Welcome to Research Commons

Research Commons is the University of Waikato's open access research repository, housing research publications and theses produced by the University's staff and students.

Communities in Research Commons

Select a community to browse its collections.

Now showing 1 - 2 of 2

Recent Submissions

Indicators of fetal engagement with the environment
(The University of Waikato, 2026) Sheik-Ebrahim, Kousar; Reid, Vincent
Fetal behaviour provides a non-invasive window into early fetal development, reflecting the maturation of sensory and motor systems. This thesis investigates the developmental trajectory of spontaneous and stimulus-evoked behaviours across late gestation (33–37 weeks) to identify behavioural indicators of selective sensory engagement. Secondary analyses were conducted on high-resolution ultrasound recordings using a systematic behavioural measurement framework, incorporating standardised coding of fetal behavioural states (FBS), fetal body movements (FBM), and fetal eye movements (FEM) to enhance observational reliability and analytical rigor. Quantitative analysis, including Dynamic Time Warping (DTW), was employed to distinguish baseline behavioural activity from responses elicited by controlled auditory and visual stimuli. Study 1 (N = 57) investigated patterns of FBM and FEM to determine whether observed behaviours reflected spontaneous activity or environmentally driven engagement. Results indicated that from approximately 33 weeks’ gestation onward, FEM classifications increasingly aligned with behavioural markers of environmental engagement, suggesting progressive maturation of sensory–behavioural integration. Study 2 (N = 24) examined fetal behavioural responses to musical stimuli varying in timbre (string versus piano) and harmonic modality (major versus minor chords). We observed distinct patterns of structured FEM and FBM across auditory conditions, indicating selective behavioural modulation in response to specific acoustic properties. The findings from this cross-sectional study suggest that late-gestation fetuses demonstrate emerging capacities for selective engagement with environmental stimuli before birth. This research advances understanding of prenatal sensory and perceptual processing and supports refinement of fetal behavioural frameworks, with implications for clinical monitoring and foundational research into early perceptual learning.
Periodic boundary conditions and G2 cosmology
(IOP Publishing, 2023) Coley, Alan A.; Lim, Woei Chet
In the standard concordance cosmology the spatial curvature is assumed to be constant and zero (or at least very small). In particular, in numerical computations of the structure of the universe using N-body simulations, exact periodic boundary conditions are assumed which constrains the spatial curvature. In order to conﬁrm this qualitatively, we numerically evolve a special class of spatially inhomogeneous G2 models with both periodic initial data and non periodic initial data using zooming techniques. We consequently demonstrate that in these models periodic initial conditions do indeed suppress the growth of the spatial curvature as the models evolve away from their initial isotropic and spatially homogeneous state, thereby verifying that the spatial curvature is necessarily very small in standard cosmology.
Automatic detection of Android crypto ransomware using supervisor reduction
(Springer Nature, 2024) Chew, Christopher J.W.; Malik, Robi; Kumar, Vimal; Patros, Panos
This paper proposes a finite-state machine based approach to recognise crypto ransomware based on their behaviour. Malicious and benign Android applications are executed to capture the system calls they generate, which are then filtered and tokenised and converted to finite-state machines. The finite-state machines are simplified using supervisor reduction, which generalises the behavioural patterns and produces compact classification models. The classification models can be implemented in a lightweight monitoring system to detect malicious behaviour of running applications quickly. An extensive set of cross validation experiments is carried out to demonstrate the viability of the approach, which show that ransomware can be classified accurately with an F1 score of up to 93.8%.
Enriching cultural heritage communities: New tools and technologies
(Oxford University Press, 2024) Dix, Alan; Jones, Elizabeth; Cowgill, Rachel; Armstrong, Charlotte; Ridgewell, Rupert; Twidale, Michael B.; Downie, J. Stephen; Reagan, Maureen; Bashford, Christina; Bainbridge, David; Neads, Carys-Ann; Davies, Vince
This paper explores ways in which scholarly skill and expertise might be embodied in tools and sustainable practices that enable communities to create and manage their own digital archives. We focus particularly on tools and practices related to the recording and annotation of digitized materials. The paper is based on co-production practice in two very different kinds of community. Although the communities are different we find that tools designed for a specific community are valuable for others, thus offering the promise of general tools to support community-centred digitization and potentially also traditional archival practice.
A multimodal bracelet to acquire muscular activity and gyroscopic data to study sensor fusion for intent detection
(MDPI, 2024) Andreas, Daniel; Hou, Zhongshi; Tabak, Mohamad Obada; Dwivedi, Anany; Beckerle, Philipp
Researchers have attempted to control robotic hands and prostheses through biosignals but could not match the human hand. Surface electromyography records electrical muscle activity using non-invasive electrodes and has been the primary method in most studies. While surface electromyography-based hand motion decoding shows promise, it has not yet met the requirements for reliable use. Combining different sensing modalities has been shown to improve hand gesture classification accuracy. This work introduces a multimodal bracelet that integrates a 24-channel force myography system with six commercial surface electromyography sensors, each containing a six-axis inertial measurement unit. The device’s functionality was tested by acquiring muscular activity with the proposed device from five participants performing five different gestures in a random order. A random forest model was then used to classify the performed gestures from the acquired signal. The results confirmed the device’s functionality, making it suitable to study sensor fusion for intent detection in future studies. The results showed that combining all modalities yielded the highest classification accuracies across all participants, reaching (Formula presented.) on average, effectively reducing misclassifications by 37% and 22% compared to using surface electromyography and force myography individually as input signals, respectively. This demonstrates the potential benefits of sensor fusion for more robust and accurate hand gesture classification and paves the way for advanced control of robotic and prosthetic hands.