Electrical and Computer Engineering

Applications are now closed

  1. » Multi-source traffic data analytics for smart cities
  2. » Identifying the anomaly patterns from advanced data types
  3. » Mixing short- and long-range wireless sensor nodes in Internet of Things
  4. » A study of on-the-fly data reduction in IoT-based applications
  5. » Energy model of future household enhanced with IoT
  6. » Tools for improving source code understanding
  7. » Tools for automatic detection of software requirements
  8. » Near-field antenna measurements
  9. » Automatic differentiation to efficiently compute sensitivities
  10. » RoboHelper: Autonomy in Human-Robot Collaboration
  11. » Designing and Completing Challenges for Service Robots
  12. » Agricultural Estimation under a Changing Climate
  13. » Mobile software app development for education
  14. » Javascript development for education
  15. » Understanding “Culture” in Software Engineering
  16. » Understanding “Agility” in Software Engineering
  17. » Personality in Agile Teams
  18. » A Model In-Motion Charging System – Part I
  19. » A Model In-Motion Charging System – Part II
  20. » Development of a High-Density DC-DC Converter
  21. » Development of Capacitive Charging Pads for Charging Drones
  22. » A scalable, high availability, source-independent IoT platform designed using commercial cloud services from Amazon AWS
  23. » Interacting with Data: improving accessibility to public data
  24. » Mobile Baxter in the Industrial Workspace
  25. » Embedded Camera-Based Person Tracking
  26. » Towards Detecting Voice Strain in Kapa Haka: A preliminary investigation
  27. » Intelligent secretary speaker system
  28. » Robot guide for Newmarket campus using Silbot robot
  29. » Robot motion dictionary engine for Silbot and EveR robots
  30. » Scheduling using machine learning
  31. » High performance processing in FPGAs for Square Kilometre Array (SKA)
  32. » Develop an online quizzes software
  33. » Educational Simulator of electric Circuits using Game Engines

Multi-source traffic data analytics for smart cities


Supervisor

Dr Xuyun Zhang

Discipline

Electrical and Computer Engineering

Project code: ENG001

Traffic management is one of the most important aspects of smart cities. Nowadays, most medium to big cities in the world are experiencing traffic problems such as traffic congestion and scarcity of parking. On the other hand, huge volumes of traffic relevant data have been sensed and collected from a variety of sources, such as loop sensors, GPS devices, mobile phones, car parking sensors, and even social networking media. Most of existing traffic data analytic approaches just make use of only one type of the said data sources for traffic management. However, given the holistic and dynamic characteristics of the traffic system of a city, multi-source data analytics can provide more comprehensive and accurate insights upon traffic systems for urban traffic planning and scheduling.

This project aims to leverage advanced machine learning techniques such as deep learning to investigate the relationships among the various traffic data sources, for example, the relationship between the traffic congestion and the on-street parking. The analysis output can greatly benefit traffic management and mitigate the traffic conditions. The expected outcome will be: Algorithms and implementation; A multi-source traffic data analytic platform; A research paper describing the algorithms and results.

Through this project, the students can be equipped with the state-of-the-art big data mining and machine learning techniques, as well as experience the feeling of doing practical research.

Prerequisites

  1. Basic linear algebra, probability and statistics for understanding basic machine learning algorithms.
  2. At least one programming language like Java, Python, C/C++, Matlab, etc.

Identifying the anomaly patterns from advanced data types


Supervisor

Dr Xuyun Zhang

Discipline

Electrical and Computer Engineering

Project code: ENG002

Anomaly detection or outlier detection aims to automatically identify or predict anomalous but insightful patterns in data sets. It is an important and powerful data mining technique which is widely adopted in a diverse range of applications such as fraud detection, system health monitoring and event detection in sensor networks. In the current big data era, data sets with large-volume, high-dimensional, heterogeneous, geographically distributed and fast-evolving characteristics pose a considerable challenge on traditional data mining and analytics techniques including anomaly detection. Recently proposed anomaly detection methods based on the tree isolation mechanism are very fast due to their logarithmic time complexity, making them capable of handling big data sets efficiently.

This project aims to design and develop anomaly detection for advanced data types which are common in applications like real-time stream data in IoT applications and graph data in online social networking applications. We have already developed an efficient and scalable version of the algorithms based on the tree data structure and a hashing scheme. In this project, we will leverage the similar techniques for the advanced data types. Based on the proposed algorithms, we plan to build up the corresponding application prototypes. E.g., we can build up a real-time monitoring application for an IoT device.

Through this project, the students can be equipped with the state-of-the-art big data mining and machine learning techniques, as well as experience the feeling of doing practical research.

Prerequisites

  1. Basic math for understanding basic machine learning algorithms;
  2. At least one programming language like Java, Python, C/C++, Matlab, etc.

Mixing short- and long-range wireless sensor nodes in Internet of Things


Supervisor

Zoran Salcic

Discipline

Electrical and Computer Engineering

Project code: ENG003

This is an initial study of mixing wireless sensor nodes that have different range and bandwidth in order to create a robust infrastructure for the applications of IoT in new classes of applications which require increased reliability and dependability. The resulting network will combine the different types of nodes into clusters that allow higher bandwidth communication within the cluster and lower bandwidth communication with the outside of the cluster. Essential are skills and knowledge of communication protocols and programming at the low level using C, as well as programming of gateways from the clusters based on standard Linux platform. Knowledge of the programming and communication with Cloud-based applications is preferable.

A study of on-the-fly data reduction in IoT-based applications


Supervisor

Zoran Salcic

Discipline

Electrical and Computer Engineering

Project code: ENG004

The project will explore algorithms that process raw data collected in IoT (Internet of Things) sensors on-the-fly (in Edge, Fog and finally Cloud) that allow significant reduction of data that need to be stored and used in high-level algorithms of data analytics applications. Trade-offs of data filtering on multi-core and dedicated hardware accelerators will be explored and demonstrated on example application. Programming skills as well as knowledge of multi-core computing is essential, while knowledge of hardware accelerators (and their FPGA implementation), which may include high-level synthesis tools, is preferable. Knowledge of Altera/Intel FPGAs and use of design specifications in OpenCL is desirable.

Energy model of future household enhanced with IoT


Supervisor

Zoran Salcic

Discipline

Electrical and Computer Engineering

Project code: ENG005

Building energy model of a household that includes consumption, generation and storage of energy, is the main target of the project. The model will be based on the use of SystemJ language as a glue that may connect different modules written in other programming languages. The model can be used both for simulation and real control of energy in a household, as well as a hybrid that allows mixing simulation with the real control of energy consumers and generators. Excellent programming skills and the use of virtual and real resources are essential.

Tools for improving source code understanding


Supervisor

Kelly Blincoe

Discipline

Electrical and Computer Engineering

Project code: ENG006

This project investigates ways to make it easier for software project newcomers to understand the project’s source code. The project could involve a mix of data analysis and tool development. Excellent communication skills required.

Tools for automatic detection of software requirements


Supervisor

Kelly Blincoe

Discipline

Electrical and Computer Engineering

Project code: ENG007

This project investigates ways to automatically detect new software requirements by analysing user text. The project could involve a mix of data analysis and tool development. An interest in machine learning and natural language processing desired. Excellent communication skills required.

Near-field antenna measurements


Supervisor

Dr Andrew Austin

Discipline

Electrical and Computer Engineering

Project code: ENG008

Antennas are typically characterised in the ‘far-field’, however, for highly directional antennas, far-field measurements are not always possible due to the large physical separations required. This project will investigate ‘near-field’ measurement techniques and develop a near-field scanner to automate the process.

Experience with MATLAB and/or Labview required; experience with electronics desirable; training will be provided on the use of RF test equipment.

Automatic differentiation to efficiently compute sensitivities


Supervisor

Dr Andrew Austin

Discipline

Electrical and Computer Engineering

Project code: ENG009

Automatic differentiation (AD) is a novel technique to efficiently compute the derivative of any arbitrary function written as a computer program. The aim of this project is to build an AD toolbox suitable for estimating the sensitivities in numerical electromagnetics computer programmes.

Good programming skills required in Matlab or C/C++ or Fortran.

RoboHelper: Autonomy in Human-Robot Collaboration


Supervisor

Dr. Mohan Sridharan

Discipline

Electrical and Computer Engineering

Project code: ENG010

The objective of this project is to enable robots to assist humans in applications such as health care, disaster rescue, and surveillance. This project will involve the design and implementation of algorithms in simulation and on physical robots, for problems such as knowledge representation, adaptive reasoning, learning and perception.

Pre-requisites

(a) proficiency in probability, calculus and linear algebra.

(b) expertise in object-oriented programming.

(c) significant interest in working with robots.

(Optional) prior experience in robotics, artificial intelligence or machine learning will help, but is not essential.

Designing and Completing Challenges for Service Robots


Supervisor

Dr. Mohan Sridharan

Discipline

Electrical and Computer Engineering

Project code: ENG011

Do you want to design and complete challenge tasks for service robots collaborating with humans? Are you interested in competing with robotics researchers in other countries who have designed such challenge tasks for robots? This project gives you the opportunity to design and complete such challenges both individually and as a member of a team.

Pre-requisites

(a) proficiency in probability, calculus and linear algebra.

(b) expertise in object-oriented programming.

(c) significant interest in working with robots.

(Optional) prior experience in robotics, AI or machine learning will help, but is not essential.

Agricultural Estimation under a Changing Climate


Supervisor

Dr. Mohan Sridharan

Discipline

Electrical and Computer Engineering

Project code: ENG012

The objective is to design and implement frameworks for estimating extreme events, irrigation requirement, and crop yield, under future climate change scenarios. The models needed to provide such estimates will be learned from decadal (historical) data of measurements of weather parameters and agricultural indicators.

Pre-requisites

(a) proficiency in probability, calculus and linear algebra

(b) expertise in object-oriented programming.

(Optional) prior experience in artificial intelligence or machine learning will help, but is not essential.

Mobile software app development for education


Supervisor

Nasser Giacaman

Discipline

Electrical and Computer Engineering

Project code: ENG013

Student needs to have experience developing mobile apps (e.g. Android or Unity), and experience with creating 3D shapes like with Unity3D. Strong programming skills in general (Java, Javascript, etc).

Javascript development for education


Supervisor

Nasser Giacaman

Discipline

Electrical and Computer Engineering

Project code: ENG014

Student needs to already have strong experience developing in Javascript. The more different libraries used, the better.

Understanding “Culture” in Software Engineering


Supervisor

Dr Rashina Hoda

Discipline

Electrical and Computer Engineering

Project code: ENG015

This project involves investigating the notion of ‘culture’ in software engineering research through a combination of research literature review and industry survey. It will include: (1) conducting a literature review on the topic and summarizing its findings and (2) conducting an industry survey and summarizing its findings in the project report.

Experience in conducting literature reviews and surveys preferred but not a must. Candidate must be willing and quick to learn, be proactive and self-organized, have a high standard of verbal and written English, and be dedicated to achieving high quality work.

Understanding “Agility” in Software Engineering


Supervisor

Dr Rashina Hoda

Discipline

Electrical and Computer Engineering

Project code: ENG016

This project involves developing an understanding of “agility” as applicable to agile software development through a combination of research literature review and industry survey. It will include: (1) conducting a literature review on the topic and summarizing its findings and (2) conducting an industry survey and summarizing its findings in the project report.

Experience in conducting literature reviews and surveys preferred but not a must. Candidate must be willing and quick to learn, be proactive and self-organized, have a high standard of verbal and written English, and be dedicated to achieving high quality work.

Personality in Agile Teams


Supervisor

Dr Rashina Hoda

Discipline

Electrical and Computer Engineering

Project code: ENG017

This project involves investigating the impact of personality in agile software engineering teams through a combination of research literature review and industry survey. It will include: (1) conducting a literature review on the topic and summarizing its findings and (2) conducting an industry survey and summarizing its findings in the project report.

Experience in conducting literature reviews and surveys preferred but not a must. Candidate must be willing and quick to learn, be proactive and self-organized, have a high standard of verbal and written English, and be dedicated to achieving high quality work.

A Model In-Motion Charging System – Part I


Supervisor

Dr Duleepa J Thrimawithana

Discipline

Electrical and Computer Engineering

Project code: ENG018

In-motion charging of electric vehicles (EV) using inductive power transfer (IPT) has become a main stream research topic in recent times. The goal of this project is to design and develop a scaled down in-motion charging system to showcase the applications of this technology. 
During this project, the student is expected to design and develop the power electronic converters and magnetics required for the power transmitters. The student should also develop a lightweight oval track and an electronic guidance system for an RC car that will be used to demonstrate the in-motion charging. A remote monitoring system should also be implemented to show real-time operating conditions of the system. Note that the project titles ‘A Model In-Motion Charging System – Part II’ will involve developing the receiver side electronics.  

The initial system specifications are

  • Supply voltage to transmitter - 40 Vdc
  • Maximum transmitter power - 100 W pulses
  • Average power consumption of the RC car - 20 W
  • Track - a lightweight 10 m oval track
  • Communication - BLE 4.1

Skills required

Students should have experience/knowldage in Altium, magnetics, power electronics, simulation, CAD and embedded programing.

A Model In-Motion Charging System – Part II


Supervisor

Dr Duleepa J Thrimawithana

Discipline

Electrical and Computer Engineering

Project code: ENG019

In-motion charging of electric vehicles (EV) using inductive power transfer (IPT) has become a main stream research topic in recent times. The goal of this project is to design and develop a scaled down in-motion charging system to showcase the applications of this technology.

During this project, the student is expected to design and develop a wireless pick-up circuit and associated magnetics to extract power from an in-motion charging system. A custom-built RC car powered by a bank of super capacitors, which is charged when it travels over transmitter coils of the in-motion charging system, will be used as the EV. In addition, the student should also develop, a brushless DC motor driver circuitry and steering circuitry for the RC car. The student will also develop necessary remote monitoring hardware to extract and display vital data of the system through a web interface or a GUI. Note that the project titles ‘A Model In-Motion Charging System – Part I’ will involve developing the transmitter side electronics.

The initial system specifications are

  • Peak power consumption of the RC car - 30 W
  • Rated voltage of the RC car – 10 V
  • Communication - BLE 4.1


Skills required

Students should have experience/knowldage in Altium, magnetics, power electronics, simulation and embedded programing.

Development of a High-Density DC-DC Converter


Supervisor

Dr Duleepa J Thrimawithana

Discipline

Electrical and Computer Engineering

Project code: ENG020

During this project, the student is expected to design and develop a 500 W Resonant Dual Active Bridge (RDAB) converter suitable for interfacing a 100 Vdc source with a 100 Vdc load. This will involve the design of suitable planer magnetics and GaN based power conversion circuitry to improve the power density. An STM32 based closed-loop controller will also be designed and implemented to control the RDAB and the ability to cascade them will be investigated.

The initial system specifications are,

  • Input voltage – 100 Vdc
  • Output voltage – 100 Vdc
  • Rated power – 500 W

Skills required

Students should have experience/knowldage in Altium, magnetics, power electronics, simulation and embedded programing.

Development of Capacitive Charging Pads for Charging Drones


Supervisor

Dr Aiguo Patrick Hu

Mr Reza Sehdehi

Discipline

Electrical and Computer Engineering

Project code: ENG021

This project aims to develop a capacitive charging station for drones. The charging station shall be able to transfer 15-20W of power continuously. A charging circuit shall be included on the drone to charge the on the board Lithium-ion battery of a drone safely.

The purpose of this project is to charge autonomous industrial drones without any plugs-ins.

During this project, the successful applicant will learn about Capacitive power transfer, Circuit simulation and analysis, PCB design, SMD soldering, assembly and testing.

The applicants must be enrolled in Electrical and Electronic, Mechatronic or Computer Systems Engineering at an approved university. The applicants must have done a course involving the basics of electronic circuit analysis. Knowledge on wireless power is advantageous but not a requirement at this stage.

A scalable, high availability, source-independent IoT platform designed using commercial cloud services from Amazon AWS


Supervisors

Dr. Kevin Wang
Mr Akshat Bisht

Discipline

Electrical and Computer Engineering

Project code: ENG031

Modern IoT (Internet of Things) technology and devices suffer from the issue of compatibility, interoperability and scalability. Cloud technology offers a unique platform to aggregate distributed devices and heterogeneous data to provide more intelligent, highly available services to the end users. This project seeks to make use of the commercial AWS cloud platform and aggregate commercial and research gadgets to form a scalable IoT platform. Knowledge in web programming is required, and experience in cloud computing and database systems is highly preferred for this project.

Interacting with Data: improving accessibility to public data


Supervisors

Dr. Kevin Wang
Mr. Andrew Chen

Discipline

Electrical and Computer Engineering

Project code: ENG032

The public sector produces a lot of data, but the majority of the public has little to no understanding about that data. Visualisation can be a meaningful tool for helping communicate public data. Using the budget as a case study, the student working on this project will create an interactive data visualisation, also allowing users to adjust and modify values, and create feedback on their preferred vision for policy. Data can then be analysed and visualised further to lead to direct democratic outputs to the public sector. Knowledge of Javascript is required, experience with web development and databases would be helpful.

Mobile Baxter in the Industrial Workspace


Supervisors

Dr. Kevin Wang
Mr. Andrew Chen

Discipline

Electrical and Computer Engineering

Project code: ENG033

The Baxter humanoid robot is designed to work alongside humans in industrial automation contexts in a safe and friendly way. We have created a mobile base for one of our Baxter robots, which will allow the robot to drive itself around an industrial workspace. The student working on this project will develop ROS scripts that allow us to control the mobile base in different ways, and integrate other sensor data to build in navigation and SLAM capabilities. Knowledge of Python is required, experience with sensor systems, robotics, and ROS would be helpful but not required.

Embedded Camera-Based Person Tracking


Supervisors

Dr. Kevin Wang
Mr. Andrew Chen

Discipline

Electrical and Computer Engineering

Project code: ENG034

A lot of computer vision research has been done into using cameras to track people as they move through a space, but the majority of these algorithms involve computationally expensive methodologies that are not suitable for implementation in embedded systems. The student working on this project will collect and annotate video data, develop and understanding of single-view geometry, work on an embedded implementation of a person tracking algorithm, and trial it in a test environment. Knowledge of Python is required, and experience with computer vision would be helpful but not required.

Towards Detecting Voice Strain in Kapa Haka: A preliminary investigation


Supervisors

Catherine Watson

Te Oti Rakena

Peter Keegan

Disciplines

Electrical and Computer Engineering, Engineering

School of Music, Creative Arts and Industries

Te Puna Wānanga, Education and Social Work

Project code: ENG061

Vocal performance often requires high level vocal effort, which can lead to vocal abuse, and ultimately vocal fold damage. These vocal health issues are known to impact singers in musical theatre and the contemporary voice genres. It can also be an issue in the Māori Performing arts genre Kapa Haka. Voice training, and specialists can help performers train their vocal system to prevent vocal strain. Voice analysis systems can also provide feedback to performers to aid them in improving their vocal health. The singing signal is rich with information about the physiology of the singer, and it is that we propose to tap into, to produce a system that will provide real-time feedback on vocal health for Kapa Haka performers.

Before we can provide feedback on vocal strain, we need to understand the acoustics of the healthy Kapa Haka voice. This project will involve making recordings of some seasoned Kapa haka performers to record of difference vocal types. It will then involve doing a variety of acoustic analysis on Kapa Haka voices corpus, using a variety of speech processing techniques, enabling us to get identify the features of a healthy Kapa Haka voice.

This project would suit students with a back ground in Engineering, Computational Science, Speech Science or Acoustic Phonetics.  An interest in Voice Acoustics or Speech Processing and  Kapa Haka or Voice Performance is encouraged. They will be able to work independently, be thorough and be excited by the prospect of working in a multi-disciplined team based in Engineering, Music, and Te Te Puna Wānanga.

Intelligent secretary speaker system


Supervisor

Ho Seok Ahn &
Bruce MacDonald

Ext: 87860

Discipline

Electrical and Computer Engineering

Project code: ENG106

This project will design an intelligent speaker system that can perform the role of a secretary. For this we will test some existing intelligent speakers and AI platforms, and design a new secretary system. The student will undertake research to enable the system to perform key functions such as conversation skills using existing speech recognition and generation methods, and a simple knowledge graph for making decisions will also be produced.

The project outcome is a prototype of working secretary speaker system that can communicate simple information to people.

Robot guide for Newmarket campus using Silbot robot


Supervisor

Ho Seok Ahn &
Bruce MacDonald

Ext: 87860

Discipline

Electrical and Computer Engineering

Project code: ENG107

This project will use our mobile Silbot robot and robotics software to create a robot that can navigate around the Newmarket campus, and is able to guide visitors from reception to the robotics labs. The student will use the ROS system and existing navigation software to implement mapping and localisation and to create a guide application. Simple verbal conversation will also be integrated. 

The outcome is a working robot guide that can navigate from reception to the Robotics and Healthcare Robotics labs at Newmarket.

Robot motion dictionary engine for Silbot and EveR robots


Supervisor

Ho Seok Ahn &
Bruce MacDonald

Ext: 87860

Discipline

Electrical and Computer Engineering

Project code: ENG108

This project will develop a robot motion dictionary that provides a technical descriptions defining robot motions that express common behaviours.

We have a prototype of this system, and this student project will apply the existing prototype to different robot systems: Silbot and EveR.

The outcome is a working robot system that can make different motions of the robots with the behaviour to express. 

Scheduling using machine learning


Supervisor

Dr Oliver Sinnen

Discipline

Electrical and Computer Engineering

Project code: ENG109

Parallel computing has become extremely important in today's IT world. Almost all computers are now parallel systems. To efficiently use them one needs to divide a program into tasks and to schedule them onto the processors or cores of the system. Theoretically this is addressed by task scheduling where a program is described by a directed acyclic graph, a so called task graph. The nodes represent the tasks and the edges represent the communications between the tasks. Algorithms are designed to find the best scheduling of this graph onto a given parallel system. Many algorithms have been proposed so far.

Recently, machine learning has been successful with other hard problems. This project aims to investigate how machine learning can be applied to task scheduling. An enabling factor for this is that recent research of the Parallel and Reconfigurable Computing (PARC) lab produced a large set of optimal schedules which can be used for training a machine learning network. The algorithms will be implement and evaluated using existing frameworks. 

High performance processing in FPGAs for Square Kilometre Array (SKA)


Supervisor

Dr Oliver Sinnen

Discipline

Electrical and Computer Engineering

Project code: ENG110

The Square Kilometre Array (SKA) will be the world's largest radio telescope. A major challenge for its construction is the transportation and processing of the massive amounts of data produced by the many radio telescope dishes. The research of this project is to use FPGAs as reconfigurable high performance processing units for the processing needs of the SKA. The students will take an algorithm or method described in C code or some other means and implement it in an FPGA board. This involves the investigation of high-level methods of programming and configuring the FPGAs. A detailed experimental evaluation will demonstrate the achievable performance.

Requirement: Experience in digital hardware design.

Develop an online quizzes software


Supervisor

Waleed Abdulla

Discipline

Electrical and Computer Engineering

Project code: ENG117

This project is about developing an educational package to initiate quizzes by instructors without programming skills. Those quizzes initially targeting courses in the electrical and computer engineering department. The package should be able to interact with other commonly used simulators such as LTspice to make it more flexible and able to solve problems. The applicant must have good programming skills.

The applicant must contact the supervisor before applying to this project to discuss the project requirements or otherwise the project may be declined even after the official acceptance.

Educational Simulator of electric Circuits using Game Engines


Supervisor

Waleed Abdulla

Discipline

Electrical and Computer Engineering

Project code: ENG116

Analysis of electric circuits using different circuit theory methods is a challenging task to perceive by many students in electrical and computer engineering. This simulator will pave the way to develop a software that will visually imitate the working condition of electric circuits. A suitable game engine will be selected to build the simulator. Game engine will provide a visually attractive objects that can be interesting to learners. A training and testing phases will be developed for each method such that the student can build up the required experience in circuit analysis in an interesting way and then go through a suitable testing procedure to assess the understanding of each topic. We will start with few topics first and extend as time allows. The applicant is expected to have good experience in programming and game engines programming is a bonus.

The applicant must contact the supervisor before applying to this project to discuss the project requirements or otherwise the project may be declined even after the official acceptance.