- Be a female student enrolled in undergraduate or postgraduate study in the 2015 academic year.
- Be enrolled in a university in Asia Pacific, excluding Greater China* where we have an additional scholars’ retreat in China Mainland. Citizens, permanent residents, and international students are eligible to apply.
- Be majoring in computer science, computer engineering, or a closely related technical field.
- Exemplify leadership and demonstrate passion for increasing the involvement of women in computer science.
SDN is a new networking technology, which greatly improves network programmability, that is changing how we design, build and operate networks. In this project, we will investigate the practical issues on the adoption of SDN in production networks. It is a great opportunity to work with SDN communities both locally and internationally.
Value of award: Up to $20,000 + tuition fees
Tenure: One year
*Strong programming skills in C/C++ or Java.
*Strong motivation for developing practical networking solutions
Contact person: Dr. Qiang Fu, firstname.lastname@example.org
- "Performance Evaluation and Analytical Modelling of SDN and OpenFlow-based Networks and Systems". The successful candidate is expected to have a good foundation in theoretical performance analysis techniques, viz. and queuing theory. Knowledge of common network simulation platforms (e.g. OmNet++, QualNet, etc) would be advantageous. He/she will also have the opportunity to spend time in Kyoto University, Japan, to work with world leading experts in performance analysis.
- "Traffic classification in Enterprise Networks using Software Defined Networking". The successful candidate is expected to have a good fundamental knowledge of networking and strong hands-on skills required to validate his/her research results on a real network testbed. Knowledge of traffic classification techniques for supporting quality of service in the Internet would be given preferential consideration.
Lived in? I lived in Hyderabad, India, before moving to New Zealand in 2006. Since then I have lived in Palmerston North, Auckland and Hamilton. First job? My first job was straight after finishing my PhD. I worked as a lecturer of Electronics Engineering at the Centre for Engineering and Industrial Design at the Waikato Institute of Technology in Hamilton. Key research interests? I have broad interests within Smart Sensors, Wireless Sensor Networks, Internet of Things, Activity detection and wellness pattern generation using ad hoc Wireless Sensor Networks, e-Learning - and last but not least - educational game development. Why Wellington? Wellington is a great place to live and work, with amazing scenery. When did you begin at Vic? I started as a Postdoctoral Research Fellow in June 2017. Where can people find you at VUW? EA 107. Who have you been working with? I am working with Dr Karsten Lundqvist as a member of the e-Learning Research Group within the School of Engineering and Computer Science. What have you been working on? Creating tools to improve teaching and learning within various cultural settings, including the use of games and gaming methods in education, and especially for engaging Maori/Pasifika students in Computer Science learning. What have you enjoyed the most so far? Developing culturally-relevant games for Māori/Pasifika students who are still in school. This is because creativity is the foundation of what we do - and it is what makes creating games so exciting. Other than that, a highlight for me has been learning to speak and use Te Reo Māori for research purposes. What are some of the challenges you have faced? Coming from an Electronics Engineering background, taking up a role in Computer Science was initially a challenge – but surprisingly, what I have learnt is that when you are motivated and push yourself to try something difficult, it becomes a passion rather than a challenge. What are you looking forward to in the future? I am looking forward to using the latest technology alongside cultural diversity to advance teaching and learning. I would also like to build on my existing skills and continue my involvement in many professional associations.
- David T. Freeman
The project was one of several available to students to choose as part of a compulsory year-long project management paper, where students were asked to combine their electronics, software and networking expertise to solve a problem for an external client.
A team comprising Patrick Savill, Layne Small, Callum Gill, Miten Chauhan, Kandice McLean and Marc Laroza created a product for the New Zealand Fire Service’s Urban Search and Rescue team that simulates human behaviours for search and rescue training exercises.
All of the team members are studying Engineering, but as they are pursuing different majors, they each brought something different to the table. The project was called ‘Operation Zombie’ as it is targeted at replacing humans during training operations. Real people cannot be placed in realistically dangerous scenarios for fear of physical harm - and using dummies to simulate these scenarios is currently too expensive.
Patrick, the team’s spokesman, says they were aiming to produce a simple and cheap solution to the problem, by creating a small, self-contained motorised control box that can be operated wirelessly from a website. Instead of using a real person, the motorised box can mimic heat loss from the human body that might occur due to exposure in an emergency situation, as well as providing a realistic rescue scenario where someone is trapped in a river or under rubble. The group’s design was praised for being robust, waterproof and able to be operated at long-range.
“We had to build the hardware, configure the network, and design software to run the web page, so we were able to utilise our team members’ individual skills based on their areas of expertise”, Patrick says. “It was an enjoyable challenge with a tangible result, which is always a bonus”.
Patrick and his teammates found the project a useful platform to practise both ‘hard’ and ‘soft’ skills learnt in class.
“I used what I knew about creating printed circuit boards - equipment that supports electrical components - as well as everything I had learnt about micro-controller coding”, says Patrick. “A happy by-product of the course was getting to practise people-management and communication skills which are so valued by employers”.
The team met some hurdles along the way, including “timing, work falling behind, untested assumptions - and blowing up electronic parts!”
“It was definitely challenging”, says Patrick. “I went in imagining the utopia of a high-functioning team, perfect circumstances and rigid scheduling – but came out the other side with an intimate understanding of Murphy’s Law.”
Patrick says that despite the challenges, the project has definitely added value to his university degree.
“I now realise that the challenging projects are the ones you learn the most from”, he says. “I have learnt far more by making mistakes than I ever could have from easy successes. Now I hope to find a job where I can use my engineering skills and really make a difference in the world”.
And Patrick’s advice to future students?
“No one said Engineering was going to be easy, so to paraphrase American writer Denis Waitley, “Expect the best, plan for the worst, and prepare to be surprised!””
New digital media centre one of first recipients of Government’s Entrepreneurial Universities funding
- Coordinated resource allocation / scheduling in 4G/5G mobile cellular networks
- Content distribution in mobile / vehicular networks
- Network Functions Virtualisation (NFV)
- Software Defined Networking (SDN)
People like to think of themselves as complex, but compared with things they are all too predictable. That’s what Winston Seah, Professor of Network Engineering in Victoria’s School of Engineering and Computer Science, has found as he leads a team of researchers working on the Internet of Things (IOT). Currently the area of internet development “the whole world is crazy about,” says Winston, IOT seeks to give everyday and other objects network connectivity so they can send and receive data. Supported by a three-year $1 million deal with telecommunication giant Huawei New Zealand, one of the aspects of IOT Winston and his team are exploring is how networks might handle the massively increased traffic such functionality would bring. “It’s already been predicted the numbers are going to exceed human connections by hundreds of thousand times or even a billion. How many smartphones can we carry? Maybe two or three—and that’s a lot. But let’s say my jacket is embedded with sensors that measure my body statistics. It could easily have 100 sensors, each sending data. Multiply that by the number of people in a city. And that’s just one application.” Then there is the variability of what is being transmitted and when. “It’s not like the internet in the past where you’re just dealing with human beings’ communications. People are creatures of habit. How we communicate tends to be the same. Whereas machines are so different. And sometimes you just can’t think what kind of data they will send and what kinds of patterns will emerge.” Winston and his team are also developing individual IOT applications such as land movement sensors that give advance warning of potential landslides, which are being trialled in the Manawatu Gorge near Palmerston North. With a glint in his eye, Winston ponders other New Zealand sensor candidates, turning the Internet of Things into “the Internet of Sheep, the Internet of Cows, the Internet of Pinot Noir vines…”
At the recent Genetic and Evolutionary Computation Conference, GECCO, Amsterdam, July 2013, they were awarded the best paper in the Genetics-based Machine Learning Track. GECCO is an Australian Research Council (ARC) A-rated conference. There were only 13 best papers awarded out of 570 submitted papers from the leading researchers worldwide.
The core idea of the work is to reuse already learnt information to solve increasingly harder problems, which the research team has shown to scale successfully to problems previously unsolved in machine learning. Surprisingly, nearly all other machine learning algorithms restart learning at the start of each new problem. This work introduces evolvable finite state machines into a problem's representation as a way of reusing cyclic building blocks, which are most appropriate for domains requiring repetitive patterns of knowledge. The work produced for the first time compact solutions that could solve any size problems in a number of important domains, such as parity problems. Evolutionary Computation is a branch of Artificial Intelligence which takes its inspiration from Darwinian ideas of survival of the fittest as multiple solutions are tested and bred with each other until the fittest survive. The research team form part of the Evolutionary Computation Research Group (ECRG), Victoria University of Wellington, which is one of the largest and most successful groups of this type in the world - currently with available doctoral places and scholarships available.
Track: Genetics Based Machine Learning
Extending Scalable Learning Classifier System with Cyclic Graphs to Solve Complex Large-Scale Boolean Problems. Muhammad Iqbal, Will N. Browne, Mengjie Zhang
(email@example.com; firstname.lastname@example.org; email@example.com)
Evolutionary computational techniques have had limited capabilities in solving large-scale problems, due to the large search space demanding large memory and much longer training time. Recently work has begun on automously reusing learnt building blocks of knowledge to scale from low dimensional problems to large-scale ones. An XCS-based classifier system has been shown to be scalable, through the addition of tree-like code fragments, to a limit beyond standard learning classifier systems. Self-modifying cartesian genetic programming (SMCGP) can provide general solutions to a number of problems, but the obtained solutions for large-scale problems are not easily interpretable. A limitation in both techniques is the lack of a cyclic representation, which is inherent in finite state machines. Hence this work introduces a state-machine based encoding scheme into scalable XCS, for the first time, in an attempt to develop a general scalable classifier system producing easily interpretable classifier rules. The proposed system has been tested on four different Boolean problem domains, i.e. even-parity, majority-on, carry, and multiplexer problems. The proposed approach outperformed standard XCS in three of the four problem domains. In addition, the evolved machines provide general solutions to the even-parity and carry problems that are easily interpretable as compared with the solutions obtained using SMCGP.
Victoria University is pleased to announce a co-funded PhD scholarship position (approx NZ$35k/year for 3 years) in Software Defined Networks (SDN). The position based at Victoria University will provide research which is of practical benefit to the SDN community and the NZ networking community in particular. This may be via applied research of use and interest to REANNZ, and possibly international research partners like ESnet.
Possible research areas
Interdomain SDN (“east-west interface”): how to connect SDN networks in different administrative domains, including BGP alternatives
Optimal network design: how to design and test through the use of automated software optimal SDN based network designs based on specified constraints (Eg, number of routes, redundancy of critical links, etc)
Migration to SDN: how to migrate common ISP/carrier architectures from non-SDN to SDN (including network management)
SDN network management: how to manage an SDN network without needing legacy protocol support (Eg, streaming statistics replacing use of SNMP polling)
Systems/networking software experience preferred
Algorithm development including its software implementation
Software development experience in C++/Java (python advantageous)
Software engineering/test practices such as unit testing
SDN/OpenFlow experience advantageous but not necessary
Networking protocols (Eg, BGP) advantageous but not necessary
Basic familiarity with network architectures and protocols, some exposure to new Future Internet Initiatives like OpenFlow, Named-Data Networking, GENI etc.
Strong ability to articulate technical problems and solutions, using various communication mechanisms such as presentations, conference papers etc.
Play it Again: Creating a Playable History of Australasian Digital Games, for Industry, Community and Research Purposes.
“So I've been looking at a passive way to measure the foetal heart rate. You can do this either by putting electrodes on the mother and then detecting the Electric Cardiogram (ECG) signal, or by listening with microphones, which is what my research has focused on. This is more like using the Pinard – the foetal stethoscope that midwives used before the invention of Doppler ultrasound, but much more reliable and easy to use.”Paul, who previously worked at Industrial Research Limited (IRL) in Gracefield, has been collaborating with his former colleagues to develop a method of using microphones to separate out the mixture of signals emitted from the womb by using a technique called Blind Source Separation.
“This isolates the foetal heart rate from the mother's heart rate, and the background noise. It's also a more passive method of monitoring that doesn't negatively impact upon either the mother or the baby.”Paul says he and his IRL counterparts are now working closely with Wellington midwives to collect data from mothers using this less invasive method.
“We've proved the method works in the last few weeks of pregnancy, but we're hopeful that eventually we will be able to use it from when a foetus is 18 weeks. Doppler ultrasound can work from about 12-14 weeks, but the important stages are later in the pregnancy.”
Daniel Akinyele has been awarded one of two student sponsorships to attend the 2014 NZ Wind Energy Conference and Exhibition, which will be held from the 14th-16th April at Te Papa Tongarewa in Wellington. In addition to presenting his proposal at the conference, Daniel will spend a day at Transpower, meeting staff and learning about the company, and about the electricity market and transmission planning and investment. Daniel’s proposal focuses on the intergration of wind power into distribution networks in New Zealand from the end-use and wider application perspectives. His research will model and simulate grid-connected micro and commercial-scale generation from residential and commercial premises respectively. It also considers microgrids connected to local grids for city-wide applications, which may also be disconnected from the network and operated independently in the event of a disaster. New Zealand probably has the most abundant wind energy resource in the world. Harnessing this natural resource for widespread distributed power generation (DPG) in New Zealand will not only provide support to the electrical network, improve the reliability and efficiency of the electricity supply and offer environmental benefits, but also aid the achievement of sustainable and future smart grid and help the government realize its goal of 90% renewable power by 2025. Daniel holds a National Diploma in Electrical and Electronic Engineering with Distinction from Osun State Polytechnic, Nigeria in 2002. He holds a First Class Degree in Electrical and Electronic Engineering from Nigeria’s Premier University, the University of Ibadan in 2008. He attended Loughborough University, UK for his Masters Degree in Renewable Energy Systems Technology, graduating with Distinction in 2010. He was a Senior Engineer in the renewable energy research group of the National Agency for Science and Engineering Infrastructure (NASENI) under the umbrella of the Federal Ministry of Science and Technology, Nigeria. He was responsible for renewable energy systems design and installation. He then joined the Department of Electrical and Information Engineering, Covenant University, Nigeria, as an assistant lecturer, teaching the fundamentals of Electrical Engineering and Network Analysis. He is currently a PhD student in the School of Engineering and Computer Science, Victoria University of Wellington, under the supervision of Dr Ramesh Rayudu.
Magritek specializes in providing compact NMR and MRI systems for industrial and educational customers around the world. The present R&D focus is the development of compact low field NMR spectroscopy systems for chemistry education, industrial chemical processing and pharmaceutical markets. The product development work we are undertaking involves several technical disciplines including electronic, software, mechanical, magnet and chemical engineering. Our products consist of electronic hardware with embedded systems that interact with an application running on a users computer.
We are developing a new software control system for a new hardware platform to be used in upgrades of existing and new NMR related products. The new embedded ARM based hardware platform will use LINUX and will typically be controlled using an Ethernet port or WIFI. In order to simplify the development for the new NMR products we intend to create one or more custom high level languages that will be used to design experiments and NMR pulse sequences. The custom languages will generate LLVM IR output. This LLVM IR can be optimized for specific CPU's, like ARM or a custom FPGA based softcore processor. The task and challenge is to create a language and an associated debugging tool to debug code on a remotely connected system. The compiler and debugging tool shall run on Windows.
Magritek has an established team of world leading scientists and engineers and this is also bolstered by our collaboration with staff at Victoria University of Wellington. This position will provide someone with the unique opportunity of developing in these areas:
1. interacting with a diverse technical team that has a commercial focus.
2. undertaking leading edge research and development
3. working on a real project that will end up with customers
4. working within an environment will real commercial pressure
5. presentation and report writing
The skills that the applicant must have are C++, Linux, LLVM and compiler development. Additional skills such as C#, ANTLR and knowledge of ARM processors would be desired but are not essential.
The applicant must also have recently graduated with a Masters or PhD in computer science or software engineering or equivalent.
For eligibility criteria, please go to https://www.callaghaninnovation.govt.nz/what-we-do/funding-and-grants/rd-student-grants
Application proceduresPlease email CV and cover letter to: Robin@magritek.com
Please also include a copy of your academic transcript and two referees. Closes: 2 Dec, 2014 Commences: 1 February 2015 Type: Contract Remuneration:$60,000 per annum pro-rata Location: Wellington, New Zealand Website: http://www.magritek.com/ Program: Graduate recruitment program
Contact detailsMr Robin Dykstra
Ph: 04 920 7671
The end goal of the project was to improve the accuracy of existing classification systems, says Michael. He used a lot of the knowledge he gained studying at Victoria, especially his 400-level Artificial Intelligence papers, which gave him the understanding of the algorithms necessary for the project. However, it turned out to be a challenge. “Often there was a lot of learning required before progress could be made.”
The facilities provided by the University were also invaluable, from the software Michael used while studying, to the hardware to run his experiments. He also has some advice for future students: “Make sure you don’t forget to document all of the small decisions that seem obvious to you. Every aspect of your project is important—and the more you can communicate what you did, the happier you’ll be with your final report.” Being in Wellington also means Michael is close to several high-profile technology companies, including TradeMe and Xero, which could now feature in his future. “I’d love to work on embedded systems, with some aspect of machine learning,” he says. “This project has given me so many skills that I hope to use in my future, both personally and professionally.”
Survey & Interviews of Recent ECS Graduates
Pilot Contamination in 5G Massive MIMO Systems
Evolutionary Machine Learning and Data Mining
Study of Industrial IoT applications and use cases in NZ
QoS-aware Web Service Location Allocation
QoS-aware Web Service Location Allocation
Automated training of orchestral conducting
Microfluidic testbed for plasmonic sensors
Lead-free ferroelectrics for tunable capacitors, acoustic transducers and data storage
Virtual Reality Simulation for Healthcare Education
Evolutionary machine learning for dynamic vehicle routing problem
Evolutionary Feature Selection and Dimensionality Reduction for Large-Scale Classification
Physiological signal processing
Electrical Standards MSL Software and Measurement Systems
Electrical Standards MSL Software and Measurement Systems
Developing a Motion Sensor
An automated ambulance critical inventory tracking and alerting system
Harmonic Scale Development
Analytics Harbour Development
TrafficVis: Visualizing Network Traffic Resilience
Transport Network Resilience Proof of Concept
Transport Network Resilience Proof of Concept
Real time video stitching for live 360 video VR streaming
There is currently a lack of resources to help beekeepers know when their hives require attention, says Reuben, as well as a recent spate of thefts throughout New Zealand that have robbed beekeepers of hard-earned revenue. Honey from hives is one of New Zealand’s main agricultural exports, with over 700,000 registered beehives. Reuben ‘smart’ beehive solution has internet connectivity and uses sensors to monitor key metrics for the beekeepers. These metrics include tracking colony activity and swarm health, and providing real-time alerts for threats to the hive. “I wanted to create an entire system to solve the problems that beekeepers are currently facing,” Reuben says. “Because there are so many elements to the system, making everything work in harmony was a challenge. I used all the skills I learnt through my university courses, from Arduino programming in first year to cloud computing in fourth year.”
Reuben used Amazon Web Services (AWS) to make a cloud database and a web application for accessing beehive metrics through a simple interface, supported by AWS Advanced Consulting Partner, API Talent.
He says that he wanted to create something using both the ‘Internet of Things’, where objects are connected to the internet, as well as the ‘cloud’ where the data is stored, as both are cutting-edge technologies. “I’ve loved the whole experience of this project, especially working with API Talent and learning about AWS which is a massive game-changer at the moment,” says Reuben. “Learning from the best in the industry really made me understand what it takes to be an engineer. It’s awesome to have experience of the process involved in designing, implementing and evaluating a solution to a real-life problem. “This experience has inspired me to keep expanding my knowledge, and I’m excited for what’s to come in the technology field.”