Vacation Research Scholarships: Information Technology, Engineering and the Environment

These scholarships give you the opportunity to earn $300 a week undertaking research for up to 8 weeks with experienced researchers, usually between November and February, in a recognised research institute or centre within the University.

Applications close 9 September 2018 

How to find a placement

Think about areas in which you would like to research.

You may wish to select an existing project.  View the projects available for 2018/2019 here and contact the person listed for the project(s) of your choice to find out more.

If you can’t find an existing project in your area of interest, or would like to develop your own project, you will need to identify a potential supervisor. You can:

-          contact a school for more information:

-          use the Directory of Research Expertise to identify experts in a given research area.

-          approach academics who are known to you (eg lecturers, course coordinators, program directors)

 Important Information

·         Placement is for a minimum of 4 weeks to a maximum of 8 weeks between November and February each year

·         To accommodate the Christmas/New Year closure of 1.5 weeks, the period of tenure may be taken in two blocks of time, subject to approval from supervisory staff. 

·         The scholarship is expected to be undertaken on a full-time basis (38 hours per week) for the period of the scholarship. Hours/duration of work are to be agreed upon with your supervisor prior to the acceptance and commencement of your project.

·         Students are eligible for the centrally funded Vacation Research Scholarship once only throughout their Undergraduate/Honours degree. However, as additional scholarships may be allocated funding from schools/divisions, applications form previous recipients will be accepted for consideration. Advice should be sought directly from the relevant school/division. Please refer to the project information links for contact details.

·         Your application will require the support of a project supervisor. If you were previously unknown to the researcher, you are encouraged to submit a separate supporting statement from an academic staff member who can comment on your academic abilities.

·         This scholarship is highly competitive based on academic merit and the availability of researchers in your area of interest and unfortunately not all applicants or projects will be funded

 ·         Successful applicants cannot defer the scholarship and must take it up during the time nominated

Projects

Further information

For more information about the scholarships and how to apply click here.

Vacation Research Project Descriptions

Information Technology & Mathematical Sciences

Project Title

Project description

Analysing comments and posts in online news stories

Project keyword(s): Social media data, sentiment analysis, metadata analysis, fake news, fake content

Social media content made by online news consumers is suitable for a range of purposes, including detecting of fake news, propaganda and illicit advertising. Analysis of the content of social media content helps detect when manipulation activities are present, who is doing it and what they aim to achieve. This project will trial suitable methods for detecting fake content, such as sentiment analysis, metadata analysis and text analysis. A number of data sets are available for this analysis, including comment data from the Guardian online, The Australian online, Twitter and the ABC Drum online. (Suitable for PhD, Masters, Honours, and Vacation Scholarship)

Contact: Prof Helen Ashman | E Helen.Ashman@unisa.edu.au
| T 8302 3741 | URL  http://people.unisa.edu.au/Helen.Ashman

Automatic labelling of tweets in civil unrest prediction

Project keyword(s): Computer Science, Data Mining

Twitter data is considered as an important open source when predicting civil unrest events. A number of models have been built with features/patterns extracted from tweets, such as the volume-based model and planned protest model in (Ramakrishnan et al. 2014), and the forward-looking approach to crowd behaviour prediction in (Kallus 2014). However, labelling of tweets still remains a challenging task due to the nature of tweets. In (Zhao et al. 2014), the authors manually labelled 5386 tweets as civil unrest related, and 6147 as unrelated, which required a large amount of labor force. The objective of this project is to design and implement either unsupervised or semi-supervised approaches (Hua et al. 2013), so as to label tweets automatically. (Suitable as PhD, Masters, and Vacation Scholarship project)

Contact: Professor Jiuyong Li, Dr Wei Kang E Jiuyong.Li@unisa.edu.au;

T 8302 3898; URL http://people.unisa.edu.au/Jiuyong.Li

Causal predication methods for evidence based decision making 

Project keyword(s): Computer Science, Data Mining, causal discovery, evidence based decision making

A causal prediction is the forecast of an outcome based on its causes. Evidence based decision making has a long history of using causal predictions. For example, in medical domain, researchers often conduct controlled experiments or observational studies to establish causal relationships and evaluate the effectiveness of treatments as strong evidence for therapeutic decisions, at population level (e.g. for developing practice guideline) or individual level (e.g. for personalised medicine). However, such approaches are hypothesis- driven. Firstly a domain expert presents a hypothesised causal relationship, e.g. between a drug and the recovery of a disease, then after data has been collected from controlled experiments or observations, the hypothesis is tested on the data. In many applications, we have an abundance of data available, but we do not know what causal relationships are hidden in the data. So it is necessary to have data mining tools to automatically explore the data to find causal evidence.  This project aims to develop efficient data-driven causal prediction techniques to obtain the strongest possible evidence for proper decision making and interventions. (Suitable as PhD, Masters, and Vacation Scholarship project)

Contact: Prof Jiuyong Li, Dr Lin Liu E Jiuyong.Li@unisa.edu.au; T 8302 3898; URL http://people.unisa.edu.au/Jiuyong.Li

Comparison of start-of-art anomaly detection algorithms in timeseries data

Project keyword(s): Timeseries, Anomaly detection, Matrix profile, Motif detection

Timeseries data is produced in many applications. For example, in water industries, sensors are used to detect water quality in terms of chemicals and turbidity, water flow rate and pressure in the supply network, and water level in pools and tanks etc. One of the problem in timeseries data is the detection of events. An example of an event is the sudden change in some water quality measurements, e.g., the sudden changes in water turbidity. The consequence of such an event is that the end users will get water that looks muddy or tests strangely. Early detection of such events in water is critical for early warning and adequate processing.

This project aims to implement and compare some newly developed algorithms in the research community for detecting anomalies. The data used in the comparison was collected from industry. The final goal of the project is to use these the implementation of the algorithms to detect events (or measurement changes) in the timeseries data. The algorithms are described in the references. If you work on the project, you will collect the implementation of these algorithms and make them run in our computer, and run on our data, and plot the final results as figures. Students must have software skills. (Suitable as Vacation Scholarship project)

Contact: Dr Jixue Liu | E jixue.liu@unisa.edu.au; T 8302 3054; URL  http://people.unisa.edu.au/jixue.liu

Discovery and use of Twitter network structural features for civil unrest prediction 

Project keyword(s): Computer Science, Data Mining

Social unrest is predicable using Twitter data (Ramakrishnan et al 2014) and the structures of the Twitter networks are strong indicators. Baltimore riots and Arab Spring share many similarities in patterns of spread of messages in Twitter (Bohannon 2015). A recent study shows that there are clear network structure and community changes in Twitter after the 2011 Japanese earthquake and Tsunami (Lu and Brelsford 2014).  Another recent study in PewResearchCenter characterises six types of conversational structures in Twitters: polarized, tight crowd, Brand clusters, Community clusters, broadcast network, and support network (Smith et al 2014). This project will study the methods for extracting structural features in social networks for improving the prediction accuracy of civil unrest. Some related work for characterisation of Twitter networks can be found in (Myers 2014, Myers and Shama, 2014). (Suitable as PhD, Masters, and Vacation Scholarship project)

Contact: Prof Jiuyong Li | E Jiuyong.Li@unisa.edu.au; T 8302 3898; URL http://people.unisa.edu.au/Jiuyong.Li

Efficient causal inference in big data 

Project keyword(s): Computer Science

Causal inference is a fundamental problem in science. The access to big data has opened up new opportunities for inferring causal relationships from purely observational data when experimental tests and interventions are difficult or unethical. Most of existing causal discovery algorithms are designed for a small and single data set. Thus, big data brings great challenges on causal inference because of its volume, the diversity of data types and the speed at which it must be managed. The project will develop efficient and effective causal inference algorithms to deal with big data challenges for advancing big data mining techniques, and extend those new algorithms to discover genetic causes of cancer for improving biomedical discovery. The novel causal inference methods developed in the project will advance data mining techniques and help human beings better understand cause-and-effect relationships hidden in big data. By extending the research outcomes to discover genetic causes of cancer to help biomedical researchers understand critical causes and trends buried in big biomedical data, this will bring great potential to improve biomedical discovery for better healthcare in Australia. (Suitable as PhD, Masters, and Vacation Scholarship project)

Contact: Dr Kui Yu | Prof Jiuyong Li, Dr Lin Liu E Jiuyong.Li@unisa.edu.au; T 8302 3898; URL http://people.unisa.edu.au/Jiuyong.Li

Exploring options for large dataset management for data sharing

Project keyword(s): Advanced database systems, distributed systems, data sharing, big data

Advanced remote sensing devices produce large amount of data. For example, when drones flying over a certain area can produce videos of large sizes. Airplane-born remote sensing  devices can record huge amount of data about geography and plantation of an area. High resolution satellite images also occupy large amount of storage spaces. Data from these devices needs to be collected, stored, retrieved, shared, and analyzed by users from different locations with different needs. For example, some users are interested more in analyzing images to find whether a person is in the images while other users are interested in analyzing videos to find whether an event is related to the videos.

This project aims to explore options for the management and use of such data with the current state of the art technologies. The deliverables of the project are a report and slides that introduce these data management options, the cost for the options, and the advantages and disadvantages to the end users. The method for conducting the project work is mainly to search for information about advanced data management systems for such applications and summarize and compare these systems in different platforms such as cloud and local shared storage etc.

Contact: Dr Jixue Liu | jixue.liu@unisa.edu.au; T 8302 3054; URL http://people.unisa.edu.au/jixue.liu; | Prof Jiuyong Li | E Jiuyong.Li@unisa.edu.au; T 8302 3898; URL http://people.unisa.edu.au/Jiuyong.Li |  

Identifying cancer subtypes from multi-levelled biological data with computational methods 

Project keyword(s): Computer Science, Bioinformatics

Cancer is a leading cause of death, accounting for more than 8.2 million of deaths worldwide, or 22,000 people every day. In the past decade, personalised medicine, using genetic information to develop cancer-specific medication, has become a strong focus for health researchers. An important step in this personalised medicine framework is to identify cancer subtypes, as different cancer subtypes may have different treatment therapies. Since cancer is an extremely complex and heterogeneous disease, the personalised medicine framework relies heavily on achievements of advanced research in system biology (Wang, 2010).  System biology approaches use knowledge in Mathematics, Statistics and Computer Science to solve the biological problems. This project will study the computational methods for identifying cancer subtypes using multi types of biological data. Examples of related works are in (Wang et al. 2014, Liu et al. 2014). Background in Biology is an advantage but not a compulsory requirement. (Suitable as PhD, Masters, and Vacation Scholarship project)

Contact: Dr Thuc Le, Professor Jiuyong Li E Thuc.Le@unisa.edu.au; T 8302 3996; URL http://people.unisa.edu.au/Thuc.Le

Influencing Reddit readers through upvote manipulation

Project keyword(s): Social media manipulation, Reddit, fake news, fake content, vote manipulation

Reddit is one of the world's largest social media sites, organised into subject-based forums called subreddits. It has a content voting system as well as a commenting system that lets users upvote or downvote posts made by other users. We have already shown that it is possible to manipulate the Reddit voting system so that some posts are given the highly-desirable front page status, so they are seen by many more users than usual. However the level of possible manipulation seems to vary, with posts on the AskReddit forum being much more manipulable than posts on The_Donald. This project will extend this work by determining whether there are characteristics of different subreddits that make them easier to manipulate, such as the controls of sentiment and topic, being political or non-political, or perhaps the filter bubble effect. (Suitable for PhD, Masters, Honours, and Vacation Scholarship)

Contact: Dr Siu Wai Ho | Prof Helen Ashman | E Helen.Ashman@unisa.edu.au

| T 8302 3741 | URL  http://people.unisa.edu.au/Helen.Ashman

Integration and visualisation of multiple civil unrest prediction models 

Project keyword(s): Computer Science, Data Mining

A complete system often consists of multiple models/components, which work and interact with each other to provide expected results. The objective of this project is to integrate multiple existing civil unrest prediction models (Ramakrishnan et al 2014) into one system, and make sure all the components work properly together to provide a comprehensive and user-friendly result through user interface and visualisation. (Suitable as PhD, Masters, and Vacation Scholarship project)

Contact: Dr Wei Kang | Prof Jiuyong Li | E Jiuyong.Li@unisa.edu.au; T 8302 3898; URL http://people.unisa.edu.au/Jiuyong.Li

Investigating genetic causes of cancer through complex gene regulatory networks 

Project keyword(s): Computer Science, Bioinformatics

This project will study the computational methods for identifying the genetic causes of cancer through gene regulatory networks containing multiple gene regulators. Gene regulatory networks play an important role in every process of life, and understanding the dynamics of these networks helps reveal the mechanisms of diseases. There have been tremendous works on inferring gene regulatory networks. However, most of the works consider the networks with only one type of gene regulator, such as transcription factors (Imam et al., 2015) or microRNAs (Le, 2013), thus only help reveal part of the whole regulatory network picture. This project aims to develop methods to construct gene regulatory networks that contain multiple types of gene regulators and methods to isolate sub-networks that are altered between normal and cancer patients. Examples of related works are in (Le et al. 2013, Ping et al. 2015). Background in Biology is an advantage but not a compulsory requirement. (Suitable as PhD, Masters, and Vacation Scholarship project)

Contact
: Dr Thuc Le, Professor Jiuyong Li E Thuc.Le@unisa.edu.au; T 8302 3996; URL http://people.unisa.edu.au/Thuc.Le

Deep neural networks for human emotion recognition 

Project keyword(s): Computer Science, Wearable Computing, Teleconferencing

Newly developed "deep learning" methods have reignited the field of neural networks in the last few years. For example, Google DeepMind recently announced the first computer program that plays the game of Go at human expert level, and this relied on deep learning.

The aim of this project is to design software that learns to robustly recognise human emotions, by making use of multiple types of sensor data, such as video, still images, and biometrics. It is expected that the main algorithm to be implemented will be a deep convolutional neural network. It will be based on the earlier work of Yu and Zhang [1] who have been able to get emotion recognition rates of up to 85% with a neural network technique.

The context of use will be explore if emotional recognition code can be developed that can run in near real time on live camera video and so provide feedback on user emotion while operating a computer interface. For example, using the video feel from a laptop camera to monitor the emotions of a person in front of it. (Suitable for Vacation Scholarship)

Contact: Professor Mark Billinghurst | E Mark.Billinghurst@unisa.edu.au | T 8302 3747 | URL http://people.unisa.edu.au/Mark.Billinghurst

Face to face collaboration using HoloLens 

Project keyword(s): Computer Science, Wearable Computing, Teleconferencing

The Microsoft HoloLens hardware combines a see-through head mounted display with excellent indoor tracking, and so provides an ideal platform for Augmented Reality. In this project we want to explore how the HoloLens could be used to enhance face-to-face collaboration.

The project will involve developing an example HoloLens application that will allow two people in the same room to view and interact with the same virtual content. This will build on earlier work that we have done in face-to-face AR interaction [1][2]. In addition we will explore novel interaction methods such as using virtual cues to show where people are looking, and enabling users to see from each other viewpoints. (Suitable for Vacation Scholarship)

Contact: Professor Mark Billinghurst | E Mark.Billinghurst@unisa.edu.au | T 8302 3747 | URL http://people.unisa.edu.au/Mark.Billinghurst

Gaze based remote conferencing 

Project keyword(s): Computer Science, Wearable Computing, Teleconferencing

For a number of years people have been studying how head worn cameras (HWCs) and head mounted displays (HMDs) can be used for remote collaboration on physical tasks. The HWC allows a remote expert to see what the local user is doing, while a HMD can allow the remote expert to provide Augmented Reality (AR) virtual cues overlaid on the local user’s view of the real world to help them complete the task. For example, in a remote maintenance task, workers using a wearable AR interface were able to reduce their task performance time by up to 30% [1].

In face-to-face conversation gaze provides information about where a person is directing his or her attention and so it could also be an important cue in remote collaboration. Previous research has found that sharing gaze between two remote collaborators significantly improved performance on a desktop visual search task, compared to audio only communication [2]. However there has been little research conducted on sharing gaze cues from a wearable collaborative system. In this project we want to explore the effect of adding gaze tracking to wearable systems for remote collaboration. 

The work would extend our earlier pilot work in this area [3] and involve the following: Background research on gaze tracking in collaborative systems, Create a prototype system integrating a HMD, HMC and eye-tracker, Conduct user studies with a variety of physical tasks, and Write research report. (Suitable for Vacation Scholarship)

Contact: Professor Mark Billinghurst | E Mark.Billinghurst@unisa.edu.au | T 8302 3747 | URL http://people.unisa.edu.au/Mark.Billinghurst

An Industry Standard Unstructured information management Implementation

Project keyword(s): Software Engineering, Natural Language Processing

Modern text analysis applications require many different tools (existing and novel) to work together across multiple platforms, programming languages, and architectures. These tools must work in parallel or sequential pipelines that can be configured and customised by the application developer. Therefore, standardised execution architectures, such as the UIMA (Unstructured Information Management Architecture) standard released by OASIS, are a necessity for ensuring the interoperability and consistent execution of different tools combined in an application. To this end, we are looking for a student to develop a standards compliant implementation of UIMA. Ability and willingness to learn at least one new programming language is expected.  The project is associated with multiple industry/defence application scenarios.  (Suitable for Vacation Scholarship)

Contact: Professor Markus Stumptner, Dr Matt Selway | E Markus.Stumptner@unisa.edu.au | T 8302 3965 | URL http://people.unisa.edu.au/Markus.Stumptner

Automated Transformation of Supply Chain Data

Project keyword(s): Automated Software Engineering, Data Integration, Industry 4.0

The digitalisation of industrial supply chains is moving rapidly towards providing end-to-end transparency for large transport customers.  Dedicated data models are being developed for data exchange along the supply chain, e.g., from trucking companies to customers, along the lines of data models already existing in the air traffic management domain.  The project will examine the development of particular data model adapters and business rules for supply chain interactions.  The project is associated with a major industrial supply chain customer company.  (Suitable for Vacation Scholarship)

 Contact: Professor Markus Stumptner, Dr Georg Grossmann | E Markus.Stumptner@unisa.edu.au | T 8302 3965 | URL http://people.unisa.edu.au/Markus.Stumptner

Beyond Google search: Learning semantic relations

Project keyword(s): Knowledge Acquisition, Machine Learning

A common technique in Natural Language Processing, popularised by Google’s word2vec, is to analyse a corpus of text to produce co-occurrence vectors for each word (basically the vectors contain one dimension per word pair), which enables us to determine the similarity between two words. Such vector models, or distributional semantics, demonstrates some neat properties: for example, subtracting the vector for 'man' from the vector for 'king' and adding the vector for 'woman' results in a vector very close to that of 'queen'. However, such models are limited, finding most use in indexing and information retrieval (such as Google's search engine), tend to focus on nouns, and are not truly semantic. For example, two word vectors may be close together, not because they are similar concepts, but because they are strongly related through some (unknown) relationship. These relationships are often identifiable by the verbs and prepositions used between them. This project aims to be a starting point for inferring semantic relations between word co-occurrence vectors by incorporating the co-occurrence information of prepositions and verbs. The candidate will develop an initial prototype that will identify basic semantic relations (such as part-whole relations, and more general/more specific terms) from key prepositions as well as a small set of domain specific relations of interest.) The project is associated with multiple potential applications in defence and industry. (Suitable for PhD, Masters, and Vacation Scholarship)

Contact: Professor Markus Stumptner, Dr Matt Selway | E Markus.Stumptner@unisa.edu.au | T 8302 3965 | URL http://people.unisa.edu.au/Markus.Stumptner

Comparative evaluation of natural language parsers

Project Keyword(s): Natural Language Processing, Software Engineering, Evaluation

Recently, Google has open-sourced their Natural Language Parser and many research groups promoted or released their own in response, providing a slew of state-of-the-art parsers for academics and organisations to use in their work. At the same time, there are many commonly used parsers, such as the Stanford Parser, that are widely considered to provide good results.

This project will produce a comparative evaluation of the currently available Natural Language Parsers, including both older parsers and those newly released. The project includes the design and execution of a systematic evaluation performed on common test corpora and will form the basis for the comparison of the Natural Language Understanding engine being developed within the Knowledge and Software Engineering Laboratory. The project is associated with current collaboration grants with Defence Science & Technology and international interoperability projects in the chemical industry, air and ground transport management.  (Suitable for PhD, Masters, and Vacation Scholarship). 

Contact: Professor Markus Stumptner, Dr Matt Selway | E Markus.Stumptner@unisa.edu.au | T 8302 3965 | URL http://people.unisa.edu.au/Markus.Stumptner 

Inferring semantic relations from word co-occurrence vectors 

Project Keyword(s): Knowledge Acquisition, Machine Learning

A common technique in Natural Language Processing, popularised by Google’s word2vec, is to analyse a corpus of text to produce co-occurrence vectors for each word (basically the vectors contain one dimension per word pair), which enables us to determine the similarity between two words. Such vector models, or distributional semantics, demonstrates some neat properties: for example, subtracting the vector for 'man' from the vector for 'king' and adding the vector for 'woman' results in a vector very close to that of 'queen'. However, such models are limited, finding most use in indexing and information retrieval (such as Google's search engine), tend to focus on nouns, and are not truly semantic. For example, two word vectors may be close together, not because they are similar concepts, but because they are strongly related through some (unknown) relationship. These relationships are often identifiable by the verbs and prepositions used between them. This project aims to be a starting point for inferring semantic relations between word co-occurrence vectors by incorporating the co-occurrence information of prepositions and verbs. The candidate will develop an initial prototype that will identify basic semantic relations (such as parthood, and more general/more specific terms) from key prepositions as well as a small set of domain specific relations of interest. (Suitable for PhD, Masters, and Vacation Scholarship)

Contact: Professor Markus Stumptner, Dr Matt Selway | E Markus.Stumptner@unisa.edu.au | T 8302 3965 | URL http://people.unisa.edu.au/Markus.Stumptner

The Cooking Simulator: A lightweight testbed for serious matters

Project Keyword(s): Behaviour Representation, Modelling, Simulation, Visualisation

The Knowledge and Software Engineering Laboratory, in conjunction with Defence Science & Technology, has developed a behaviour representation language for use with combat simulations. We invite students to participate in the development of a lightweight simulation engine and demonstration platform in which behaviours can be tested rapidly before being deployed to target simulation engines. Cooking recipes will be used as example simulations. The project will comprise: development of an event-driven simulation engine; a web-based front-end for visualising the cooking processes; and the modelling of a small set of recipes for demonstrating the simulation capability. Focus and scope of the project can be shaped according to the student’s interests and level of participation (Honours/Masters, Vacation Scholarship). Moreover, there is opportunity to contribute to research papers.

(Suitable for Honours/Masters, Vacation Scholarship.)

This work is aligned with the ‘Doctrine to Code’ project funded by Australian Defence Science and Technology (DST).

Contact: Professor Markus Stumptner, Dr Matt Selway | Dr Georg Grossmann | Dr Wolfgang Mayer |E Markus.Stumptner@unisa.edu.au | T 8302 3965 | URL http://people.unisa.edu.au/Markus.Stumptner

Cybersecurity policy

Project Keyword(s): Cybersecurity, Information Systems

Cybersecurity has become an increasingly important topic, not only for IT professionals, but for almost all organisations that utilise IT systems. In this project, students will explore a contemporary aspect of cybersecurity policy for a mutually agreed upon industry sector/client. For example, students may choose to investigate the practicality of a particular industry sector hosting its corporate data using a public cloud service from privacy, security and data sovereignty perspectives. (Suitable as PhD, Masters, Honours, and Vacation Scholarship project)

Contact: Dr Ben Martini, Dr Gaye Deegan| E Ben.Martini@unisa.edu.au; T 8302 5688; URL http://people.unisa.edu.au/Ben.Martini

Vulnerable communities in the digital age

Project Keyword(s): Information systems, technology use, information behaviour, vulnerable communities

Digital technologies have a radical impact on vulnerable people's everyday lives including searching for and using valuable information. Questions of equality and social justice are as important as ever in the information age. There have been concerns about issues around bias, social exclusion, misinformation and information sharing hazards. Vulnerable people typically include women and children, ethnic people of colour, immigrants, gay men and lesbians [currently people from LBGTQI populations], the homeless, and the elderly (Du et al., 2017). Understanding information behaviours of vulnerable communities and its evolution in the digital age is a critical area of research and practice within the field of information science and technology which provides an excellent venue to discuss this crucial topic at the intersection of information, society, and technology. This project focuses on the design and use of social technologies for/with vulnerable populations, associated ethical issues, and creative uses of new technologies for social inclusion.  (Suitable as PhD, Masters, and Vacation Scholarship project)

Contact: Dr Tina Du | E Tina.Du@unisa.edu.au; T 8302 5269; URL http://people.unisa.edu.au/Tina.Du

AR/VR Sports Visualisation

 

Project Keyword(s): Augmented Reality, Visualization, Athlete Skill Learning and Training.

This project will investigate the use of augmented and virtual reality systems to support athletes and recreational sports participants in skill learning and training.  AR may be employed to supplement training by providing a mechanism to visualize set plays, visualize body movements/position, and visualize game performance [1,2].  The affordances of AR technology equips an athlete with the ability practice both physically and mentally anywhere and at any time, e.g. while at home, going for a walk or on the field.  Visualisations may also be used by coaching staff to assess player performance data.  Research has shown that mental rehearsal or visualisation can improve athlete performance.  This project will explore new methods of AR for visualization with the aim of enhancing sports performance, i.e. AR as a support to mental rehearsal.  This project may also explore AR as a support to movement/skill learning (i.e. kicking a football) [3,4].  The project will explore how Augmented reality systems can be employed to develop athlete training applications.  This project will develop a prototype that will allow an athlete to visualise plays (i.e. in miniature or real size); or to provide visual representation of a movement, i.e. practise kicking a football, etc.

This project is well suited for anyone interested in learning or has experience with the Unity Game Engine; an interest in sports or visualization methods. You will be part of an enthusiastic group of talented developers with diverse experience in a very collaborative and supportive group. (Suitable as Vacation Scholarship project)

Contact: Dr Jo Zucco and Dr Ross Smith E Jo.Zucco@unisa.edu.au; T 8302 3447; URL http://people.unisa.edu.au/Jo.Zucco; E Ross.T.Smith@unisa.edu.au; T 8302 5551; URL http://people.unisa.edu.au/ross.t.smith

AR/VR Virtual Pet Therapy (Saab collaboration)

Project Keyword(s): Augmented Reality, Visualization, Interaction, Animal-assisted Therapy, Health Sciences.

This project will explore the use of augmented reality technology to provide a virtual pet in place of a real pet for animal-assisted therapy.  Animal-assisted therapy provides interaction with an animal in order to improve human health and well-being.  The benefits of animal-assisted therapy are well known and are employed from the young to the elderly [1].  Animal-assisted therapy has been explored in many areas; stress [2], trauma [3], depression [4], illness (both patients and caregivers) [5].  In many cases, it may not be possible for those that would may benefit from animal-assisted therapy to interact with a real pet.  In addition, interaction with real animals are limited to certain days and times.  A virtual pet, on the other hand, is readily available as an intervention when required.  This research aims to explore whether an AR virtual pet provides the same benefits over traditional approaches (i.e. a real pet).  This project will develop a virtual pet viewed by using either a head-worn display or a handheld system (such as a mobile phone).  The system should provide realistic behaviours and meaningful interactions with the virtual pet.  Suitable interaction techniques may also be explored as a part of the project.

This project is well suited for anyone interested in learning or has experience with the Unity Game Engine; an interest in health and well-being or visualization and interaction methods. You will be part of an enthusiastic group of talented developers with diverse experience in a very collaborative and supportive group. (Suitable as Vacation Scholarship project)

Contact: Dr Jo Zucco and Dr Ross Smith E Jo.Zucco@unisa.edu.au; T 8302 3447; URL http://people.unisa.edu.au/Jo.Zucco; E Ross.T.Smith@unisa.edu.au; T 8302 5551; URL http://people.unisa.edu.au/ross.t.smith

Augmented Reality Tool to Support Student Learning of Arduino Microcontrollers

Project Keyword(s): Augmented Reality, Microcontrollers, Teaching and Learning.

This project will explore the use of augmented reality technology in education.  Augmented reality enhances the user's real world with virtual information.  Augmented reality has been explored in many educational settings (e.g. anatomy, chemistry, maintenance, assembly tasks [1,2,3]), and has been shown to positively influence and improve the learning process [4,5,6].  The project aims to leverage the capabilities of AR technology in an educational setting providing engaging, authentic and active learning experiences.  The research aims to explore whether an AR system improves learning over traditional approaches (e.g. paper based instruction, etc).  This project will develop an application that will allow a user to view the Arduino microcontroller using either a head-worn display or a handheld system (such as a mobile phone) and see the device with virtual information superimposed.  The system should provide information pertaining to the Arduino, how to connect it to other components (i.e. breadboards, LEDs, sensors, buzzers, etc) and meaningful interactions with virtual information. Through AR it will also reveal invisible elements such as electron flow direction, current and voltage information and allow manual annotations to be added to support the learning process.

This project is well suited for anyone interested in learning or has experience with the Unity Game Engine; an interest in teaching and learning or visualization methods. You will be part of an enthusiastic group of talented developers with diverse experience in a very collaborative and supportive group. (Suitable as Vacation Scholarship project)

Contact: Dr Jo Zucco and Dr Ross Smith E Jo.Zucco@unisa.edu.au; T 8302 3447; URL http://people.unisa.edu.au/Jo.Zucco; E Ross.T.Smith@unisa.edu.au; T 8302 5551; URL http://people.unisa.edu.au/ross.t.smith

Develop HoloLens Stroop Task Experiment Application 

Project Keyword(s): Computer Science, Augmented Reality, ACRC, Wearable Computer Lab

In psychology, the Stroop effect is a demonstration of interference in the reaction time of a task. When the name of a color (e.g., "blue", "green", or "red") is printed in a color which is not denoted by the name (i.e., the word "red" printed in blue ink instead of red ink), naming the color of the word takes longer and is more prone to errors than when the color of the ink matches the name of the color. Naming the font color of a printed word is an easier and quicker task if word meaning and font color are not incongruent. If both are printed in red, the average time to say "RED" in response to the word 'Green' is greater than the time to say "RED" in response to the word 'Mouse'.

This project is to develop an Unity3D application that operates on a HoloLens optical see through head mounted display that performs the Stroop Task. The goal is to determine if there is a difference between the Stoop Task presented on HoloLens and on a traditional monitor. A research publication will be written after a formal user study is performed. The user study will run after the summer internship. The student is welcomed to help write the paper and help perform the user study. (Suitable as Vacation Scholarship Project)

Contact: Professor Bruce Thomas | E Bruce Thomas@unisa.edu.au |T 8302 3464; URL http://people.unisa.edu.au/Bruce.Thomas

Mixed Reality Physio-therapy Assistant 

Project Keyword(s): Augmented Reality, Visualization, Rehabilitation, Gamification, Health Sciences.

This project will explore the possibility of using a mixed reality application to help aid the recovery process after physical injury [1,2]. It will explore how gamification can be incorporated into exercise routines to make the experience of recovery more enjoyable. The system is intended to support exercises for areas such as the neck, shoulder, back, knees and other areas of the body. We aim to use features such as tracking the progress made during the therapy session to inform both the patient and clinicians (such as physiotherapists) of progress and provide analysis tools [3] to evaluate the data. The system will track movements and record information such as angle for how far patients can rotate their head. It will also, display angle data when a user finishes their input, and aims to visualize data in an entertaining way.

This project is well suited for anyone interested in learning or has experience with the Unity Game Engine; an interest in gamification, rehabilitation or visualization methods. You will be part of an enthusiastic group of talented developers with diverse experience in a very collaborative and supportive group. (Suitable as Vacation Scholarship project)

Contact: Dr Jo Zucco and Dr Ross Smith E Jo.Zucco@unisa.edu.au; T 8302 3447; URL http://people.unisa.edu.au/Jo.Zucco; E Ross.T.Smith@unisa.edu.au; T 8302 5551; URL http://people.unisa.edu.au/ross.t.smith

Spatial Augmented Reality Experiments for Understanding How This Technology Improves Users Performance 

Project Keyword(s): Augmented Reality, Unity3D, User Studies

Augmented Reality is gaining acceptance within the general public. We do not understand how AR improves peoples lives. Spatial Augmented Reality is process of projecting digital information directly onto physical objects. We have been investigating how Spatial Augmented Reality usability compares with other forms of presenting Augment Reality, such head mounted displays. This project will be developing a set of Unity3D applications that are appropriate with Spatial Augmented Reality. One initial experiment to be developed is to change the user’s field of view (how much Spatial Augmented Reality) information is presented to the user.  The hypothesis is a smaller field of view would reduce the user’s ability to perform particular tasks. . A research publication will be written after a formal user study is performed. The user study will run after the summer internship. The student is welcomed to help write the paper and help perform the user study. (Suitable as Vacation Scholarship Project)

Contact: Professor Bruce Thomas | E Bruce Thomas@unisa.edu.au |T 8302 3464; URL http://people.unisa.edu.au/Bruce.Thomas

Telling Immersive Data Stories: Exploring Narrative Visualisation Techniques in Virtual Reality 

Project Keyword(s): Infographics, Virtual Reality, Vive Headsets

For over thirty years, computer-aided data visualisation has been a powerful tool in the data scientist’s toolbox to externalise thought processes and help build an understanding of real-world phenomena. Techniques like scatterplots, sparklines, and even the humble barchart, have been used to gain insight into social and world issues like poverty and global warming to great success. Communicating these finding, however, has often been a challenge in itself. These visualisations commonly require some level of interpretation and explanation — the insights must be communicated. To the layman, these visualisations can be inscrutable and do not translate into actionable knowledge.

Infographics — data stories using leveraging visualisation and narrative techniques — has been a recent solution to the dilemma of communicating data. Mastered by the likes of the New York Times, infographics, and more broadly “Narrative Visualisation”, have been successful in communicating complex insights into subjects as diverse as politics, crime, and even Netflix, to millions of readers around the world. More recently, these Narrative Visualisations have moved to the web to introduce elements of interactivity to aid the reader in internalising the underlying insights.

Virtual Reality (VR) has seen a recent surge in popularity with the introduction of the Oculus and HTC Vive headsets. The fidelity and level of immersion provided by these modern headsets is unprecedented, so much so that data visualisation researchers are beginning to explore the potential of the medium in a new research field known as Immersive Analytics. It then begs the question, what is Narrative Visualisation in the world of Virtual Reality?

This project will develop new techniques for navigating and interacting with data stories within virtual reality environments. The researcher will investigate modern techniques for storytelling used in VR and build a set of VR data stories that explore the influence that various methods of interaction and feedback (such as sound, walking and haptics) have on the delivery of the narrative.

Contact: Dr Andrew Cunningham | E: andrew.cunningham@unisa.edu.au; T: 8302 3081; URL: http://http://people.unisa.edu.au/Andrew.Cunningham

Adaptive Forecasting Methods Applied to Solar Farm Output 

Project Keyword(s): Recursive Least Squares, Adaptive Forecasting, Solar Farms

Boland (2008) describes the rigorous development of a short term solar forecasting tool based on using Fourier series to describe the climate for a location, and then an autoregressive (AR) process for the short term weather forecasting segment.  Let’s name this approach FSAR.  It was used as the tool for the 5 minute to one hour time scale for a Australian Solar Energy Forecasting System research grant.  It has also been the basis for work such as that of Grantham et al. (2016) for the creation of error bounds around short term forecasts, as well as in other endeavours.  In various publications, its performance has been successfully compared to other tools in the literature, including those using neural networks, wavelets and combinations of the two.  Most telling is the comparison given in Boland et al. (2016), which principally focuses on island versus continental locations.  However, another part of the work compared FSAR with the use of neural network forecast of clear sky index, and separately AR coupled with clear sky index. It should be noted that the neural network segment was performed in a rigorous setup for selecting the primary inputs.  The FSAR approach performed at least as well and usually better for the separate locations, and more importantly reflects the physical nature of the influences on the solar resource.

The FSAR approach will be used for various time scales for this project.  However, there are some significant differences between forecasting the solar energy compared with forecasting solar farm output.  Analysis performed on data from the Broken Hill solar farm shows that for clear days, in both winter and summer, the farm will reach maximum capacity, and sometimes remain at that level for a number of hours.  One can use a heuristic to do something like, if the output is at capacity for two time steps consecutively, then forecast that output for the next step.  There may well be a more sophisticated way to deal with this phenomenon.  Bacher et al. (2009) use recursive least square regression to create an adaptive AR model for solar forecasting.  The autoregressive coefficients are altered as time progresses to reflect recent past conditions, not by using a moving window for estimation, but using the recursive least squares.  They make use of the tool for forecasting the clear sky index.  Preliminary tests have been performed using this tool for the series with the seasonality removed using Fourier series with encouraging results.  This approach has the potential to both improve the overall forecasting performance of FSAR, already performing at least as well as any tool, and take care of the problem of the solar farm staying at capacity.  The project will entail preparatory development and testing of the enhancements of FSAR. (Suitable as Vacation Scholarship project)

Contact: Professor John Boland | E John.Boland@unisa.edu.au; T 8302 3464; URL http://people.unisa.edu.au/John.Boland

Forward and inverse electromagnetic scattering 

Project Keyword(s): Applied Mathematics

When a radio wave meets a dense body, the irregular density of the body contributes to scatter the radio wave in different directions. In forward scattering we try to predict the wave spread from our knowledge of the body density. In inverse scattering we try to discover the body density from information about the wave spread. The work involves knowledge of differential equations. (Suitable for summer or semester-long research project)

Contact: Dr Jorge Aarao | E Jorge.Aarao@unisa.edu.au | T 8302 3741 | URL  http://people.unisa.edu.au/Jorge.Aarao

Singular perturbations in differential equations 

Project Keyword(s): Applied Mathematics

The goal is to study the behaviour of solutions to a differential equation, when the equation itself depends on a parameter. The case of interest is when the parameter vanishes, and makes the higher derivatives disappear from the equation. Many examples are known, but not a full general theory. (Suitable for summer or semester-long research project)

Contact: Dr Jorge Aarao | E Jorge.Aarao@unisa.edu.au | T 8302 3741 | URL  http://people.unisa.edu.au/Jorge.Aarao

Natural and Built Environments 

Project title

Project description

Redesign Mawson Lakes Campus Smart Traffic Management Plan

Project keyword(s): Campus transport plan, smart signage, safety, transport convenience

The UniSA Mawson Lakes campus is located in the City of Salisbury and is bordered by Mawson Lakes Boulevard, Main Street and Elder Smith Road. Endeavour College is located at the south and school children between years 7 and 12 are regularly in the vicinity during school term. The road infrastructure for the campus is classed as a private road. The primary link through the campus is University Boulevard and connects a number of car parks around the campus whilst also providing access to Endeavour College. There are a number of smaller access roads through the campus where there is frequent interaction between vehicle and pedestrian traffic.

Vehicle speed is controlled by three differing speed limits within the campus – 40km/h, 25km/h and 10km/h. There are also a number of one way streets and eleven zebra crossing locations with inconsistent line marking and warning signage. A school zone is located on University Boulevard along the eastern boundary of Endeavour College to improve safety for school students whom utilise the adjacent oval for both educational and recreational use daily. Adelaide Metro bus services utilise the road infrastructure and specially requested (already existent) a lay off area on University Blvd east of the Endeavour College boundary.

The purpose of this project is to review the current traffic management conditions, identify improvements and provide key recommendations to achieve compliance with local DPTI regulations and Australian Standards, leading to the compliance of the road network.  

Areas of focus (Outcome is the creation of a compliant Traffic Management Plan), these can use software ‘staffhub’ to assist:

Review and provide recommendations for speed limits. Currently deemed to be inconsistent

Review and provide recommendations around shared zones and street parking. Area of non-compliance.

Review and provide recommendations around the compliance of current road signage and road markings including pedestrian crossings and improvements to current signage.

Provision of map and table detailing locations, current issues and recommendations to make compliant.

Provide recommendations around the creation of a safer path of pedestrian travel on the corner of university Blvd. and Minerals Lane in response to near misses.

Opportunities for the creation of more street parking on campus

Campus smart signage design to apply smart sensors to monitor parking and providing a quick way of accessing car parks

Economic analysis on the cost of smart signage hardware and software system, road signage change material and labor

The project can also lead or expand to a wider scope (if there are more students) considering the following:

shared mobility—instead of using Hughes cars, investigating sustainable travel for renting or shared cars from GoGet or Holden Maven gig, shared bikes (ofo bike, or obike) and on demand shuttle buses linking ML campus to Mawson lakes Interchange, and other UniSA campuses.  

Shared mobility economy analyses -  what money can be saved or spent (using Stella Model or others, as discussed previously between Paul Reynolds and Li Meng)

(For work on these extended areas: student supervisors can be transport supervisors: Dr Li Meng, Dr Sekhar Somenahalli, Dr Mark Ellis, Economic analyses supervisors: Dr Li Meng, Dr James Wards and Dr Xin Deng, and facility advisors: Mr Tim Golding, Mr Justin Faggotter, and Mr Paul Reynolds)

Contact: Dr Li Meng | E li.meng@unisa.edu.au
| T 8302 3223 | URL  http://people.unisa.edu.au/Li.Meng
Mr Tim Golding | E tim.golding@unisa.edu.au
| T 8302 5120 | URL  http://people.unisa.edu.au/Tim.Golding
Mr Justin Faggotter E justin.faggotter@unisa.edu.au
| T 8302 3347 | URL  http://people.unisa.edu.au/Justin.Faggotter

Innovative Method of Utilising Hydrogen Peroxide for Source Water Management of Blue-green algae

Project keyword(s): Water Management, Algal bloom control, Environmental Science and Analytical chemistry

Cyanobacteria (blue green algae) is an ongoing problem in water reservoirs, wastewater systems, and recreational water in South Australia. SA Water is currently developing a new innovative control method based on applying hydrogen peroxide. The use of hydrogen peroxide has a lot of advantages over the existing method of using copper sulphate algaecide. The aim of this research is to investigate the impact of different water chemistries on hydrogen peroxide decomposition and identifies the most efficient dose range in maintaining residuals to control algal blooms. The student will develop the project together with the SA Water team and also spending time at the SA Water laboratory and possible some field works with the objective to implement and provide an engineering solution to manage the blue-green algae issue

Contact: Dr Leslie Huang | E leslie.huang@unisa.edu.au;
| T 0451 595 426 | URL  http://people.unisa.edu.au/Leslie.huang

Dr Peter Hobson E Peter.Hobson@sawater.com.au 

The first record of subduction in the ancient Earth

Project keyword(s): Subduction, early Earth, Tanzania, metamorphism, high pressure

Subduction is a well-known process that recycles oceanic crust into the mantle. While the behaviour of subduction systems is well understood, what is poorly known is when modern-style subduction began on Earth. The record of this process is encoded in the formation of low geothermal gradient but high pressure rocks. These rocks seem to have emerged in the geological record only in the last 2 billion years, or put another way, in the most recent 40% of Earth’s history. Our research group has collected a suite of samples from central Tanzania that at face value represent the oldest products of modern-style subduction ever found. The aim of this project is to undertake thermodynamic modelling of the mineral assemblages in these ancient and unique rocks, to determine their thermal gradients and rates of exhumation, thereby constraining the thermal regimes that existed in probable early subduction systems.

Contact: Associate Professor Tom Raimondo | E Tom.Raimondo@unisa.edu.au
| T 0451 595 426 | URL  http://people.unisa.edu.au/Tom.Raimondo
Dr Laura Morrissey | E Laura.Morrissey@unisa.edu.au | T 0451 595 426 | URL  http://people.unisa.edu.au/Laura.Morrissey

Innovative treatment to reuse drinking water treatment sludge in concrete paver

Project keyword(s): Circular Economy, Materials Science, Concrete, Waste Disposal, Environmental Management.   

In the conventional coagulation-filtration treatment process, suspended solids and natural organic matter are removed from the raw water supply by the addition of aluminium and iron salts as coagulants, resulting in the production of water treatment residuals (WTR) or sludge. It has been estimated that SA Water generates 10,000 to 15,000 tons of sludge annually and the generation of sludge will increase with the rapid growth of population. The main solid components in sludge are irregular sized dust-like sand (~70% with size below 2mm) and stone particles (~10% with size of 9.5mm or above), and the main chemical components include aluminate (~10% by weight) mixed with or adhered to the sand and stone. The particle size and chemical composition analysis of the sludge suggested possible beneficial use of sludge to partially replace soil, sand and coarse aggregate (stone) in making concrete paver. The project aims to develop sustainable high-performance concrete pavers through recycling of drinking water sludge.  This is an innovative idea as no previous research was found in the literature and the proposed project will provide dual benefits to both water treatment and concrete construction industry.  If successfully implemented, this new type of concrete paver will have many advantages over normal concrete paver, such as higher strength, superior durability and superior corrosion resistance due to high content of aluminium in sludge. This project enable the student to gain experience with two different industries, particularly as a team member of this multidisciplinary research team.

Contact: Professor Yan Zhuge | E yan.zhuge@unisa.edu.au 
| T 8302 3093 | URL  http://people.unisa.edu.au/yan.zhuge

Dr Danda Li | E danda.li@unisa.edu.au | Dr Chris Chow | E chris.chow@unisa.edu.au

Asset Maintenance Data Platform: A case study to utilise multiple operating and maintenance data in asset management decision support

Project keyword(s): Asset Maintenance, Asset Management, Data Quality Assurance, Water Industry.   

Water utilities are generally asset intensive organisations and as such needs to ensure that the management of assets is carried out efficiently and effectively to deliver the required service levels at an affordable (lowest possible) price for the customer. Good business decision requires the support of data but often the first step in this process is to obtain good quality data, including a data quality assurance step, before the analyst can perform data analytics task for various purposes. Data management is becoming more important as we are producing larger and more complex data than ever before. There is a large volume of data being collected as part of the asset management process via monitoring, measurement, analysis and evaluation, to support decision making, however, part of this data can go into the situation of commonly described as ‘Data rich and information poor’ or at least not fully utilised. The project will provide mechanisms to identify high value data from the range of data being generated and collected. This collection of data is then processed by various data analytic / quality assurance techniques and create linkages between disparate sources of high value data to enable access to the data via a single portal. It is a step towards proactive in preventative maintenance. The student is required to party based in industry and this will enable the student to gain experience with industry, particularly as a team member of this multidisciplinary research team.

Contact: Dr Danda Li | E danda.li@unisa.edu.au  
| T 8302 3761 | URL  http://people.unisa.edu.au/danda.li

Asset Maintenance Data Platform: A case study to utilise multiple operating and maintenance data in asset management decision support.

Project keyword(s): Asset Maintenance, Asset Management, Data Quality Assurance, Water Industry.   

Water utilities are generally asset intensive organisations and as such needs to ensure that the management of assets is carried out efficiently and effectively to deliver the required service levels at an affordable (lowest possible) price for the customer. Good business decision requires the support of data but often the first step in this process is to obtain good quality data, including a data quality assurance step, before the analyst can perform data analytics task for various purposes. Data management is becoming more important as we are producing larger and more complex data than ever before. There is a large volume of data being collected as part of the asset management process via monitoring, measurement, analysis and evaluation, to support decision making, however, part of this data can go into the situation of commonly described as ‘Data rich and information poor’ or at least not fully utilised. The project will provide mechanisms to identify high value data from the range of data being generated and collected. This collection of data is then processed by various data analytic / quality assurance techniques and create linkages between disparate sources of high value data to enable access to the data via a single portal. It is a step towards proactive in preventative maintenance. The student is required to party based in industry and this will enable the student to gain experience with industry, particularly as a team member of this multidisciplinary research team.

Contact: Dr Danda Li | E danda.li@unisa.edu.au  
| T 8302 3761 | URL  http://people.unisa.edu.au/danda.li

Engineering

Project Title

Project description

Development of a modular viewer for HDR and esoteric images

Project keyword(s): Software development, High dynamic range images

High Dynamic Range (HDR) imaging is the process of capturing scenes with greater contrast than traditional cameras allow, thereby retaining details in the shadows and overcoming saturation in bright areas. Although it is a field still within its relative infancy it has great potential in robotics and surveillance applications. Whereas the capture, creation, and compression of HDR images is receiving significant attention from the research community, the storage and visualisation of these images is lagging behind. This has created an ecosystem with a multitude of incompatible image formats, many often being created by individual research groups for internal use. This makes sharing data increasingly difficult. Software solutions do exist for viewing HDR images, however they are either expensive, closed-source, or non-expandable, generally only being compatible with older formats. In this project a student would develop a HDR image viewer, working alongside researchers within the field. The developed solution would need to incorporate the viewing and remapping of HDR images on traditional displays, be modular (enabling easy implementation of additional formats and compressors), and provide a consumer grade GUI, enabling non-researchers to use the software.

Contact: Dr Daniel Griffiths T 8302 5664 | E daniel.griffiths@unisa.edu.au URL | http://people.unisa.edu.au/Daniel.Griffiths

High Dynamic Range Video dataset for Autonomous Vehicles

Project keyword(s): autonomous vehicles, visual navigation

Autonomous vehicles are a cutting-edge implementation of mechatronic technologies. However, whereas we primarily rely on our vision to drive, current self-driving cars are largely reliant of LIDAR and sonar based solutions. This creates issues when the vast majority of our roadways are designed around visual feedback: such as road markings, traffic signs, traffic lights, and even other vehicles. Traditional cameras lack the dynamic range required to operate on unconstrained roads. They can lose information in high contract environments and have difficulty detecting light objects against a bright sky or dark objects in shadows, difficulties that can have dire consequences. On the other hand High Dynamic Range (HDR) imaging techniques greatly exceed the limits of traditional cameras and can meet---if not exceed---human vision when applied correctly. Computer generated HDR driving datasets exist, but there is a lack of real-world HDR driving data. A student on this project would work alongside university staff capturing real-world HDR video from a vehicle using research grade cameras, cataloguing the data, and publishing the dataset to the wider academic community. This work will contribute to making safe autonomous vehicles a reality.

Contact: Dr Daniel Griffiths T 8302 5664 | E daniel.griffiths@unisa.edu.au URL | http://people.unisa.edu.au/Daniel.Griffiths

Upgrading the P3-DX mobile robotic platform

Project keyword(s): mobile robotics, reverse engineering, co-operative vehicles

The P3-DX by Adept was once one of the leading research platforms within the mobile robotics field, now discontinued, this model has fallen well below the current state of the art. The Defence and System’s Institute possesses two of these platforms, and although the electronics are outdated, the physical shell, motors, and sensors are still of excellent quality. The student on this project would be responsible for giving these robots a ‘brain transplant’ by replacing the outdated electronics and control boards with modern solutions. This would revive these unpiloted ground vehicles for research and marketing use. This could be achieved by using low-power microcontrollers communicating with a base station, or incorporating embedded PC’s into the platforms. Regardless of the exact direction chosen the final solution must incorporate wireless communication, enabling the two upgraded units to eventually perform collaborative autonomy.

Contact: Dr Daniel Griffiths T 8302 5664 | E daniel.griffiths@unisa.edu.au URL | http://people.unisa.edu.au/Daniel.Griffiths

Exploring the role of creativity and creative problem solving in STEM education

 

Project keyword(s): Creativity, innovation, education, STEM

This project is exploring the vital role that creativity and creative problem solving play for students in technological disciplines including engineering, aviation (pilot) and aviation (Management) disciplines. The project is seeking to gather data from students in these three disciplines in order to understand better how creative these students are, and how these disciplines compare, as well as understanding better the need for creativity and creative problem solving skills in each area. This vacation program will suit a student in psychology, engineering or aviation with an interest in creativity, problem solving, and human factors. The specific work will be to help set up the proposed data collection activities, conduct data collection, and assist with analysis and publication. 

Contact: Professor David Cropley | T 8302 3301 | E david.cropley@unisa.edu.au URL | http://people.unisa.edu.au/David.Cropley

Nanomaterial/polymer composite sensors

Project keyword(s):  composite sensors

Conventional strain sensors are broadly used for many applications. Their existing limitations include low stretchability and fixed directional sensing, making them not suitable for flexible and large-strain sensing. This project aims to develop structured nanocomposite materials that suit flexible strain sensors. The composites should have innovative characteristics such as high stretchability, high sensitivity, adequate linearity and low hysteresis, providing ideal candidates for stretchable strain sensors.

Contact Associate Professor Jun Ma | T 8320 3380 | E jun.ma@unisa.edu.au URL | http://people.unisa.edu.au/Jun.Ma 

Thermoelectric polymer composites

Project keyword(s): polymer composites

This project aims to elevate the power factor (a figure to evaluate the TE performance) of organic thermoelectric materials to the level that is comparable to the inorganic semiconductor. Compounding with carbon nanoparticles is proved to be a promising way to improve TE performance but not sufficient, the best-reported result is still several order-of-magnitudes lower than the inorganic semiconductor. Therefore, multiple approaches including designing new composites structure, optimization of the molecular structure of conducting polymer matrix and tuning processing conditions should be utilized simultaneously.

Contact Associate Professor Jun Ma | T 8320 3380 | E jun.ma@unisa.edu.au URL | http://people.unisa.edu.au/Jun.Ma  

A Comparison of the Effectiveness of Safety Communication Media to Airline Passengers

Project keyword(s): Aviation, safety briefing, airline passengers, communication, retention

The ways in which airlines comply with regulatory requirement for pre-flight safety briefings has evolved significantly, and airlines have recently begun to recognize passengers’ general apathy towards receiving this information.  This has led to more creative approaches to how passengers are briefed, with the hope that passengers will pay more attention to safety presentations and have a better understanding of emergency procedures. Drawing on methods from communication studies, this project will examine the different forms of safety-critical pre-flight information, including safety briefing cards, live demonstrations, and video demonstrations.  It will then apply methods from psychology to address passengers’ knowledge retention, attention capture, anxiety, and confidence, associated with different deliveries of safety information. 

Contact Dr Steve Leib | T 8320 3865| E  steve.leib@unisa.edu.au URL |  http://people.unisa.edu.au/Steve.Leib 

Engineering/Future Industries Institute 

Project Title

Project description

Macroporous polymer monoliths for the separation of biomolecules

Project keyword(s): Materials science, porous polymers, monoliths, protein separation; capillary chromatography

Monolithic polymers have become generation of stationary phase to be used for the separation of large biomolecules. Their best-known advantage is a rigid structure with high permeability due to the presence of large through pores, which permit the use of high liquid flow rates at low backpressures. Even though this column technology has recognised advantages over particulate counterparts, it is well established that one of the limiting factors in preparing reproducible polymer monoliths with good chromatographic performance is the degree of bed heterogeneity. Therefore, alternative polymerization methods are needed to improve the structural homogeneity of polymer monoliths.

This study will explore the preparation of polymer monoliths by controlled living radical polymerisation aiming to obtain materials with well-defined macroporous structure. The materials will be used for the separation of biomolecules by capillary liquid chromatography.

Contact: Professor Emily Hilder | T 8320 6292 | E emily.hilder@unisa.edu.au URL |  http://people.unisa.edu.au/Emily.Hilder

Porous polymers for CO 2  

 

 

Project keyword(s): Materials science, porous polymers, monoliths, carbon capture

High internal phase emulsions (HIPEs) are concentrated mixtures of liquid droplets dispersed in another liquid, defined by a minimum droplet volume fraction of 74%. These HIPEs are commonly stabilized by commercially available non-ionic surfactants, where these amphiphilic species decrease the interfacial tension between the oil and the aqueous phases, allowing emulsification. However, stabilization of a new system often requires a different choice of surfactant.

Novel amphiphilic polymeric emulsifiers with versatile synthesis and ease of functionalization are highly desired, as this may allow the stabilization of multiple systems by simply altering the ratio of hydrophilic to hydrophobic portions of the molecule. The reversible addition-fragmentation chain transfer (RAFT) process allows the synthesis of such amphiphilic polymers, with the most common method through sequential polymerization of two monomers.

This study will explore the preparation of polymeric surfactant by controlled living radical polymerization to allow straightforward functionalization of the obtained polyHIPE via surfactant-assisted functionalization strategy as a new CO 2 capturing material.

Contact: Professor Emily Hilder | T 8320 6292 | E emily.hilder@unisa.edu.au URL |  http://people.unisa.edu.au/Emily.Hilder

Real-time ion sensing, using conducting polymers

Project keyword(s): Sensors, Conducting Polymers, Electronics, Manufacturing 

Conducting polymers are an interesting class of materials that bring the electrical properties of metals together with the mechanical properties of polymers. More recently they have shown promise to do new things, such as selective interaction with certain salts and ions in water. When this selectivity is combined with a monitoring process, a novel range of sensors can be developed.

This project will explore ways to tune the selectivity, investigate the electrical or optical techniques to detect the polymer response, and importantly develop processes to manufacture the materials and device. The overall aim is to enhance the manufacturability and performance of conducting polymers so our industry partners can exploit them in new commercial products.

Contact: Associate Prof Drew Evans | T 83025719 | E drew.evans@unisa.edu.au URL | http://people.unisa.edu.au/Drew.Evans 

Developing a protocol for the application of conducting paints and the measurement of target sensitivity and EMI interference of metal detector coils

Project keyword(s): Magnetic Coils, EMI shielding, Conductive Paint, Conducting Polymers, Electronics

Magnetic coils form part of the sensor head/assembly for metal detectors.  To improve the signal to noise (S/N) performance of the coils they are typically coated with a conductive paint. The conductive paint or conducting polymer is used as an EMI coating.  Optimising the performance of these coils is somewhat of a “black art” and involves a trade-off between target sensitivity and immunity to false triggering due to EMI sources.  This project will develop a measurement protocol that will correlate coil sensitivity and EMI suppression against the applied conductive coating resistance.  The aim will be to benchmark an existing conductive paint against a newly developed conducting polymer paint and to optimise both products with respect to coil performance.  The project is well suited for a materials science student with a basic understanding of electrical concepts or an electrical engineering student with a basic understanding of material properties.    

Contact: Dr Rick Fabretto | T83023675 | E rick.fabretto@unisa.edu.au URL | http://people.unisa.edu.au/rick.fabretto

Creating Customized Pharmaceuticals using Droplet-based Microfluidics

Project keyword(s): Microfluidics, microdroplets

The global technology trend is moving towards miniaturization and customized personal devices for variable applications from electronics and communication to personalized medicine and health care. Miniaturized devices are portable and consume less energy making them suitable for use in remote areas or even while travelling. In this project, the student will explore the exciting field of microfluidics by forming customised droplets that are able to carry and release high-value, lipophilic pharmaceuticals. This project will be conducted at UniSA’s Future Industries Institute, where devices will be prepared using > $10M world class micro-fabrication facilities (Australian National Fabrication Facility – South Australia). The student will experience a vibrant research environment, conduct experiments, and report their results.

Contact: Dr Aliaa Shallan | T 0402543098 | E Aliaa.Shallan@unisa.edu.au URL | http://people.unisa.edu.au/Aliaa.Shallan

Areas of study and research

+ Click to minimise