Science and Engineering Papers

Permanent URI for this collectionhttps://researchcommons.waikato.ac.nz/handle/10289/38

This collection houses research from the School of Science and from the School of Engineering at the University of Waikato.

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On the processing of a powder metallurgy low-cost Ti alloy via different induction sintering strategies
(MDPI, 2026) Bolzoni, Leandro; Roduit, Valentin; Carreno-Morelli, Efrain; Yang, Fei; Raynova, Stiliana (Stella) Rousseva
Reducing the cost of Ti alloys, both from a processing and compositional point of view, is a pathway forward for harnessing more extensively the industrial and environmental benefits associated with their uses, and it is an outstanding manufacturing challenge. Thus, this study investigated the manufacturing of a low-cost Ti alloy using different induction sintering strategies where both aspects are designed to reduce the final cost. The primary conclusion is that induction sintering of Ti alloys by cycling the temperature above and below the allotropic phase transformation (i.e., beta transus) suffices for the need for high green density values, which are commonly achieved using more expensive methods. This is due to faster densification kinetics. The fast uniform heating of the alloys leads to homogenous pore distribution in the entire volume and the dissolution of the alloying elements, especially during cycling sintering due to the fine particle size of the alloying elements. Consequently, despite the low homologous sintering temperatures used, the induction sintered low-cost Ti alloys do not fail catastrophically, although the ability to sustain plastic deformation is affected by the features of the residual porosity.
Supercapacitor assisted multilevel inverter topology for off-grid renewable energy systems
(IEEE, 2024) Naligama, Chamila Anuradha; Kularatna, Nihal; Steyn-Ross, D. Alistair; Gunawardane, Kosala
Supercapacitor assisted (SCA) techniques are a new family of power electronic building blocks for renewable energy systems based on the theory published as SCA-loss management (SCALoM) concept. SCA inverter is a unique application of SCALoM theory, capable of meeting any capacity of off-grid inverter requirement, based on a combination of an even number of smaller inverters with suitably sized supercapacitor banks. SCA-inverter technique reduces input loop losses and transistor voltage stresses while providing DC-UPS capability without using batteries. This paper discusses the technical background of the SCA-inverter arrangement of operating seamlessly without common load balancing issues observed in other multi-inverter systems and techniques. The experiment results of two/four-SCA-inverter arrangement show stable operation of the inverter systems, both in switching transients of the supercapacitor banks and steady state operation. Four-SCA-inverter arrangement shows up to 2.5% efficiency improvement by equally partitioning the load into four inverters with no load balancing issues. As the four supercapacitor modules are switched at millihertz order frequency, it eliminaties the need for any EMC filters and dynamic losses in the frontend SCA arrangement are negligible.
Implementation of a supercapacitor assisted inverter system for reducing input loop losses: Preliminary results of a four-stage inverter
(IEEE, 2024) Naligama, Chamila Anuradha; Kularatna, Nihal; Steyn-Ross, D. Alistair; Gunawardane, Kosala
Supercapacitor assisted loss management (SCALoM) concept has laid the theoretical foundation to develop high efficiency power converters for renewable energy systems using supercapacitors as lossless voltage droppers and energy buffers. SCALoM theory is applicable to inverter combinations in reducing overall losses, mainly occurring in the input loop, including losses in the DC bus capacitor. Supercapacitor assisted (SCA) multi-inverter technique reduces the voltage stress on the internal components of inverters enabling the use of more efficient lower voltage rated components. A summary of the theoretical foundations with reference to previous works on the topic is presented first. The four-SCA-inverter technique discussed in this paper extends the input voltage range of an inverter system by two times and reduces the input loop current by two times. The preliminary results obtained from the first experimental prototype of the 700W four-SCA-inverter system coupled with four identical 250F supercapacitor modules switched at low frequencies (less than 0.02 Hz), show up to 2.53% of efficiency gain at the inverter end. Additionally, proposed technique provides unquantified efficiency gains by reducing the ripple currents in the system, functioning as an energy buffer. Development of a high power capable SCA-inverter prototype is in progress.
Effects of different triggering mechanisms on pulse shaping of a TMS pulse generator based on supercapacitor energy storage
(IEEE, 2025) Raju, Soniya; Kularatna, Nihal; Wilson, Marcus T.; Steyn-Ross, D. Alistair
High-voltage pulse generators are useful in many applications such as transient surge simulators, electric fence energizers, and transcranial magnetic stimulation (TMS) systems. Traditionally they are based on a high-voltage DC power supply with or without voltage multipliers for raising the voltage to several thousand volts. In contrast to these complex and costly approaches, we use a pre-charged supercapacitor followed by a step-up transformer and associated output circuitry to develop an inexpensive and flexible high-voltage pulse generator for applications in transcranial magnetic stimulation. Our new approach is to use the high-power delivery capability of commercial supercapacitors. In this work, we explore the advantages of different waveforms applied to the MOSFET associated with the secondary stage of the circuit topology and how they affect the final pulse shape.
Identifying biological fluids using hyperspectral near-infrared imaging and machine learning for forensics analysis
(IEEE, 2025) Faisal, Shah; Ooi, Melanie Po-Leen; Kuang, Ye Chow; Thawdar, Yaminn; Abeysekera, Sanush K.; Reutemann, Peter; Fletcher, Dale; Fleming, Rachel; McClelland, Heather
Near-infrared hyperspectral imaging captures subtle spatial and spectral characteristics of biological fluids on fabrics for forensics analysis. It is a non-destructive approach that could potentially be used for onsite crime scene investigations. However, current data processing methods lack the accuracy and sensitivity required for real-world applications. This paper utilised a 900-1700nm hyperspectral camera to capture blood, semen, and urine fluids that were systematically applied onto a patterned rayon fabric. Multi-Later Perception (MLP) and 1D Convolutional Neural Network (1D-CNN) classifiers were then trained to identify them. A dual evaluation approach, combining machine learning model evaluation metrics (F1-score and accuracy) with uncertainty quantification (Entropy-based and Modal Predictionbased) was then used to assess the performance of the classifiers. The results indicated that the MLP slightly outperformed the 1DCNN for fluid-specific classification tasks, achieving slightly better accuracy and F1-score and lower uncertainty in predictions.
Supercapacitor based TMS pulse generator design - Experimental results versus Matlab MOSFET simulation model
(IEEE, 2025) Raju, Soniya; Kularatna, Nihal; Wilson, Marcus T.; Steyn-Ross, D. Alistair
Supercapacitors are ideal for short-term energy storage due to their high instantaneous power capability. Unlike traditional high-voltage DC power supplies typically used in transcranial magnetic stimulation (TMS) applications, a precharged supercapacitor module can serve as a cost-effective alternative for building a pulse generator with adjustable waveform capabilities. This approach utilizes a supercapacitor module with a capacitor bank rated at a few hundred farads and 10 V, combined with a step-up transformer-based converter and medium-voltage MOSFETs to meet TMS requirements. The output of this pulse generator, including the effects of varying gate voltages, can be accurately predicted using a robust model. This paper presents the design of a MOSFET model, incorporating a newly derived equation that effectively captures the subthreshold, near-threshold, and above-threshold regions of the MOSFET operation. Experimental results are provided and compared with Matlab model simulations, demonstrating the model's accuracy and effectiveness.
Puarenga stream alum dosing: Effects on Lake Rotorua biota 2023
(Environmental Research Institute | Te Tumu Whakaora Taiao, The University of Waikato, 2026) Tempero, Grant Wayne; Ling, Nicholas
Following eutrophication in the second-half of the 20th century, Lake Rotorua currently experiences regular algal blooms reducing the recreational amenity of the lake. In 2007, an Action Plan was formulated to limit nutrient inputs to the lake, thereby improving water quality. One approach to reducing dissolved reactive phosphorus reaching the lake was the initiation of continuous alum dosing of the Puarenga Stream in 2010. The aluminium component of alum (aluminium sulphate) binds with dissolved phosphate making it unavailable for uptake by algae and limiting their growth. As a condition of the current resource consent RM18-0213-AP to alum dose the Puarenga Stream, monitoring surveys of lake water, common bully, plant material and macroinvertebrates must be conducted every 5 years to test for bioaccumulation of aluminium.
How positioning wearable haptic interfaces on limbs influences virtual embodiment
(IEEE, 2024) Dwivedi, Anany; Yu, Shihan; Hao, Chenxu; Salvietti, Gionata; Prattichizzo, Domenico; Beckerle, Philipp
With increasing use of computer applications and robotic devices in our everyday life, and with the advent of metaverse, there is an urgent need of developing new types of interfaces that facilitate a more intuitive interaction in physical and virtual space. In this work, we investigate the influence of the location of haptic feedback devices on embodiment of virtual hands and user load during an interactive pick-and-place task. To do this, we conducted a user study with a 3x2 repeated measure experiment design: feedback position is varied between the distal phalanx of the index finger and the thumb, the proximal phalanx of the index finger and the thumb, and the wrist. These conditions of feedback are tested with the stimuli applied synchronously to the participant in one case, and with an additional delay of 350 ms in the second case. The results show that the location of the haptic feedback device does not affect embodiment, whereas the delay, i.e., whether the feedback is applied synchronously or asynchronously, affects embodiment. This suggests that for pick-and-place tasks, haptic feedback devices can be placed on the user's wrist without compromising performance making the hands to remain free, allowing unobstructed hand visibility for precise motion tracking, thereby improving accuracy.
Leveraging large language models for automated feature extraction and model training in EMG-based motion decoding
(IEEE, 2025) Dwivedi, Anany; Guan, Bonnie; Lahr, Gustavo J. G.; Head, Mitchell A.; König, Jemma; Owen, Mahonri; Liarokapis, Minas; Bifet, Albert
Feature extraction and model training are critical steps in developing machine learning models for electromyography (EMG) based motion decoding. Traditionally, these processes require domain expertise and programming knowledge to implement signal processing algorithms with optimized model training pipelines. In this work, we investigate the feasibility of using Large Language Models (LLMs) to automate both the extraction of features from EMG data and the development of machine learning models for decoding human motion with minimal human intervention. More specifically, we compare LLM extracted features and their corresponding motion decoding models against those developed using manually developed code. Our results indicate that LLM extracted features and their corresponding trained models achieve performance comparable to traditional methods, demonstrating the potential of accelerating research and scientific investigations with AI-driven biosignal processing. This study highlights LLMs’ capabilities and limitations in replacing manual coding for developing muscle-machine interfaces and provides insights into their integration into biomedical signal analysis workflows.
On the impact of different light wavelengths in decoding human intention in lightmyography controlled prosthetic hands
(IEEE, 2025) Guan, Bonnie; Godoy, Ricardo V.; Dwivedi, Anany; Liarokapis, Minas
Lightmyography is a method of human interfacing proposed for the control of prosthetic systems in which light is reflected on the skin and captured using photosensors. While sharing conceptual similarities with forcemyography, lightmyography differentiates itself in the way it interacts with the skin due to skin optics. Different wavelengths of light interact with the skin in different depths and degrees, a principle utilized in photoplethysmography to estimate heart rate and blood oxygen levels. Therefore, lightmyography was previously designed with two wavelengths of light, green and near-infrared. This study seeks to examine the impact of using different wavelengths of light in the decoding of hand postures using different machine learning methods. Four light configurations were tested: using only green light, using only infrared light, using both green and infrared light blinking alternately each 250 ms, and finally using both lights blinking at 125 ms. Data was decoded using four machine learning algorithms, namely linear discriminant analysis, random forest, convolutional neural networks, and a transformer-based model. Statistical testing showed no significant effect from light configuration in the machine learning decoding of hand posture performance. The dataset collected in this study has been made available for further investigation.
A video dataset of everyday life grasps for the training of shared control operation models for myoelectric prosthetic hands
(IEEE, 2024) Godoy, Ricardo V.; Guan, Bonnie; Dwivedi, Anany; Owen, Mahonri; Liarokapis, Minas
The control of high degree of freedom prosthetic hands requires high cognitive effort and has proven difficult to navigate particularly as prosthetic hands become more advanced. To reduce control complexity, vision-based shared control methods have been proposed. Such methods rely on image processing and/or machine learning to identify object features and classes and introduce a level of autonomy to the grasping process. However, currently available image datasets lack focus in the area of prosthetic grasping. Thus, in this paper, we present a new dataset capturing user interactions with a wide variety of everyday life objects using a fully actuated, human-like robot hand and an onboard camera. The dataset includes videos of over 100 grasps of 50 objects from 35 classes. A video analysis has been conducted, using established grasp taxonomies to compile a list of grasp types based on interactions of the user with the objects. The resulting dataset can be used to develop more efficient prosthetic hand operation systems based on shared control frameworks.
Multi-grasp classification for the control of robot hands employing transformers and lightmyography signals
(IEEE, 2023) Godoy, Ricardo V.; Guan, Bonnie; Dwivedi, Anany; Shahmohammadi, Mojtaba; Owen, Mahonri; Liarokapis, Minas
The increasing use of smart technical devices in our everyday lives has necessitated the use of muscle-machine interfaces (MuMI) that are intuitive and that can facilitate immersive interactions with these devices. The most common method to develop MuMIs is using Electromyography (EMG) based signals. However, due to several drawbacks of EMG-based interfaces, alternative methods to develop MuMI are being explored. In our previous work, we presented a new MuMI called Lightmyography (LMG), which achieved outstanding results compared to a classic EMG-based interface in a five-gesture classification task. In this study, we extend our previous work experimentally validating the efficiency of the LMG armband in classifying thirty-two different gestures from six participants using a deep learning technique called Temporal Multi-Channel Vision Transformers (TMC-ViT). The efficiency of the proposed model was assessed using accuracy. Moreover, two different undersampling techniques are compared. The proposed thirty-two-gesture classifiers achieve accuracies as high as 92%. Finally, we employ the LMG interface in the real-time control of a robotic hand using ten different gestures, successfully reproducing several grasp types from taxonomy grasps presented in the literature.
Exploring prompt engineering for grapevine pruning decision making with limited data
(Australian Robotics & Automation Association (ARAA), 2025) He, Wanrui; McGuinness, Benjamin; Holmes, Geoffrey; Fletcher, Dale; Duke, Mike; Au, Chi Kit; Williams, Henry A. M.; Smith, David; Lim, Shen Hin
To address the challenges posed by the shortage of agricultural labour, this paper explores the use of Prompt Engineering (PE) to guide Large Language Models (LLMs) for grapevine pruning decision-making. Based on real grapevine images and structured data, five prompt templates were designed and tested on four mainstream LLMs: Gemma-3, Llama-4, Gemini 2.5 Pro, and GPT-o3. Experimental results show that some models excel in consistency—GPT-o3 achieved full consistency (1.0) under Prompt 3, and Gemma-3 performed well under Prompts 2 and 3 (0.98 and 0.96). However, inter-model agreement remained low (Fleiss’ Kappa < 0.07) across all conditions. These findings confirm the feasibility of using PE to simulate expert pruning in data-scarce environments and offer insights for building scalable, interpretable decision-support systems in agriculture.
Navigating the challenges of wearables: A case study of research-grade devices
(ACM, 2025) König, Jemma; Bidois, Ben; Manchester, Ryan; Head, Mitchell; Owen, Mahonri; Hoskens, Merel; Dwivedi, Anany; August, Luke; Fredericks, Joel; Yoo, Soojeong; Tran, Tram Thi Minh; Pantidi, Nadia; Hoang, Thuong; Hoggenmueller, Marius; Caldwell, Glenda; Tag, Benjamin; Andres, Josh; Davis, Hilary; Boden, Marie; Zhu, Howe; Harman, Joel; Rahman, Jessica
The use of wearable devices in HCI research is becoming more common. However, it is also widely accepted that there are challenges associated with the use of wearable devices. Researchers have investigated generalized challenges, and more recently, challenges associated with low-cost commercial devices. Yet, little work has been published on the challenges of working with higher-cost research-grade devices. In this paper, we use a case study to explore the challenges associated with research-grade devices, and compare these challenges with those that have been identified by other researchers. The case study involves the use of two research-grade devices to record a series of physiological data-points. The case study identified one new challenge that is associated with research-grade wearable devices (setup complexity (researcher adoption)) and highlighted similarities between generalized challenges, low-cost device challenges, and research-grade challenges. The findings from this work can be used to further investigate challenges associated with wearable devices.
More than just a margin: Local and landscape‐scale factors regulating the effects of riparian forest buffers on stream‐riparian ecosystems in urban and agricultural landscapes
(Wiley, 2026-08-17) McKie, Brendan G.; Friberg, Nikolai; Forio, Marie Anne Eurie; Kupilas, Benjamin; Mutinova, Petra T.; Sargac, Jasmina; Ramberg, Ellinor; Popescu, Cristina; Niță, Darmina; Dinu, Valentin; Sava, Mihaela; Witing, Felix; Truchy, Amélie; Kahlert, Maria; Johnson, Richard K.; Goethals, Peter; Rîşnoveanu, Geta; Volk, Martin; Burdon, Francis J.
Riparian forest buffers are a potentially potent nature-based solution for mitigating environmental degradation and improving biodiversity and ecosystem functioning in stream-riparian networks. However, uncertainties about how buffer size, spatial configuration and land-use intensity shape outcomes and trade-offs hinder management applications. We conducted coordinated field surveys and modelling across four European countries to evaluate how riparian forest properties influence stream-riparian environments, biodiversity, ecosystem processes and macroinvertebrate functional traits along agricultural and urban land-use gradients. Riparian forest buffers had strong effects on habitats, increasing shading and coarse woody debris. Sediment deposition was halved in buffered stream reaches, and longer buffers lowered thermal maxima in stream and riparian habitats by 13%–17%. Buffers further reduced effects of increasing agricultural land use on soluble reactive phosphate concentrations, although this response was highly variable. These environmental changes were associated with multiple effects on biodiversity and ecosystem function. Buffer presence and/or increasing length were associated with 30%–167% increases in microbial detritus decomposition, a 44% increase in riparian ground beetle diversity, and 18%–77% increases in stream macroinvertebrate diversity and the EPT biotic index, and macroinvertebrate leaf shredding and algal grazing traits. In contrast, algal biomass accrual and web-building spider diversity declined by 33% and 36%, respectively, in buffered reaches. As land-use intensified, riparian forest buffers helped maintain macroinvertebrate functional diversity and shredding, predation, and filtration traits, and saturated fatty acid content in terrestrial predators. In contrast, macroinvertebrate particle-gathering traits declined. The spatial configuration of riparian forest influenced stream ecosystems at local scales: dissolved phosphorus and diatom species richness declined as spatial proximity among upstream riparian forest blocks decreased, while a diatom-based biomonitoring index increased. Synthesis and applications. Riparian forest buffers support biodiversity, ecosystem functioning and food webs in human-modified landscapes, and dampen land-use and climate stressors (sediments, nutrients, thermal maxima), motivating their wider use in management. These outcomes were regulated by the presence or size of mostly small, mature buffers and sometimes by proximity among riparian forest blocks upstream, indicating that even modest investments in expanding existing buffer networks are likely to deliver cumulative ecological benefits.
Beyond living memory: Why past rainfall extremes misrepresent future risk
(University of Waikato, 2026) Harrington, Luke; McAleer, Annabel; McMaster, Rebecca
Relying on historical observations of rainfall means we underestimate the risk of future, unprecedented extreme rainfall events across New Zealand. Society tends to adapt only to the most extreme events experienced within living memory, which compounds this problem and leaves communities under-prepared. This research finds the range of plausible future rainfall events is far wider than the historical record suggests. Decision-makers must plan for extreme rainfall despite this uncertainty, with confidence that events will become more intense and frequent under climate change.
Beyond the technology: Wearables and the cultural compass
(IEEE, 2024) Owen, Mahonri; König, Jemma ; Head, Mitchell; Hoskens, Merel Cornelie Janna; Sullivan, Katie; Miller, Jadon; Keegan, Te Taka Adrian Gregory
For professionals involved in human interaction and technology usage, engaging with individuals constitutes an integral aspect of our responsibilities. When conducting any form of user study, it becomes crucial to thoroughly examine potential ethical or cultural biases that may exist within us, our data, or our methodologies. Although this consideration is not novel, the integration of wearable technology has introduced new dimensions to user studies that may not have been previously apparent. In this article, we identify cultural factors that need to be taken into account when operating in culturally significant spaces. This is achieved through a user study centred around wearable technology. Cultural considerations revolve around the awareness required for participants' cultural back-grounds and the treatment of their data within this cultural context. This article highlights the need to consider not only usability and commodification, but also the value in returning to foundational theories that underpin this research field. While many of us partake in the feast of innovation and progress, the time has come to strive for a more equitable instrumentation and measurement research landscape. We propose that in situations where cultural and racial differences influence participant expectations and technical outcomes, it is no longer acceptable or appropriate to continue in research without actively investigating the associated cultural factors.
Muscle myography for human-machine interfaces: A review of sensing modalities and control interfaces
(Frontiers, 2026) Zhuwawu, Sudhir Solomon; Owen, Mahonri; Bifet, Albert; Dwivedi, Anany
Muscle myography encompasses a family of techniques for sensing muscle activity through its electrical, mechanical, and optical manifestations, offering non-invasive and embodied pathways for human–machine interaction. Unlike traditional input devices that are hand-centric and device-bound, myography-based interfaces enable hands-free, inclusive control for prosthetics, exoskeletons, teleoperation, and immersive computing, while also opening opportunities in rehabilitation and health monitoring. This review surveys the state of muscle myography with an emphasis on its implications for human–machine interfaces (HMIs). We examine established and emerging modalities—including electromyography (EMG), mechanomyography (MMG), forcemyography (FMG), electrical impedance myography (EIM), optomyography (OMG), and the recently introduced lightmyography (LMG)—highlighting their respective strengths, weaknesses, and the trade-offs between signal quality, robustness, and wearability. We also identify recurring challenges such as limb position effects, motion artifacts, sensor reliability, and clinical acceptability, and discuss strategies such as hybrid sensing, sensor fusion, and machine learning aimed at mitigating these issues. For the HMI community, the significance of muscle myography lies not only in technical performance but also in its potential to redefine interaction design, shifting toward interfaces that operate less as external devices and more as natural extensions of the human body.
New directions and software tools within the process systems engineering ecosystem
(PSE Press, 2025) Burroughs, Stephen; Lincoln, Benjamin; Adeel, Aleeza; Severinsen, I.; Lee, A.; Amusat, O.; Gunter, D.; Nicholson, B.; Apperley, Mark; Young, B.; Siirola, J; Walmsley, Timothy G.
Process Systems Engineering (PSE) provides the advanced conceptual framework and software tools to formulate and optimise well-considered integrated solutions that could accelerate the sustainability transition within the industrial sector. The landscape of advanced PSE is poised to undertake a considerable transformation with the rise in popularity of open-source and script-based software platforms with predictive modelling capabilities based on modern mathematical optimization techniques. This paper highlights three leading equation-based platforms-IDAES, Modelica, and GEKKO-that are increasingly utilised for the modelling, simulation, and optimisation of complex systems within the advanced PSE domain, alongside the strengths and limitations of each approach. Following this, we present a framework through which emerging techniques within the domain of Software Engineering could be leveraged to address these limitations, with a vision of improving the accessibility and flexibility of complex modelling tools for industrial partners.
Systematic mapping study to assess security landscape for IoT-based smart farming systems
(Elsevier, 2026) Zahid, Farzana; Chen, Xiao; Sohail, Shaleeza; Li, Boyang; Ooi, Melanie Po-Leen
Smart farming systems sit at the intersection between three rapidly and independently advancing fields of IoT, Security, and Machine Learning. Its full realisation has tremendous positive impacts on food production; yet agricultural settings come with unique challenges that inhibit the rapid deployment of such state-of-the-art technologies. In this paper, we systematically study the current state of security for IoT-based smart farming research and development landscape and assess the proposed security solutions through the lens of technology readiness levels (TRL) and ISO/IEC 25010 security product evaluation framework. By analysing forty-eight primary studies, we identified the top security technologies under development, the critical security threats being addressed, and the most popularly used machine learning-based security solutions. Furthermore, we found that most of the ISO/IEC 25010 security characteristics considered by the security solutions are currently below TRL 6, indicating that they are well below the deployment readiness levels. Therefore, we recommend several supporting transitional technologies be developed to move the prototype development towards system validation and deployment to avoid the technology “valley of death”, such as farming-specific intrusion detection public datasets and large-scale IoT agriculture testbeds to validate the interoperability and transparency of security solutions at different layers. This systematic mapping study, together with a TRL assessment and ISO 25010 standard mapping, is the first of its kind, intending to provide a standardised comparison of the current state of security technologies for IoT-based smart farms to define a clear roadmap for future research and development. It provides a common terminology for the multidisciplinary stakeholders of smart farming to distinguish between theoretical security concepts and ready-to-deploy solutions, facilitating crucial decisions for investment, deployment, and commercialisation.

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