AUT Faculties

Permanent link for this community

https://hdl.handle.net/10292/10755

The research activities within each of AUT’s five faculties are overseen by an Associate Dean of Research who works closely with the Pro Vice-Chancellor Research, Innovation and Enterprise. We encourage you to explore the broad suite of research activities from each faculties. Full text digital files are available open access for all items.

Browse

Now showing 1 - 20 of 22

A Metasurface-Based LTC Polarization Converter With S-shaped Split Ring Resonator Structure for Flexible Applications
(MDPI AG, 2023-07-10) Li, Erfeng; Li, Xue Jun; Seet, Boon-Chong; Ghaffar, Adnan; Aneja, Aayush
This paper presents a metasurface-based linear-to-circular polarization converter with a flexible structure for conformal and wearable applications. The converter consists of nested S- and C-shaped split ring resonators in the unit cell and can convert linearly polarized incident waves into left-handed circularly polarized ones at 12.4 GHz. Simulation results show that the proposed design has a high polarization conversion rate and efficiency at the operating frequency. Conformal tests are also conducted to evaluate the performance under curvature circumstances. A minor shift in the operating frequency is observed when the converter is applied on a sinusoidal wavy surface.
A Pilot Study Examining the Dielectric Response of Human Forearm Tissues
(MDPI AG, 2023-10-29) Yu, Yang; Kalra, Anubha Manju; Anand, Gautam; Lowe, Andrew
This work aims to describe the dielectric behaviors of four main tissues in the human forearm using mathematical modelling, including fat, muscle, blood and bone. Multi-frequency bioimpedance analysis (MF-BIA) was initially performed using the finite element method (FEM) with a 3D forearm model to estimate impedance spectra from 10 kHz to 1 MHz, followed by a pilot study involving two healthy subjects to characterize the response of actual forearm tissues from 1 kHz to 349 kHz. Both the simulation and experimental results were fitted to a single-dispersion Cole model (SDCM) and a multi-dispersion Cole model (MDCM) to determine the Cole parameters for each tissue. Cole-type responses of both simulated and actual human forearms were observed. A paired t-test based on the root mean squared error (RMSE) values indicated that both Cole models performed comparably in fitting both simulated and measured bioimpedance data. However, MDCM exhibited higher accuracy, with a correlation coefficient (R2) of 0.99 and 0.89, RMSE of 0.22 Ω and 0.56 Ω, mean difference (mean ± standard deviation) of 0.00 ± 0.23 Ω and −0.28 ± 0.23 Ω, and mean absolute error (MAE) of 0.0007 Ω and 0.2789 Ω for the real part and imaginary part of impedance, respectively. Determining the electrical response of multi-tissues can be helpful in developing physiological monitoring of an organ or a section of the human body through MF-BIA and hemodynamic monitoring by filtering out the impedance contributions from the surrounding tissues to blood-flow-induced impedance variations.
A Unified Efficient Deep Learning Architecture for Rapid Safety Objects Classification Using Normalized Quantization-Aware Learning
(MDPI, 2023-11-05) Okeke, Stephen; Nguyen, Minh
The efficient recognition and classification of personal protective equipment are essential for ensuring the safety of personnel in complex industrial settings. Using the existing methods, manually performing macro-level classification and identification of personnel in intricate spheres is tedious, time-consuming, and inefficient. The availability of several artificial intelligence models in recent times presents a new paradigm shift in object classification and tracking in complex settings. In this study, several compact and efficient deep learning model architectures are explored, and a new efficient model is constructed by fusing the learning capabilities of the individual, efficient models for better object feature learning and optimal inferencing. The proposed model ensures rapid identification of personnel in complex working environments for appropriate safety measures. The new model construct follows the contributory learning theory whereby each fussed model brings its learned features that are then combined to obtain a more accurate and rapid model using normalized quantization-aware learning. The major contribution of the work is the introduction of a normalized quantization-aware learning strategy to fuse the features learned by each of the contributing models. During the investigation, a separable convolutional driven model was constructed as a base model, and then the various efficient architectures were combined for the rapid identification and classification of the various hardhat classes used in complex industrial settings. A remarkable rapid classification and accuracy were recorded with the new resultant model.
An Adaptive Traffic-flow Management System with a Cooperative Transitional Maneuver for Vehicular Platoons
(MDPI AG, ) Hota, Lopamudra; Nayak, Biraja Prasad; Sahoo, Bibhudatta; Chong, Peter HJ; Kumar, Arun
Globally, the increases in vehicle numbers, traffic congestion, and road accidents are serious issues. Autonomous vehicles (AVs) traveling in platoons provide innovative solutions for efficient traffic flow management, especially for congestion mitigation, thus reducing accidents. In recent years, platoon-based driving, also known as vehicle platoon, has emerged as an extensive research area. Vehicle platooning reduces travel time and increases road capacity by reducing the safety distance between vehicles. For connected and automated vehicles, cooperative adaptive cruise control (CACC) systems and platoon management systems play a significant role. Platoon vehicles can maintain a closer safety distance due to CACC systems, which are based on vehicle status data obtained through vehicular communications. This paper proposes an adaptive traffic flow and collision avoidance approach for vehicular platoons based on CACC. The proposed approach considers the creation and evolution of platoons to govern the traffic flow during congestion and avoid collisions in uncertain situations. Different obstructing scenarios are identified during travel, and solutions to these challenging situations are proposed. The merge and join maneuvers are performed to help the platoon’s steady movement. The simulation results show a significant improvement in traffic flow due to the mitigation of congestion using platooning, minimizing travel time, and avoiding collisions.
Assessing Gait & Balance in Adults with Mild Balance Impairment: G&B App Reliability and Validity
(MDPI, 2023-12-09) Shafi, Hina; Awan, Waqar Ahmed; Olsen, Sharon; Siddiqi, Furqan Ahmed; Tassadaq, Naureen; Rashid, Usman; Niazi, Imran Khan
Smartphone applications (apps) that utilize embedded inertial sensors have the potential to provide valid and reliable estimations of different balance and gait parameters in older adults with mild balance impairment. This study aimed to assess the reliability, validity, and sensitivity of the Gait&Balance smartphone application (G&B App) for measuring gait and balance in a sample of middle- to older-aged adults with mild balance impairment in Pakistan. Community-dwelling adults over 50 years of age (N = 83, 50 female, range 50–75 years) with a Berg Balance Scale (BBS) score between 46/56 and 54/56 were included in the study. Data collection involved securing a smartphone to the participant’s lumbosacral spine. Participants performed six standardized balance tasks, including four quiet stance tasks and two gait tasks (walking looking straight ahead and walking with head turns). The G&B App collected accelerometry data during these tasks, and the tasks were repeated twice to assess test-retest reliability. The tasks in quiet stance were also recorded with a force plate, a gold-standard technology for measuring postural sway. Additionally, participants completed three clinical measures, the BBS, the Functional Reach Test (FRT), and the Timed Up and Go Test (TUG). Test-retest reliability within the same session was determined using intraclass correlation coefficients (ICCs) and the standard error of measurement (SEM). Validity was evaluated by correlating the G&B App outcomes against both the force plate data and the clinical measures using Pearson’s product-moment correlation coefficients. To assess the G&B App’s sensitivity to differences in balance across tasks and repetitions, one-way repeated measures analyses of variance (ANOVAs) were conducted. During quiet stance, the app demonstrated moderate reliability for steadiness on firm (ICC = 0.72) and compliant surfaces (ICC = 0.75) with eyes closed. For gait tasks, the G&B App indicated moderate to excellent reliability when walking looking straight ahead for gait symmetry (ICC = 0.65), walking speed (ICC = 0.93), step length (ICC = 0.94), and step time (ICC = 0.84). The TUG correlated with app measures under both gait conditions for walking speed (r −0.70 and 0.67), step length (r −0.56 and −0.58), and step time (r 0.58 and 0.50). The BBS correlated with app measures of walking speed under both gait conditions (r 0.55 and 0.51) and step length when walking with head turns (r = 0.53). Force plate measures of total distance wandered showed adequate to excellent correlations with G&B App measures of steadiness. Notably, G&B App measures of walking speed, gait symmetry, step length, and step time, were sensitive to detecting differences in performance between standard walking and the more difficult task of walking with head turns. This study demonstrates the G&B App’s potential as a reliable and valid tool for assessing some gait and balance parameters in middle-to-older age adults, with promise for application in low-income countries like Pakistan. The app’s accessibility and accuracy could enhance healthcare services and support preventive measures related to fall risk.
Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review
(MDPI AG, 2023-06-16) Rathee, Munish; Bačić, Boris; Doborjeh, Maryam
Recently, there has been a substantial increase in the development of sensor technology. As enabling factors, computer vision (CV) combined with sensor technology have made progress in applications intended to mitigate high rates of fatalities and the costs of traffic-related injuries. Although past surveys and applications of CV have focused on subareas of road hazards, there is yet to be one comprehensive and evidence-based systematic review that investigates CV applications for Automated Road Defect and Anomaly Detection (ARDAD). To present ARDAD’s state-of-the-art, this systematic review is focused on determining the research gaps, challenges, and future implications from selected papers (N = 116) between 2000 and 2023, relying primarily on Scopus and Litmaps services. The survey presents a selection of artefacts, including the most popular open-access datasets (D = 18), research and technology trends that with reported performance can help accelerate the application of rapidly advancing sensor technology in ARDAD and CV. The produced survey artefacts can assist the scientific community in further improving traffic conditions and safety.
Cylindrical Piezoelectric PZT Transducers for Sensing and Actuation
(MDPI AG, 2023-03-11) Meshkinzar, Ata; Al-Jumaily, Ahmed M
Piezoelectric transducers have numerous applications in a wide range of sensing and actuation applications. Such a variety has resulted in continuous research into the design and development of these transducers, including but not limited to their geometry, material and configuration. Among these, cylindrical-shaped piezoelectric PZT transducers with superior features are suitable for various sensor or actuator applications. However, despite their strong potential, they have not been thoroughly investigated and fully established. The aim of this paper is to shed light on various cylindrical piezoelectric PZT transducers, their applications and design configurations. Based on the latest literature, different design configurations such as stepped-thickness cylindrical transducers and their potential application areas will be elaborated on to propose future research trends for introducing new configurations that meet the requirements for biomedical applications, the food industry, as well as other industrial fields.
Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography
(MDPI AG, 2020-11-26) Jochumsen, M; Niazi, IK; Rehman, MZU; Amjad, I; Shafique, M; Gilani, SO; Waris, A
Brain‐ and muscle‐triggered exoskeletons have been proposed as a means for motor training after a stroke. With the possibility of performing different movement types with an exoskeleton, it is possible to introduce task variability in training. It is difficult to decode different movement types simultaneously from brain activity, but it may be possible from residual muscle activity that many patients have or quickly regain. This study investigates whether nine different motion classes of the hand and forearm could be decoded from forearm EMG in 15 stroke patients. This study also evaluates the test‐retest reliability of a classical, but simple, classifier (linear discriminant analysis) and advanced, but more computationally intensive, classifiers (autoencoders and convolutional neural networks). Moreover, the association between the level of motor impairment and classification accuracy was tested. Three channels of surface EMG were recorded during the following motion classes: Hand Close, Hand Open, Wrist Extension, Wrist Flexion, Supination, Pronation, Lateral Grasp, Pinch Grasp, and Rest. Six repetitions of each motion class were performed on two different days. Hudgins time‐domain features were extracted and classified using linear discriminant analysis and autoencoders, and raw EMG was classified with convolutional neural networks. On average, 79 ± 12% and 80 ± 12% (autoencoders) of the movements were correctly classified for days 1 and 2, respectively, with an intraclass correlation coefficient of 0.88. No association was found between the level of motor impairment and classification accuracy (Spearman correlation: 0.24). It was shown that nine motion classes could be decoded from residual EMG, with autoencoders being the best classification approach, and that the results were reliable across days; this may have implications for the development of EMG‐controlled exoskeletons for training in the patient’s home.
Decoding of Ankle Joint Movements in Stroke Patients Using Surface Electromyography
(MDPI AG, 2021-02-24) Noor, A; Waris, A; Gilani, SO; Kashif, AS; Jochumsen, M; Iqbal, J; Niazi, IK
Stroke is a cerebrovascular disease (CVD), which results in hemiplegia, paralysis, or death. Conventionally, a stroke patient requires prolonged sessions with physical therapists for the recovery of motor function. Various home-based rehabilitative devices are also available for upper limbs and require minimal or no assistance from a physiotherapist. However, there is no clinically proven device available for functional recovery of a lower limb. In this study, we explored the potential use of surface electromyography (sEMG) as a controlling mechanism for the development of a home-based lower limb rehabilitative device for stroke patients. In this experiment, three channels of sEMG were used to record data from 11 stroke patients while performing ankle joint movements. The movements were then decoded from the sEMG data and their correlation with the level of motor impairment was investigated. The impairment level was quantified using the Fugl-Meyer Assessment (FMA) scale. During the analysis, Hudgins time-domain features were extracted and classified using linear discriminant analysis (LDA) and artificial neural network (ANN). On average, 63.86% ± 4.3% and 67.1% ± 7.9% of the movements were accurately classified in an offline analysis by LDA and ANN, respectively. We found that in both classifiers, some motions outperformed oth-ers (p < 0.001 for LDA and p = 0.014 for ANN). The Spearman correlation (ρ) was calculated between the FMA scores and classification accuracies. The results indicate that there is a moderately positive correlation (ρ = 0.75 for LDA and ρ = 0.55 for ANN) between the two of them. The findings of this study suggest that a home-based EMG system can be developed to provide customized therapy for the improvement of functional lower limb motion in stroke patients.
Deploying Wireless Sensor Networks in Multi-story Buildings Towards IoT-Based Intelligent Environments: An Empirical Study
(MDPI, 2024-05-25) Sarkar, Nurul I; Gul, S
With the growing integration of the Internet of Things in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of WSNs in a commercial multi-story building, specifically addressing the difficulty of dealing with the variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights into the potential to significantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system’s performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. We provide an in-depth analysis for scrutinizing the disparities and commonalities between the datasets obtained from real-world deployments and simulation. The results obtained show the significance of accurate simulation models for reliable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. This research’s findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental parameters. This includes insights into temperature, humidity, and light intensity, offering opportunities for enhanced comfort and efficiency in intelligent environments.
Design and Modeling of a Terahertz Transceiver for Intra- and Inter-chip Communications in Wireless Network-on-Chip Architectures
(MDPI AG, 2024-05-18) Paudel, Biswash; Li, Xue Jun; Seet, Boon-Chong
This paper addresses the increasing demand for computing power and the challenges associated with adding more core units to a computer processor. It explores the utilization of System-on-Chip (SoC) technology, which integrates Terahertz (THz) wave communication capabilities for intra- and inter-chip communication, using the concept of Wireless Network-on-Chips (WNoCs). Various types of network topologies are discussed, along with the disadvantages of wired networks. We explore the idea of applying wireless connections among cores and across the chip. Additionally, we describe the WNoC architecture, the flip-chip package, and the THz antenna. Electromagnetic fields are analyzed using a full-wave simulation software, Ansys High Frequency Structure Simulator (HFSS). The simulation is conducted with dipole and zigzag antennas communicating within the chip at resonant frequencies of 446 GHz and 462.5 GHz, with transmission coefficients of around -28 dB and -33 to -41 dB, respectively. Transmission coefficient characterization, path loss analysis, a study of electric field distribution, and a basic link budget for transmission are provided. Furthermore, the feasibility of calculated transmission power is validated in cases of high insertion loss, ensuring that the achieved energy expenditure is less than 1 pJ/bit. Finally, employing a similar setup, we study intra-chip communication using the same antennas. Simulation results indicate that the zigzag antenna exhibits a higher electric field magnitude compared with the dipole antenna across the simulated chip structure. We conclude that transmission occurs through reflection from the ground plane of a printed circuit board (PCB), as evidenced by the electric field distribution.
Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network
(MDPI AG, 2021-09-18) Usama, N; Niazi, IK; Dremstrup, K; Jochumsen, M
Error-related potentials (ErrPs) have been proposed as a means for improving brain– computer interface (BCI) performance by either correcting an incorrect action performed by the BCI or label data for continuous adaptation of the BCI to improve the performance. The latter approach could be relevant within stroke rehabilitation where BCI calibration time could be minimized by using a generalized classifier that is continuously being individualized throughout the rehabilitation session. This may be achieved if data are correctly labelled. Therefore, the aims of this study were: (1) classify single-trial ErrPs produced by individuals with stroke, (2) investigate test–retest reliability, and (3) compare different classifier calibration schemes with different classification methods (artificial neural network, ANN, and linear discriminant analysis, LDA) with waveform features as input for meaningful physiological interpretability. Twenty-five individuals with stroke operated a sham BCI on two separate days where they attempted to perform a movement after which they received feedback (error/correct) while continuous EEG was recorded. The EEG was divided into epochs: ErrPs and NonErrPs. The epochs were classified with a multi-layer perceptron ANN based on temporal features or the entire epoch. Additionally, the features were classified with shrinkage LDA. The features were waveforms of the ErrPs and NonErrPs from the sensorimotor cortex to improve the explainability and interpretation of the output of the classifiers. Three calibration schemes were tested: within-day, between-day, and across-participant. Using within-day calibration, 90% of the data were correctly classified with the entire epoch as input to the ANN; it decreased to 86% and 69% when using temporal features as input to ANN and LDA, respectively. There was poor test–retest reliability between the two days, and the other calibration schemes led to accuracies in the range of 63–72% with LDA performing the best. There was no association between the individuals’ impairment level and classification accuracies. The results show that ErrPs can be classified in individuals with stroke, but that user-and session-specific calibration is needed for optimal ErrP decoding with this approach. The use of ErrP/NonErrP waveform features makes it possible to have a physiological meaningful interpretation of the output of the classifiers. The results may have implications for labelling data continuously in BCIs for stroke rehabilitation and thus potentially improve the BCI performance.
Geometric Implications of Photodiode Arrays on Received Power Distribution in Mobile Underwater Optical Wireless Communication
(MDPI AG, 2024-05-28) Govinda Waduge, Tharuka; Seet, Boon-Chong; Vopel, Kay
Underwater optical wireless communication (UOWC) has gained interest in recent years with the introduction of autonomous and remotely operated mobile systems in blue economic ventures such as offshore food production and energy generation. Here, we devised a model for estimating the received power distribution of diffused line-of-sight mobile optical links, accommodating irregular intensity distributions beyond the beam-spread angle of the emitter. We then used this model to conduct a spatial analysis investigating the parametric influence of the placement, orientation, and angular spread of photodiodes in array-based receivers on the mobile UOWC links in different Jerlov seawater types. It revealed that flat arrays were best for links where strict alignment could be maintained, whereas curved arrays performed better spatially but were not always optimal. Furthermore, utilizing two or more spectrally distinct wavelengths and more bandwidth-efficient modulation may be preferred for received-signal intensity-based localization and improving link range in clearer oceans, respectively. Considering the geometric implications of the array of receiver photodiodes for mobile UOWCs, we recommend the use of dynamically shape-shifting array geometries.
Grape Maturity Estimation Using Time-of-Flight and LiDAR Depth Cameras
(MDPI AG, 2024-08-07) Legg, Mathew; Parr, Baden; Pascual, Genevieve; Alam, Fakhrul
This article investigates the potential for using low-cost depth cameras to estimate the maturity of green table grapes after they have been harvested. Time-of-flight (Kinect Azure) and LiDAR (Intel L515) depth cameras were used to capture depth scans of green table grape berries over time. The depth scans of the grapes are distorted due to the diffused scattering of the light emitted from the cameras within the berries. This causes a distance bias where a grape berry appears to be further from the camera than it is. As the grape aged, the shape of the peak corresponding to the grape became increasingly flattened in shape, resulting in an increased distance bias over time. The distance bias variation with time was able to be fitted with an 𝑅2 value of 0.969 for the Kinect Azure and an average of 0.904 for the Intel L515. This work shows that there is potential to use time-of-flight and LIDAR cameras for estimating grape maturity postharvest in a non-contact and nondestructive manner.
Leveraging Temporal Information to Improve Machine Learning-Based Calibration Techniques for Low-Cost Air Quality Sensors
(MDPI, 2024-05-04) Ali, Sharafat; Alam, Fakhrul; Potgieter, Johan; Arif, Khalid Mahmood
Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory.
Metabolic Regulation of Copper Toxicity during Marine Mussel Embryogenesis
(MDPI AG, 2023-07-11) Young, Tim; Gale, Samantha L; Ragg, Norman LC; Sander, Sylvia G; Burritt, David J; Benedict, Billy; Le, Dung V; Villas-Bôas, Silas G; Alfaro, Andrea C
The development of new tools for assessing the health of cultured shellfish larvae is crucial for aquaculture industries to develop and refine hatchery methodologies. We established a large-volume ecotoxicology/health stressor trial, exposing mussel (Perna canaliculus) embryos to copper in the presence of ethylenediaminetetraacetic acid (EDTA). GC/MS-based metabolomics was applied to identify potential biomarkers for monitoring embryonic/larval health and to characterise mechanisms of metal toxicity. Cellular viability, developmental abnormalities, larval behaviour, mortality, and a targeted analysis of proteins involved in the regulation of reactive oxygen species were simultaneously evaluated to provide a complementary framework for interpretative purposes and authenticate the metabolomics data. Trace metal analysis and speciation modelling verified EDTA as an effective copper chelator. Toxicity thresholds for P. canaliculus were low, with 10% developmental abnormalities in D-stage larvae being recorded upon exposure to 1.10 μg·L-1 bioavailable copper for 66 h. Sublethal levels of bioavailable copper (0.04 and 1.10 μg·L-1) caused coordinated fluctuations in metabolite profiles, which were dependent on development stage, treatment level, and exposure duration. Larvae appeared to successfully employ various mechanisms involving the biosynthesis of antioxidants and a restructuring of energy-related metabolism to alleviate the toxic effects of copper on cells and developing tissues. These results suggest that regulation of trace metal-induced toxicity is tightly linked with metabolism during the early ontogenic development of marine mussels. Lethal-level bioavailable copper (50.3 μg·L-1) caused severe metabolic dysregulation after 3 h of exposure, which worsened with time, substantially delayed embryonic development, induced critical oxidative damage, initiated the apoptotic pathway, and resulted in cell/organism death shortly after 18 h of exposure. Metabolite profiling is a useful approach to (1) assess the health status of marine invertebrate embryos and larvae, (2) detect early warning biomarkers for trace metal contamination, and (3) identify novel regulatory mechanisms of copper-induced toxicity.
Metabolite Changes of Perna canaliculus Following a Laboratory Marine Heatwave Exposure: Insights from Metabolomic Analyses
(MDPI AG, 2023-07-03) Azizan, Awanis; Venter, Leonie; Jansen van Rensburg, Peet J; Ericson, Jessica A; Ragg, Norman LC; Alfaro, Andrea C
Temperature is considered to be a major abiotic factor influencing aquatic life. Marine heatwaves are emerging as threats to sustainable shellfish aquaculture, affecting the farming of New Zealand's green-lipped mussel [Perna canaliculus (Gmelin, 1791)]. In this study, P. canaliculus were gradually exposed to high-temperature stress, mimicking a five-day marine heatwave event, to better understand the effects of heat stress on the metabolome of mussels. Following liquid chromatography-tandem mass spectrometry analyses of haemolymph samples, key sugar-based metabolites supported energy production via the glycolysis pathway and TCA cycle by 24 h and 48 h of heat stress. Anaerobic metabolism also fulfilled the role of energy production. Antioxidant molecules acted within thermally stressed mussels to mitigate oxidative stress. Purine metabolism supported tissue protection and energy replenishment. Pyrimidine metabolism supported the protection of nucleic acids and protein synthesis. Amino acids ensured balanced intracellular osmolality at 24 h and ammonia detoxification at 48 h. Altogether, this work provides evidence that P. canaliculus has the potential to adapt to heat stress up to 24 °C by regulating its energy metabolism, balancing nucleotide production, and implementing oxidative stress mechanisms over time. The data reported herein can also be used to evaluate the risks of heatwaves and improve mitigation strategies for aquaculture.
Multi-Tracking Sensor Architectures for Reconstructing Autonomous Vehicle Crashes: An Exploratory Study
(MDPI AG, 2024-06-27) Haque, Mohammad Mahfuzul; Ghobakhlou, Akbar; Narayanan, Ajit
With the continuous development of new sensor features and tracking algorithms for object tracking, researchers have opportunities to experiment using different combinations. However, there is no standard or agreed method for selecting an appropriate architecture for autonomous vehicle (AV) crash reconstruction using multi-sensor-based sensor fusion. This study proposes a novel simulation method for tracking performance evaluation (SMTPE) to solve this problem. The SMTPE helps select the best tracking architecture for AV crash reconstruction. This study reveals that a radar-camera-based centralized tracking architecture of multi-sensor fusion performed the best among three different architectures tested with varying sensor setups, sampling rates, and vehicle crash scenarios. We provide a brief guideline for the best practices in selecting appropriate sensor fusion and tracking architecture arrangements, which can be helpful for future vehicle crash reconstruction and other AV improvement research.
Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks
(MDPI AG, 2024-03-22) Khan, Mohammad Usman Ali; Babar, Mohammad Inayatullah; Rehman, Saeed Ur; Komosny, Dan; Chong, Peter Han Joo
A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions.
Single-Trial Classification of Error-Related Potentials in People with Motor Disabilities: A Study in Cerebral Palsy, Stroke, and Amputees
(MDPI AG, 2022-02-21) Usama, N; Niazi, IK; Dremstrup, K; Jochumsen, M
Brain-computer interface performance may be reduced over time, but adapting the classifier could reduce this problem. Error-related potentials (ErrPs) could label data for continuous adaptation. However, this has scarcely been investigated in populations with severe motor impairments. The aim of this study was to detect ErrPs from single-trial EEG in offline analysis in participants with cerebral palsy, an amputation, or stroke, and determine how much discriminative information different brain regions hold. Ten participants with cerebral palsy, eight with an amputation, and 25 with a stroke attempted to perform 300–400 wrist and ankle movements while a sham BCI provided feedback on their performance for eliciting ErrPs. Pre-processed EEG epochs were inputted in a multi-layer perceptron artificial neural network. Each brain region was used as input individually (Frontal, Central, Temporal Right, Temporal Left, Parietal, and Occipital), the combination of the Central region with each of the adjacent regions, and all regions combined. The Frontal and Central regions were most important, and adding additional regions only improved performance slightly. The average classification accuracies were 84 ± 4%, 87± 4%, and 85 ± 3% for cerebral palsy, amputation, and stroke participants. In conclusion, ErrPs can be detected in participants with motor impairments; this may have implications for developing adaptive BCIs or automatic error correction.

Browse

Browsing AUT Faculties by Subject "0301 Analytical Chemistry"

Results Per Page

Sort Options