Electrical and Computer Engineering

Exploring augmented and virtual reality

Supervisor

Nasser Giacaman

Faculty of Engineering

Project code: ENG001

Augmented reality (AR) and virtual reality (VR) technologies are gaining popularity in mainstream applications. This project will involve designing and building either an AR or VR mobile application in an educational domain. The particular domain selected will be determined closer to the time, but may include helping students learn mathematics or programming concepts. To be successful in this project, you should be a strong programmer confident in using Unity (C#).

Machine learning models predicting multiple drug effects on human cardiac rhythm

Supervisor

Avinash Malik

Faculty of Engineering

Project code: ENG002

In this project the student will devise machine learning algorithms, e.g., deep neural nets, basis function decomposition, etc., to predict effects of multiple medications on cardiac rhythm. In particular we are interested in understanding how drugs administered at different time points over a 24-hour period interact with each other to effect the cardiac rhythm.

(Unconscious) bias and its impact in Software Engineering

Supervisor

Kelly Blincoe

Faculty of Engineering

Project code: ENG003

This project will use manual analysis together with Machine Learning and Natural Language Processing methods to identify cases of bias in Software Engineering communication traces. Thematic Content Analysis will be used to analyze this data and surveys/interviews will be designed to understand the issue. Quantitative analysis of contribution activity will be analysed to understand the impact of these biases. The goal of the project is to understand bias and its impact on the community related to diversity.

Vanadium Redox Battery based Hybrid Energy Storage System for Microgrid

Supervisor

Abhisek Ukil

Faculty of Engineering

Project code: ENG004

Renewable energy and grid integration will be of great importance in coming decades to move from fossil fuel based energy source to green energy. New Zealand as well as worldwide, this is a significantly important research topic. However, renewable energy like solar and wind are intermittent in nature. Therefore, we cannot readily connect those devices into the grid.

To facilitate the renewable energy into the grid, grid scale energy storage devices are needed. Typically battery and supercapacitors are used in conjunction as hybrid energy storage. One of the most promising technologies is the vanadium redox flow (VRB) batteries. On one hand, it is capable of delivering grid scale power, on the other hand, it is free of charging and discharging cycles, like other types of batteries. This project will involve modelling of VRBs in a hybrid energy storage system. This would be followed by in-depth simulation verification of the most promising methods using software like PSCAD, EMTP, and hardware testing.

Pre-requisite: Power systems, Power electronics courses

1. U. Manandhar, N.R. Tummuru, S. K. Kollimalla, A. Ukil, H.B. Gooi, K. Chaudhari, “Validation of Faster Joint Control Strategy for Battery and Supercapacitor Based Energy Storage System,” IEEE Transactions on Industrial Electronics (IF: 7.16), vol. 65, no. 4, pp. 3286–3295, 2018.

DC Arc Fault Detection

Supervisor

Abhisek Ukil

Faculty of Engineering

Project code: ENG015

As the number of DC loads are growing in our daily uses, the low voltage DC (LVDC) distribution system is becoming important, especially for EV charging, datacenter applications. Integration of renewable energy sources at LV level, like rooftop photovoltaic (PV), fuel cells, etc. would be relatively easier with the LVDC system than the LVAC.For high-voltage DC (HVDC) transmission, active power conduction losses are relatively low and reactive power conduction losses are zero. One of the most important and pivotal topics for such DC grid, is the protection, for safeguarding the grid against faults [1-2]. For both levels, arcing faults or high impedance faults are difficult to detect, as it involves very little increase in fault current. In this project, theoretical investigations about dc arc fault detection methods, e.g., its requirements, state-of-the-art devices, R&D needs, etc. would be performed. This would be followed by in-depth simulation verification of the most promising methods using software like PSCAD, EMTP. Pre-requisite: Power systems, power electronics courses.

HealthBots software improvements

Supervisor

Craig Sutherland & Bruce MacDonald

Faculty of Engineering

Project code: ENG016

The Centre for Automation and Robotics Engineering Science (CARES) has been developing robots for healthcare (the HealthBots system). The robots are designed to help elderly people live at home for longer, and performs tasks like reminding about medications, exercises and daily tasks. The system consists of software running on a robot and a web-based backend for storing patient and study information. It is currently used in a number of projects in CARES.

We are looking for a student to make improvements to the software, both on the robot and the web backend. The changes are based on feedback from the current projects.

This will require a knowledge of computer programming and a willingness to learn about how robots work. Knowledge of web-based programming will be useful but not essential.

Collaborative game design tool

Supervisor

Rashina Hoda

Faculty of Engineering

Project code: ENG017

Game design typically involves multi-disciplinary teams collaborating together to brainstorm and design the story, characters, and other game elements. We’re developing a software tool to support this process. This project involves extending the tool to add more features to support collaborative design. The tool is being developed as a Windows application for the Microsoft Surface Hub. Ideal candidates will have a strong programming background, including both back and front-end. Interest in or experience of game design will be preferred.

Analysing Performance Overhead of Encryption/Decryption for Secure Memory Transactions in Multiprocessor Systems on Chip

Supervisor

Morteza Biglari-Abhari and Benjamin Tan

Faculty of Engineering

Project code: ENG018

The number of applications using network connected embedded systems are increasing rapidly to be employed in the so-called Internet of Things (IoT). However, many of such systems may process sensitive data, which require reliable and secure processing and distribution. There have been some architectural extensions to assist processors and storage systems for security improvements but using encryption/decryption algorithms at the chip level transactions in Multi-processing Systems on Chip (MPSoC) is still an effective mechanism to satisfy the system security requirements. However, this approach introduces some overhead on the processing which may not achieve the required level of performance and energy efficiency. The aim of this project is to develop a system level model to investigate performance overhead for encrypted/decrypted memory transactions in an MPSoC.

Linking heart-lung synchronization with controlled breathing and associated digital pacing devices

Supervisor

Partha Roop and Vinod Suresh

Faculty of Engineering

Project code: ENG019

Recent research findings indicate that respiratory sinus arrhythmia (RSA) is an essential component of a healthy heart. This achieves heart-lung synchronisation and may achieve cardiac efficiency (Ben Tal et al. 2014, Norgaret et al. 2014). During this project, we will examine (1) digital pacing protocols that achieves RSA in patients who have lost RSA due to disease, and (2) study the impact of pranayama i.e. specialised breathing techniques, associated with yoga and the link to RSA, if any.

Students with good background in signal processing, embedded systems are preferred. Students from biomedical engineering can also apply for this project. Software engineering students with interest in human health may also apply.

Automatic differentiation to efficiently compute sensitivities

Supervisor

Andrew Austin

Faculty of Engineering

Project code: ENG020

Automatic differentiation (AD) is a recently proposed novel technique to efficiently compute the derivative of any arbitrary function. Information about the derivatives is extremely useful in optimization and sensitivity analysis. The aim of this project is to build an AD toolbox suitable for estimating the sensitivities in numerical electromagnetics.

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