cfm

Resource scavenging for a distributed Computer Vision application

Project Code:R&CV_11

Supervisor:

Dr Lars Petersson

Outline:

Project: Build a CPU cycle scavenging client/server system which contributes unused CPU cycles from donor machines to a central Linux cluster

Computer vision algorithms are often computationally intensive and in our application the most computationally intensive part is the scanning of video for the purpose of detecting and identifying objects of interest. Fortunately, our scanning problem breaks up well into sub problems and can be executed in parallel and distributed over many computers.

A common approach to cope with the scale of computing resources required in computer vision is to build dedicated compute clusters of x86 machines and distribute the problem over many dedicated CPU's, however this is not necessarily the most cost effective method.

In a standard office environment, there are many high powered computers and servers which sit idle most of the time, even whilst users are using them. Few desktop, laptop or server computers are ever used close to their computing capacity.

We would like to see these computers at 100% utilization by configuring the computers to contribute unused CPU cycles to a compute cluster whenever they are powered on. These office computers currently run a variety of operating systems, including Linux and Windows XP and are interconnected via a gigabit Ethernet network

The project is concerned with designing, building and porting to a variety of operating systems an application which can contribute the spare CPU cycles of underutilised machines to a computing cluster running computer vision applications.

The development and deployment of a distributed processing application means computer hardware is available both for primary business tasks like word processing and at the same time contributing spare CPU cycles as a compute node in a computer vision cluster. Having this software system means the we can realise a greater return on existing investments in hardware resources, reduce or eliminate the need to purchase additional computing resources as well as reducing energy overall consumption.

As CPU cycles are typically being donated voluntarily by users, it is an important requirement that there is little or no inconvenience to the donor and that they have at least some degree of control over when and how their resources are utilised. Ensuring minimal impact on the donor will be an important success factor for the successful adoption and deployment of the application.

Update: Check out the recent ComputerWorld media coverage of the NICTA project related to this summer scholar project: http://www.computerworld.com.au/article/313968/new_linux-based_technology_make_smarter_gps

Requirements/Prerequisites

Be well versed in C++ programming under both Linux and Windows

Possess the ability to quickly browse and understand a large existing code base.

Student Gain

You will benefit from these projects as follows:

You will learn about computer vision algorithms, distributed processing and SQL-databases.
The results of your project will be put into active use upon successful completion and will be a valued contribution supporting future research and commercialisation activities
In addition to your Academic supervisor, you will have contact with NICTA's business development team

Contact:

E: lars.petersson@nicta.com.au
W: http://nicta.com.au/director/research/programs/asst/people/lars_petersson.cfm

Useful Games

Project Code:R&CV_12

Supervisor:

Dr Lars Petersson

Outline:

Games are becoming closer and closer to real life and some tasks we have to solve in games are similar to tasks we have to solve in real life. One surprising difference is that for many people solving the tasks in games is much more fun and less tiring than in real life. Consequently, it can be useful to transform tasks or work we do not like into a game and do our work by playing a game--or even better, let other players do the work for us.

In this project we aim to develop a useful game for quality assurance of computer vision software. Our existing computer vision software is able to detect objects we see while driving a car. In order to test whether our software correctly identifies all objects, we have to manually browse through large amounts of video footage in several views. As part of this project we analyse how this task can be solved by a useful and fun game.

Contact:

E: lars.petersson@nicta.com.au
W: http://nicta.com.au/director/research/programs/asst/people/lars_petersson.cfm

Software Intensive Systems Engineering - 1 projects

Show Projects...

Monitoring Java programs in the real world

Project Code:SISE_01

Supervisor:

Dr Andreas Bauer

Outline:

The goal of this project is to investigate means of monitoring the behaviour of Java programs while they execute. Monitoring is useful in many different scenarios, e.g., for testing systems or in a security critical context to avoid systems entering a bad state when they execute.

On the site [1] an open-source tool can be downloaded which, given a formal specification of a monitorable property (which intuitively may translate to "it is always the case that a request signal is followed by an acknowledgement signal"), generates a so-called monitor for that property (more precisely, a state machine which acts as a monitor). The monitor can then be used to monitor the system for that particular property, that was prior entered by the user.

However, there does not currently exist an automatic translation from the generated monitor to actual Java code, which is needed for making this method work in the real world.

Thus, in this project, the student should investigate means for instrumenting Java programs such that they can be combined with the generated monitors. A new technology from SUN Microsystems in the form of [2] is available for that purpose, and will aid as a starting point
for this investigation. Other promising approaches to integrate
monitors with Java code may be based on aspect-oriented technologies, where the monitors are, but aspects of a program. On the other hand, [2] bears the advantage that not even access to the Java program's source code is required for monitoring it.

The Pros and Cons of either solution, as well as other available options, should be determined in this project, and one particularly suitable solution demonstrated in terms of building a small, but fully functional proto-type.

Background Literature

W: LTL3 tools

W: BTrace

Contact:

E: andreas.bauer@nicta.com.au
W: http://rsise.anu.edu.au/~baueran

Solar Thermal Science and Technologies - 2 projects

Show Projects...

Solar Thermal Group

Project Code:ST_01

Supervisor:

Dr John Pye
Dr Keith Lovegrove

Outline:

The Solar Thermal Group at ANU has recently completed construction of a new 500 mÂ² dish concentrator, designed for mass-production, in cooperation with our commercial partner, Wizard Power, a Canberra-based business. There are a huge range of possible areas where a summer student interested in sustainable energy could contribute at this important stage, we'd encourage any interested student to get in contact with us and we can discuss a particular project plan. Both experimental and theoretical projects are possible.

Contact:

E: john.pye@anu.edu.au
E: keith.lovegrove@anu.edu.au

Gas turbine dynamic simulation using ASCEND

Project Code:ST_02

Supervisor:

Dr John Pye

Outline:

The open-source mathematical modelling program, ASCEND, provides a framework for solving a wide range of engineering equations. This project would involve using ASCEND to develop a dynamic model for the small gas turbine currently proposed for installation at the focus of the new ANU SG4 dish concentrator. We would seek to calculate the annual output achievable from this dish/engine, using as sophisticated a model as time permits, and to perform a range of design optimisations based on the system outputs. The model system is likely to incorporate an 'economiser' heat exchanger.
The student would gain understanding of a detailed multi-physics engineering problem with high current relevance and impact, and develop useful skills in process modelling and design.

Contact:

E: john.pye@anu.edu.au

	Skip Navigation InfoEng Home \| CECS Home \| ANU Home \| Search ANU \| HORUS \| Staff Home
	Information Engineering ANU College of Engineering and Computer Science