Open Research
Permanent link for this community
About
Tuwhera Open Access Research Outputs provides free access to full texts of scholarly works from AUT's Schools, Research Institutes and Centres.
AUT's research is built on a foundation of innovation and excellence, with the aim that its discoveries and applications are shared in ways that enhance wellbeing and prosperity.
Adding your outputs
AUT staff research outputs are added to this collection via Research Elements. All items submitted to this collection are checked to ensure material does not breach publisher copyright and is suitable for archiving prior to being made open access.
Find out more about making your work open access
For help with Research Elements contact the Research and Innovation Office.
Browse
Browsing Open Research by Subject "0502 Environmental Science and Management"
Now showing 1 - 20 of 20
Results Per Page
Sort Options
- ItemA Framework for Mapping and Monitoring Human-Ocean Interactions in Near Real-Time During COVID-19 and Beyond(Elsevier BV, 2022-04-16) Ward-Paige, CA; White, ER; Madin, EMP; Osgood, GJ; Bailes, LK; Bateman, RL; Belonje, E; Burns, KV; Cullain, N; Darbyshire-Jenkins, P; de Waegh, RS; Eger, AM; Fola-Matthews, L; Ford, BM; Gonson, C; Honeyman, CJ; House, JE; Jacobs, E; Jordan, LK; Levenson, JJ; Lucchini, K; Martí-Puig, MPP; McGuire, LAH; Meneses, C; Montoya-Maya, PH; Noonan, RA; Ruiz-Ruiz, PA; Ruy, PE; Saputra, RA; Shedrawi, G; Sing, B; Tietbohl, MD; Twomey, A; Florez, DV; Yamb, LThe human response to the COVID-19 pandemic set in motion an unprecedented shift in human activity with unknown long-term effects. The impacts in marine systems are expected to be highly dynamic at local and global scales. However, in comparison to terrestrial ecosystems, we are not well-prepared to document these changes in marine and coastal environments. The problems are two-fold: 1) manual and siloed data collection and processing, and 2) reliance on marine professionals for observation and analysis. These problems are relevant beyond the pandemic and are a barrier to understanding rapidly evolving blue economies, the impacts of climate change, and the many other changes our modern-day oceans are undergoing. The “Our Ocean in COVID-19″ project, which aims to track human-ocean interactions throughout the pandemic, uses the new eOceans platform (eOceans.app) to overcome these barriers. Working at local scales, a global network of ocean scientists and citizen scientists are collaborating to monitor the ocean in near real-time. The purpose of this paper is to bring this project to the attention of the marine conservation community, researchers, and the public wanting to track changes in their area. As our team continues to grow, this project will provide important baselines and temporal patterns for ocean conservation, policy, and innovation as society transitions towards a new normal. It may also provide a proof-of-concept for real-time, collaborative ocean monitoring that breaks down silos between academia, government, and at-sea stakeholders to create a stronger and more democratic blue economy with communities more resilient to ocean and global change.
- ItemA 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, AayushThis 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.
- ItemA Preliminary Investigation into the Degradation of Asbestos Fibres in Soils, Rocks and Building Materials Associated with Naturally Occurring Biofilms(MDPI, 2024-01-19) Berry, TA; Wallis, S; Doyle, E; de Lange, P; Steinhorn, G; Vigliaturo, R; Belluso, E; Blanchon, DBioremediation utilizes living organisms such as plants, microbes and their enzymatic products to reduce toxicity in xenobiotic compounds. Microbial-mediated bioremediation is cost effective and sustainable and in situ application is easily implemented. Either naturally occurring metabolic activity can be utilized during bioremediation for the degradation, transformation or accumulation of substances, or microbial augmentation with non-native species can be exploited. Despite the perceived low potential for the biological degradation of some recalcitrant compounds, successful steps towards bioremediation have been made, including with asbestos minerals, which are prevalent in building stock (created prior to the year 2000) in New Zealand. Evidence of the in situ biodegradation of asbestos fibres was investigated in samples taken from a retired asbestos mine, asbestos-contaminated soils and biofilm or lichen-covered asbestos-containing building materials. Microbial diversity within the biofilms to be associated with the asbestos-containing samples was investigated using internal transcribed spacer and 16S DNA amplicon sequencing, supplemented with isolation and culturing on agar plates. A range of fungal and bacterial species were found, including some known to produce siderophores. Changes to fibre structure and morphology were analysed using Transmission Electron Microscopy and Energy-Dispersive X-ray Spectroscopy. Chrysotile fibrils from asbestos-containing material (ACMs), asbestos-containing soils, and asbestos incorporated into lichen material showed signs of amorphisation and dissolution across their length, which could be related to biological activity.
- ItemA Unified Efficient Deep Learning Architecture for Rapid Safety Objects Classification Using Normalized Quantization-Aware Learning(MDPI, 2023-11-05) Okeke, Stephen; Nguyen, MinhThe 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.
- ItemAn 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, ArunGlobally, 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.
- ItemAssessing 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 KhanSmartphone 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.
- ItemAutomated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review(MDPI AG, 2023-06-16) Rathee, Munish; Bačić, Boris; Doborjeh, MaryamRecently, 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.
- ItemCentring Localised Indigenous Concepts of Wellbeing in Urban Nature-Based Solutions for Climate Change Adaptation: Case-Studies from Aotearoa New Zealand and the Cook Islands(Frontiers Media SA, 2024-02-02) Mihaere, Shannon; Holman-Wharehoka, Māia-te-oho; Mataroa, Jovaan; Kiddle, Gabriel Luke; Pedersen Zari, Maibritt; Blaschke, Paul; Bloomfield, SibylNature-based solutions (NbS) offer significant potential for climate change adaptation and resilience. NbS strengthen biodiversity and ecosystems, and premise approaches that centre human wellbeing. But understandings and models of wellbeing differ and continue to evolve. This paper reviews wellbeing models and thinking from Aotearoa New Zealand, with focus on Te Ao Māori (the Māori world and worldview) as well as other Indigenous models of wellbeing from wider Te Moana-nui-a-Kiwa Oceania. We highlight how holistic understandings of human-ecology-climate connections are fundamental for the wellbeing of Indigenous peoples of Te Moana-nui-a-Kiwa Oceania and that they should underpin NbS approaches in the region. We profile case study experience from Aotearoa New Zealand and the Cook Islands emerging out of the Nature-based Urban design for Wellbeing and Adaptation in Oceania (NUWAO) research project, that aims to develop nature-based urban design solutions, rooted in Indigenous knowledges that support climate change adaptation and wellbeing. We show that there is great potential for nature-based urban adaptation agendas to be more effective if linked closely to Indigenous ecological knowledge and understandings of wellbeing.
- ItemCylindrical Piezoelectric PZT Transducers for Sensing and Actuation(MDPI AG, 2023-03-11) Meshkinzar, Ata; Al-Jumaily, Ahmed MPiezoelectric 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.
- ItemDecoding 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, ABrain‐ 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.
- ItemDecoding 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, IKStroke 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.
- ItemDetection 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, MError-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.
- ItemImpact Assessment of Climate Change on Energy Performance and Thermal Load of Residential Buildings in New Zealand(Elsevier, 2023-07-17) Jalali, Z; Shamseldin, AY; Ghaffarianhoseini, AWhile it is evident that climate change will have an impact on the energy demand for heating and cooling in buildings, the exact extent of this impact is not yet fully understood. Quantification of future cooling and heating need in buildings provides a basis for taking appropriate measures for building climate change adaptation. The focus of this study is to examine how future climate change scenarios will impact the heating and cooling of residential buildings across different climatic regions in New Zealand. The future weather data under changing climate were generated for six climatic zones of New Zealand employing the statistical downscaling method. The study used various climate change scenarios, which represent concentration pathways (RCPs), to generate weather data. Specifically, the RCP8.5 and RCP4.5 scenarios were employed in the building performance simulations for different prototypes of residential buildings. The results showed there would be a significant change in the thermal performance of residential buildings, with a noticeable increase in cooling load and a decrease in heating load. These changes include a maximum thermal load change of 3 kWh/m2 in Auckland by 2090, 2.7 kWh/m2 in Hamilton, 8.3 kWh/m2 in Wellington, 4.2 kWh/m2 in Rotorua, 11 kWh/m2 in Christchurch, and 11.6 kWh/m2 in Queenstown. The warmer climatic zones are expected to change from a heating dominated to a cooling-dominated zone. The results indicated the importance of considering present and future climatic conditions in design and establishing a foundation for actions for the resilience of buildings to climate change.
- ItemImproving Urban Habitat Connectivity for Native Birds: Using Least-Cost Path Analyses to Design Urban Green Infrastructure Networks(MDPI AG, 2023-07-21) MacKinnon, M; Pedersen Zari, M; Brown, DKHabitat loss and fragmentation are primary threats to biodiversity in urban areas. Least-cost path analyses are commonly used in ecology to identify and protect wildlife corridors and stepping-stone habitats that minimise the difficulty and risk for species dispersing across human-modified landscapes. However, they are rarely considered or used in the design of urban green infrastructure networks, particularly those that include building-integrated vegetation, such as green walls and green roofs. This study uses Linkage Mapper, an ArcGIS toolbox, to identify the least-cost paths for four native keystone birds (kererū, tūī, korimako, and hihi) in Wellington, New Zealand, to design a network of green roof corridors that ease native bird dispersal. The results identified 27 least-cost paths across the central city that connect existing native forest habitats. Creating 0.7 km2 of green roof corridors along these least-cost paths reduced cost-weighted distances by 8.5–9.3% for the kererū, tūī, and korimako, but there was only a 4.3% reduction for the hihi (a small forest bird). In urban areas with little ground-level space for green infrastructure, this study demonstrates how least-cost path analyses can inform the design of building-integrated vegetation networks and quantify their impacts on corridor quality for target species in cities.
- ItemInfluence of Native and Exotic Plant Diet on the Gut Microbiome of the Gray’s Malayan Stick Insect, Lonchodes brevipes(Frontiers Media SA, 2023-07-27) Lim, YZ; Poh, YH; Lee, KC; Pointing, SB; Wainwright, BJ; Tan, EJHerbivorous insects require an active lignocellulolytic microbiome to process their diet. Stick insects (phasmids) are common in the tropics and display a cosmopolitan host plant feeding preference. The microbiomes of social insects are vertically transmitted to offspring, while for solitary species, such as phasmids, it has been assumed that microbiomes are acquired from their diet. This study reports the characterization of the gut microbiome for the Gray's Malayan stick insect, Lonchodes brevipes, reared on native and introduced species of host plants and compared to the microbiome of the host plant and surrounding soil to gain insight into possible sources of recruitment. Clear differences in the gut microbiome occurred between insects fed on native and exotic plant diets, and the native diet displayed a more species-rich fungal microbiome. While the findings suggest that phasmids may be capable of adapting their gut microbiome to changing diets, it is uncertain whether this may lead to any change in dietary efficiency or organismal fitness. Further insight in this regard may assist conservation and management decision-making.
- ItemIntegrating Energy Retrofit with Seismic Upgrades to Future-Proof Built Heritage: Case Studies of Unreinforced Masonry Buildings in Aotearoa New Zealand(Elsevier BV, 2023-06) Besen, P; Boarin, PDeep energy retrofit can improve historic buildings’ indoor environmental quality and protect them from decay and obsolescence while reducing their energy use and related greenhouse gas emissions. Although this practice has been growing internationally, in Aotearoa New Zealand there are currently no policies or initiatives to encourage energy retrofit in historic buildings and no substantial examples of projects. Most retrofits currently focus on much-needed earthquake strengthening, due to high seismic risks and national policies which mandate all existing earthquake-prone buildings to be either structurally retrofitted or demolished over the next decades. As seismic upgrade projects are widespread, this study explores the potential of applying energy retrofit concurrently with seismic strengthening, with a focus on unreinforced masonry (URM) – the main type of earthquake-prone historic construction in the country. The research investigates three case studies of listed heritage URM buildings using Post-Occupancy Evaluation and simulation. Their current performance was investigated, and retrofit scenarios were analysed through energy and hygrothermal simulation, utilising the EnerPHit standard as a guide. The energy models demonstrated a potential reduction of up to 92% in heating demand when comparing the most comprehensive retrofit scenario with the baseline in the coldest climate. The potential energy savings from each intervention were balanced against their heritage impact, based on the standard EN16883:2017. The study provides a methodology for balancing several considerations in integrated retrofit to make historic buildings more resilient not only to seismic threats, but also to a changing climate, while keeping a respectful approach to heritage.
- ItemIntegrating Spatial and Temporal Information for Violent Activity Detection from Video Using Deep Spiking Neural Networks(MDPI AG, 2023-05-06) Wang, Xiang; Yang, Jie; Kasabov, Nikola KIncreasing violence in workplaces such as hospitals seriously challenges public safety. However, it is time- and labor-consuming to visually monitor masses of video data in real time. Therefore, automatic and timely violent activity detection from videos is vital, especially for small monitoring systems. This paper proposes a two-stream deep learning architecture for video violent activity detection named SpikeConvFlowNet. First, RGB frames and their optical flow data are used as inputs for each stream to extract the spatiotemporal features of videos. After that, the spatiotemporal features from the two streams are concatenated and fed to the classifier for the final decision. Each stream utilizes a supervised neural network consisting of multiple convolutional spiking and pooling layers. Convolutional layers are used to extract high-quality spatial features within frames, and spiking neurons can efficiently extract temporal features across frames by remembering historical information. The spiking neuron-based optical flow can strengthen the capability of extracting critical motion information. This method combines their advantages to enhance the performance and efficiency for recognizing violent actions. The experimental results on public datasets demonstrate that, compared with the latest methods, this approach greatly reduces parameters and achieves higher inference efficiency with limited accuracy loss. It is a potential solution for applications in embedded devices that provide low computing power but require fast processing speeds.
- ItemOptimizing 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 JooA 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.
- ItemSingle-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, MBrain-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.
- ItemSurvey on Intrusion Detection Systems Based on Machine Learning Techniques for the Protection of Critical Infrastructure(MDPI AG, ) Pinto, Andrea; Herrera, Luis-Carlos; Donoso, Yezid; Gutierrez, Jairo AIndustrial control systems (ICSs), supervisory control and data acquisition (SCADA) systems, and distributed control systems (DCSs) are fundamental components of critical infrastructure (CI). CI supports the operation of transportation and health systems, electric and thermal plants, and water treatment facilities, among others. These infrastructures are not insulated anymore, and their connection to fourth industrial revolution technologies has expanded the attack surface. Thus, their protection has become a priority for national security. Cyber-attacks have become more sophisticated and criminals are able to surpass conventional security systems; therefore, attack detection has become a challenging area. Defensive technologies such as intrusion detection systems (IDSs) are a fundamental part of security systems to protect CI. IDSs have incorporated machine learning (ML) techniques that can deal with broader kinds of threats. Nevertheless, the detection of zero-day attacks and having technological resources to implement purposed solutions in the real world are concerns for CI operators. This survey aims to provide a compilation of the state of the art of IDSs that have used ML algorithms to protect CI. It also analyzes the security dataset used to train ML models. Finally, it presents some of the most relevant pieces of research on these topics that have been developed in the last five years.