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

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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;


Recent Submissions

Now showing 1 - 5 of 1388
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    A Novel Approach to Evaluate Robotic in Vitro Chewing Effect on Food Bolus Formation Using the GLCM Image Analysis Technique
    (Institute of Electrical and Electronics Engineers (IEEE), 2024-07-02) Akarawita, Isurie; Chen, Bangxiang; Dhupia, Jaspreet Singh; Stommel, Martin; Xu, Weiliang
    In the context of food science and engineering, the in vitro chewing effect on food bolus formation is a critical area of research that explores the mechanical and textural properties of ingested materials. This article presents a pioneering approach to assess the in vitro chewing impact on food bolus formation using the gray level co-occurrence matrix (GLCM) image analysis technique. As technological advancements lead to the development of mastication robots, the need for evaluating in vitro chewed food bolus has grown. To address this challenge, a case study is conducted. The study's objectives encompass utilizing GLCM to determine the in vitro chewing cycle phase, analyzing texture features, and investigating chewing trajectory differences for beef and plant-based burger patties. Applying GLCM as a methodology, the research quantitatively analyzes textural features of food bolus formations under controlled in vitro chewing conditions. The outcomes reveal distinct differences between beef and plant-based samples through GLCM parameters. Significantly, the study identifies a consistent trend across various scenarios, indicating an increase in energy and homogeneity and a decrease in dissimilarity with an increasing number of in vitro chewing cycles. This investigation offers valuable insights into the dynamic relationship between chewing cycles and textural features in the oral processing of beef and plant-based burger patties.
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    Musical Instrument Recognition in Polyphonic Audio Through Convolutional Neural Networks and Spectrograms
    (World Academy of Science, Engineering and Technology, 2024-07-04) Rujia, Chen; Ghobakhlou, Ali; Narayanan, Ajit
    This study investigates the task of identifying musical instruments in polyphonic compositions using Convolutional Neural Networks (CNNs) from spectrogram inputs, focusing on binary classification. The model showed promising results, with an accuracy of 97% on solo instrument recognition. When applied to polyphonic combinations of 1 to 10 instruments, the overall accuracy was 64%, reflecting the increasing challenge with larger ensembles. These findings contribute to the field of Music Information Retrieval (MIR) by highlighting the potential and limitations of current approaches in handling complex musical arrangements. Future work aims to include a broader range of musical sounds, including electronic and synthetic sounds, to improve the model's robustness and applicability in real-time MIR systems.
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    Analysis of Prevalence, Socioeconomic and Disease Trends of Non-melanoma Skin Cancer in New Zealand From 2008 to 2022
    (Springer, 2024-06-06) Paul, Sharad; Chen, Yipan; Mohaghegh, Mahsa
    BACKGROUND: Skin cancer shows geographic and ethnic variation. New Zealand-with a predominantly fair-skinned populations, high UV indices and outdoor lifestyles-has high rates of skin cancer. However, population prevalence data is lacking. This study aimed to determine the demographics and socioeconomic disease trends of non-melanoma skin cancer prevalence in New Zealand from a large targeted-screening study. METHODS: A targeted screening programme was conducted among 32,839 individuals, Fitzpatrick Skin Types I to IV in Auckland, New Zealand during the 2008-2022 period. This data was analyzed retrospectively. Linear regression models were used to assess statistical trends of skin cancer prevalence over time, along with associated factors that included demographics, disease trends and overall prevalence. RESULTS: A total of 32,839 individuals were screened and 11,625 skin cancers were detected. 16,784 individuals were females who had 4,378 skin cancers. 16,055 individuals were males who had 5,777 skin cancers. 54 males and 65 females had multiple skin cancers. The article presents detailed descriptions of tumour types and subtypes detected, age groups, demographic and socioeconomic information. regarding the non-melanoma skin cancers detected. CONCLUSION: Overall men have more non-melanoma skin cancer (NMSC) than females; however females develop more BCC on the lips. BCC is three times more common in the 31-50 age group, whereas SCC are significantly more prevalent after age 80. Prevalence of BCC has not changed over the 15-year timeframe of the study but SCC has increased. Older ages and higher incomes are associated with higher rates of NMSC in New Zealand.
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    Dynamical Properties of a Small Heterogeneous Chain Network of Neurons in Discrete Time
    (Springer Science and Business Media LLC, 2024-06-24) Ghosh, I; Nair, AS; Fatoyinbo, HO; Muni, SS
    We propose a novel nonlinear bidirectionally coupled heterogeneous chain network whose dynamics evolve in discrete time. The backbone of the model is a pair of popular map-based neuron models, the Chialvo and the Rulkov maps. This model is assumed to proximate the intricate dynamical properties of neurons in the widely complex nervous system. The model is first realized via various nonlinear analysis techniques: fixed point analysis, phase portraits, Jacobian matrix, and bifurcation diagrams. We observe the coexistence of chaotic and period-4 attractors. Various codimension-1 and -2 patterns for example saddle-node, period-doubling, Neimark–Sacker, double Neimark–Sacker, flip- and fold-Neimark–Sacker, and 1 : 1 and 1 : 2 resonance are also explored. Furthermore, the study employs two synchronization measures to quantify how the oscillators in the network behave in tandem with each other over a long number of iterations. Finally, a time series analysis of the model is performed to investigate its complexity in terms of sample entropy.
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    Challenges to Sustaining Agility: An Exploratory Case Study
    (ACM, 2024-05-21) Senapathi, Mali; Strode, Diane
    Many organisations have embraced agile software development to achieve high developer productivity, improve flexibility and shorten lead times through continuous improvement. While agile principles, methods, and practices have proved highly effective for software development teams and projects, whole organisations are now basing their agile transformations on these same principles. Some of these organisations have sustained their agility over many years, requiring significant commitment and ongoing change. However, there is limited empirical research on how agility is sustained in organisations. Based on an in-depth exploratory case study involving nine interviews with experienced agile practitioners across various roles, we identify 10 challenges faced by a single organisation in its efforts to sustain agility over six years.
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