School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau

Permanent link for this collection

https://hdl.handle.net/10292/553
AUT is home to a number of renowned research institutes in engineering, and computer and mathematical sciences. The School of Engineering, Computer and Mathematical Sciences strong industry partnerships and the unique combination of engineering, computer and mathematical sciences within one school stimulates interdisciplinary research beyond traditional boundaries. Current research interests include:
  • Artificial Intelligence; Astronomy and Space Research;
  • Biomedical Technologies;
  • Computer Engineering; Computer Vision; Construction Management;
  • Data Science;
  • Health Informatics and eHealth;
  • Industrial Optimisation, Modelling & Control;
  • Information Security;
  • Mathematical Sciences Research; Materials & Manufacturing Technologies;
  • Networking, Instrumentation and Telecommunications;
  • Parallel and Distributed Systems; Power and Energy Engineering;
  • Software Engineering; Signal Processing; STEM Education;
  • Wireless Engineering;

Browse

Recent Submissions

Now showing 1 - 5 of 1377
  • Item
    Comprehensive Machinability Assessment of Ti6Al4V Alloy During Drilling and Helical Milling Using Sustainable Dry Condition
    (Springer Science and Business Media LLC, 2024-06-22) Hiremath, Anupama; Malghan, Rashmi L; Bolar, Gururaj; Polishetty, Ashwin
    Cutting fluids are an essential requirement while machining materials like Ti6Al4V alloy exhibiting low thermal conductivity and work hardening behavior. However, the non-biodegradable nature of the oil increases carbon emissions and causes serious health concerns, thus jeopardizing sustainability. In addition, complexity increases when drilling Ti6Al4V alloy due to the temperature build-up, leading to material adhesion and accelerated tool wear. The study, therefore, investigates the utility of helical milling for creating holes in Ti6Al4V alloy. The hole-making operations were appraised considering the chip morphology, microhardness, machining temperature, tool wear, and surface roughness. The findings show that hole-making using helical milling was beneficial since it produced lower thrust force. Measured temperatures during helical milling were significantly lower than in drilling. Helically milled holes displayed superior quality holes with lower surface roughness; however, at higher productivity conditions, chatter marks were noted. The microhardness was lower near the machined surface in the case of conventional drilling, indicating material softening. In comparison, helical milled holes displayed higher microhardness very close to the edge of the hole due to work hardening. The helical milling operation produced short discontinuous chips, which are desirable while machining Ti6Al4V alloy. Furthermore, the examination of the cutting tool showed material adhesion. The severity of tool damage was significantly lower during the helical milling operation. The initial assessment indicates that helical milling is an adept process for making holes in Ti6Al4V alloy.
  • Item
    A Lightweight Underwater Fish Image Semantic Segmentation Model Based on U-Net
    (Wiley, 2024-06-25) Zhang, Zhenkai; Li, Wanghua; Seet, Boon-Chong
    Semantic segmentation of underwater fish images is vital for monitoring fish stocks, assessing marine resources, and sustaining fisheries. To tackle challenges such as low segmentation accuracy, inadequate real-time performance, and imprecise location segmentation in current methods, a novel lightweight U-Net model is proposed. The proposed model acquires more segmentation details by applying a multiple-input approach at the first four encoder levels. To achieve both lightweight and high accuracy, a multi-scale residual structure (MRS) module is proposed to reduce parameters and compensate for the accuracy loss caused by the reduction of channels. To improve segmentation accuracy, a multi-scale skip connection (MSC) structure is further proposed, and the convolution block attention mechanism (CBAM) is introduced at the end of each decoder level for weight adjustment. Experimental results demonstrate a notable reduction in model volume, parameters, and floating-point operations by 94.20%, 94.39%, and 51.52% respectively, compared to the original model. The proposed model achieves a high mean intersection over union (mIOU) of 94.44%, mean pixel accuracy (mPA) of 97.03%, and a frame rate of 43.62 frames per second (FPS). With its high precision and minimal parameters, the model strikes a balance between accuracy and speed, making it particularly suitable for underwater image segmentation.
  • Item
    The Pressure-Volume Relationship in an Ideal Stirling Refrigerator
    (Elsevier BV, 2024-06-19) Yang, Danielle; Gschwendtner, Michael; Waleed, Aishath Zindh; Protheroe, Michael
    Hysteresis losses in the heat transfer between compressing or expanding gas and the adjacent wall is said to play an important role in Stirling machines, where it increases the amount of required p-V work. Previous studies have linked hysteresis loss with the pressure phase shift. In the context of this research, the effect of the pressure phase shift on the net p-V work in a single space is examined. A Sage model of a single space piston-cylinder device is used to investigate the underlying mechanisms of the pressure phase shift. The Sage model is validated using an experimental piston seal rig. In addition, the time dependence of heat transfer is discussed along with how it affects the pressure phase shift, using an iterative model. The Schmidt equations were manipulated to determine the phase shift between pressure and volumetric oscillation in an ideal Stirling refrigerator. The results of this investigation are surprising. It was found that even in the case of an idealized Stirling refrigerator, the phase shift between pressure and volume is non-zero in order to produce a refrigeration effect.
  • Item
    Decoding a Decade: The Evolution of Artificial Intelligence in Security, Communication, and Maintenance Within the Construction Industry
    (Elsevier BV, 2024-09) Mai, Thu Giang; Nguyen, Minh; Ghobakhlou, Akbar; Yan, Wei Qi; Chhun, Bunleng; Nguyen, Hoa
    This paper analyzes the evolution of Artificial Intelligence (AI) in the construction industry from 2014 to 2023, focusing on enhancing security, communication, and maintenance. It combines in-depth analysis of 121 papers with visualizations of 507 articles from major databases such as SCOPUS, IEEE, ACM, Science Direct, and Google Scholar to map AI advancements in construction. The study found that security is established as a mature research domain, whereas communication and maintenance are at comparatively earlier stages of development. Specifically, the analysis reveals a shift from Radio Frequency Identification (RFID) to more sophisticated technologies such as Internet of Things (IoT), Virtual Reality (VR), blockchain, Building Information Modeling (BIM), and digital twins, which significantly improve security. Communication and maintenance have also evolved towards greater digital integration and predictive analytics. The integration of AI innovations with human expertise is emphasized as a strategic direction to enhance decision-making and operational efficiency in construction.
  • Item
    Machine Learning Cryptography Methods for IoT in Healthcare
    (Springer Science and Business Media LLC, 2024-06-04) Chinbat, Tserendorj; Madanian, Samaneh; Airehrour, David; Hassandoust, Farkhondeh
    BACKGROUND: The increased application of Internet of Things (IoT) in healthcare, has fueled concerns regarding the security and privacy of patient data. Lightweight Cryptography (LWC) algorithms can be seen as a potential solution to address this concern. Due to the high variation of LWC, the primary objective of this study was to identify a suitable yet effective algorithm for securing sensitive patient information on IoT devices. METHODS: This study evaluates the performance of eight LWC algorithms-AES, PRESENT, MSEA, LEA, XTEA, SIMON, PRINCE, and RECTANGLE-using machine learning models. Experiments were conducted on a Raspberry Pi 3 microcontroller using 16 KB to 2048 KB files. Machine learning models were trained and tested for each LWC algorithm and their performance was evaluated based using precision, recall, F1-score, and accuracy metrics. RESULTS: The study analyzed the encryption/decryption execution time, energy consumption, memory usage, and throughput of eight LWC algorithms. The RECTANGLE algorithm was identified as the most suitable and efficient LWC algorithm for IoT in healthcare due to its speed, efficiency, simplicity, and flexibility. CONCLUSIONS: This research addresses security and privacy concerns in IoT healthcare and identifies key performance factors of LWC algorithms utilizing the SLR research methodology. Furthermore, the study provides insights into the optimal choice of LWC algorithm for enhancing privacy and security in IoT healthcare environments.
Items in these collections are protected by the Copyright Act 1994 (New Zealand). These works may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use:
  • Any use you make of these works must be for research or private study purposes only, and you may not make them available to any other person.
  • Authors control the copyright of their works. You will recognise the author’s right to be identified as the author of the work, and due acknowledgement will be made to the author where appropriate.
  • You will obtain the author’s permission before publishing any material from the work.