Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item Effects of biochar in combination with varied N inputs on grain yield, N uptake, NH3 volatilization, and N2O emission in paddy soil(Frontiers Media, 12/05/2023) Yi Z; Jeyakumar P; Yin C; Sun HBiochar application can improve crop yield, reduce ammonia (NH3) volatilization and nitrous oxide (N2O) emission from farmland. We here conducted a pot experiment to compare the effects of biochar application on rice yield, nitrogen (N) uptake, NH3 and N2O losses in paddy soil with low, medium, and high N inputs at 160 kg/ha, 200 kg/ha and 240 kg/ha, respectively. The results showed that: (1) Biochar significantly increased the rice grain yield at medium (200 kg/ha) and high (240 kg/ha) N inputs by 56.4 and 70.5%, respectively. The way to increase yield was to increase the rice N uptake, rice panicle number per pot and 1,000 grain weight by 78.5–96.5%, 6–16% and 4.4–6.1%, respectively; (2) Under low (160 kg/ha) N input, adding biochar effectively reduced the NH3 volatilization by 31.6% in rice season. The decreases of pH value and NH4+-N content in surface water, and the increases of the abundance of NH4+-N oxidizing archaea and bacteria (AOA and AOB) communities contributed to the reduction of NH3 volatilization following the biochar application; (3) Under same N input levels, the total N2O emission in rice season decreased by 43.3–73.9% after biochar addition. The decreases of nirK and nirS gene abundances but the increases of nosZ gene abundance are the main mechanisms for biochar application to reduce N2O emissions. Based on the results of the current study, adding biochar at medium (200 kg/ha) N level (N200 + BC) is the best treatment to synchronically reduce NH3 and N2O losses, improve grain yield, and reduce fertilizer application in rice production system.Item Static Hand Gesture Recognition Using Capacitive Sensing and Machine Learning(MDPI AG, 24/03/2023) Noble F; Xu M; Alam FAutomated hand gesture recognition is a key enabler of Human-to-Machine Interfaces (HMIs) and smart living. This paper reports the development and testing of a static hand gesture recognition system using capacitive sensing. Our system consists of a 6×18 array of capacitive sensors that captured five gestures-Palm, Fist, Middle, OK, and Index-of five participants to create a dataset of gesture images. The dataset was used to train Decision Tree, Naïve Bayes, Multi-Layer Perceptron (MLP) neural network, and Convolutional Neural Network (CNN) classifiers. Each classifier was trained five times; each time, the classifier was trained using four different participants' gestures and tested with one different participant's gestures. The MLP classifier performed the best, achieving an average accuracy of 96.87% and an average F1 score of 92.16%. This demonstrates that the proposed system can accurately recognize hand gestures and that capacitive sensing is a viable method for implementing a non-contact, static hand gesture recognition system.Item Multi-Layer Blockchain-Based Security Architecture for Internet of Things(MDPI (Basel, Switzerland), 2021-02) Pajooh HH; Rashid M; Alam F; Demidenko SThe proliferation of smart devices in the Internet of Things (IoT) networks creates significant security challenges for the communications between such devices. Blockchain is a decentralized and distributed technology that can potentially tackle the security problems within the 5G-enabled IoT networks. This paper proposes a Multi layer Blockchain Security model to protect IoT networks while simplifying the implementation. The concept of clustering is utilized in order to facilitate the multi-layer architecture. The K-unknown clusters are defined within the IoT network by applying techniques that utillize a hybrid Evolutionary Computation Algorithm while using Simulated Annealing and Genetic Algorithms. The chosen cluster heads are responsible for local authentication and authorization. Local private blockchain implementation facilitates communications between the cluster heads and relevant base stations. Such a blockchain enhances credibility assurance and security while also providing a network authentication mechanism. The open-source Hyperledger Fabric Blockchain platform is deployed for the proposed model development. Base stations adopt a global blockchain approach to communicate with each other securely. The simulation results demonstrate that the proposed clustering algorithm performs well when compared to the earlier reported approaches. The proposed lightweight blockchain model is also shown to be better suited to balance network latency and throughput as compared to a traditional global blockchain.Item Occluded Grape Cluster Detection and Vine Canopy Visualisation Using an Ultrasonic Phased Array(MDPI (Basel, Switzerland), 20/03/2021) Parr B; Legg M; Bradley S; Alam FGrape yield estimation has traditionally been performed using manual techniques. However, these tend to be labour intensive and can be inaccurate. Computer vision techniques have therefore been developed for automated grape yield estimation. However, errors occur when grapes are occluded by leaves, other bunches, etc. Synthetic aperture radar has been investigated to allow imaging through leaves to detect occluded grapes. However, such equipment can be expensive. This paper investigates the potential for using ultrasound to image through leaves and identify occluded grapes. A highly directional low frequency ultrasonic array composed of ultrasonic air-coupled transducers and microphones is used to image grapes through leaves. A fan is used to help differentiate between ultrasonic reflections from grapes and leaves. Improved resolution and detail are achieved with chirp excitation waveforms and near-field focusing of the array. The overestimation in grape volume estimation using ultrasound reduced from 222% to 112% compared to the 3D scan obtained using photogrammetry or from 56% to 2.5% compared to a convex hull of this 3D scan. This also has the added benefit of producing more accurate canopy volume estimations which are important for common precision viticulture management processes such as variable rate applications.Item Monitoring wellbeing during recovery from the 2010-2011 Canterbury earthquakes: The CERA wellbeing survey(Elsevier Ltd, 10/03/2015) Morgan J; Begg A; Beaven S; Schluter P; Jamieson K; Johal S; Johnston D; Sparrow MIn this paper we outline the process and outcomes of a multi-agency, multi-sector research collaboration, led by the Canterbury Earthquake Recovery Authority (CERA). The CERA Wellbeing Survey (CWS) is a serial, cross-sectional survey that is to be repeated six-monthly (in April and September) into the foreseeable future. The survey gathers self-reported wellbeing data to supplement the monitoring of the social recovery undertaken through CERA's Canterbury Wellbeing Index. Thereby informing a range of relevant agency decision-making, the CWS was also intended to provide the community and other sectors with a broad indication of how the population is tracking in the recovery. The primary objective was to ensure that decision-making was appropriately informed, with the concurrent aim of compiling a robust dataset that is of value to future researchers, and to the wider, global hazard and disaster research endeavor. The paper begins with an outline of both the Canterbury earthquake sequence, and the research context informing this collaborative project, before reporting on the methodology and significant results to date. It concludes with a discussion of both the survey results, and the collaborative process through which it was developed.Item Hyperledger Fabric Blockchain for Securing the Edge Internet of Things(MDPI (Basel, Switzerland), 7/01/2021) Pajooh HH; Rashid M; Alam F; Demidenko SProviding security and privacy to the Internet of Things (IoT) networks while achieving it with minimum performance requirements is an open research challenge. Blockchain technology, as a distributed and decentralized ledger, is a potential solution to tackle the limitations of the current peer-to-peer IoT networks. This paper presents the development of an integrated IoT system implementing the permissioned blockchain Hyperledger Fabric (HLF) to secure the edge computing devices by employing a local authentication process. In addition, the proposed model provides traceability for the data generated by the IoT devices. The presented solution also addresses the IoT systems’ scalability challenges, the processing power and storage issues of the IoT edge devices in the blockchain network. A set of built-in queries is leveraged by smart-contracts technology to define the rules and conditions. The paper validates the performance of the proposed model with practical implementation by measuring performance metrics such as transaction throughput and latency, resource consumption, and network use. The results show that the proposed platform with the HLF implementation is promising for the security of resource-constrained IoT devices and is scalable for deployment in various IoT scenarios.Item Objective measures for the assessment of post-operative pain in Bos indicus Bull calves following castration(MDPI AG, 2017-09) Musk GC; Jacobsen S; Hyndman TH; Lehmann HS; Tuke SJ; Collins T; Gleerup KB; Johnson CB; Laurence MThe aim of the study was to assess pain in Bos indicus bull calves following surgical castration. Forty-two animals were randomised to four groups: no castration (NC, n = 6); castration with pre-operative lidocaine (CL, n = 12); castration with pre-operative meloxicam (CM, n = 12); and, castration alone (C, n = 12). Bodyweight was measured regularly and pedometers provided data on activity and rest from day -7 (7 days prior to surgery) to 13. Blood was collected for the measurement of serum amyloid A (SAA), haptoglobin, fibrinogen, and iron on days 0, 3 and 6. Bodyweight and pedometry data were analysed with a mixed effect model. The blood results were analysed with repeated measure one-way analysis of variance (ANOVA). There was no treatment effect on bodyweight or activity. The duration of rest was greatest in the CM group and lowest in the C group. There was a significant increase in the concentrations of SAA, haptoglobin, and fibrinogen in all of the groups from day 0 to 3. Iron concentrations were not different at the time points it was measured. The results of this study suggest that animals rest for longer periods after the pre-operative administration of meloxicam. The other objective assessments measured in this study were not able to consistently differentiate between treatment groups.Item The productivity paradox in green buildings(Mary Ann Liebert, 8/04/2016) Rasheed EN; Byrd. HIn this paper we challenge the notion that “green” buildings can achieve greater productivity than buildings that are not accredited as “green”. For nearly two decades, research has produced apparent evidence which indicates that the design of a “green” building can enhance the productivity of its occupants. This relationship between building design and productivity is claimed to be achieved through compliance with internal environmental quality (IEQ) criteria of Green rating tools. This paper reviews methods of measuring productivity and the appropriateness of the metrics used for measuring IEQ in office environments. This review is supported by the results of a survey of office building users which identifies social factors to be significantly more important than environmental factors in trying to correlate productivity and IEQ. It also presents the findings of observations that were discretely carried out on user-response in green buildings. These findings demonstrate that, despite a building’s compliance with IEQ criteria, occupants still resort to exceptional measures to alter their working environment in a bid to achieve comfort. The work has been carried out on “green” buildings in New Zealand. These buildings are rated based on the NZ “Green Star” system which has adopted the Australian “green star” system with its roots in BREEAM. Despite this, the results of this research are applicable to many other “green” rating systems. The paper concludes that methods of measuring productivity are flawed, that IEQ criteria for building design is unrepresentative of how occupants perceive the environment and that this can lead to an architecture that has few of the inherent characteristics of good environmental design.Item Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks(MDPI AG, 23/09/2022) Konings D; Alam F; Faulkner N; de Jong CIn recent publications, capacitive sensing floors have been shown to be able to localize individuals in an unobtrusive manner. This paper demonstrates that it might be possible to utilize the walking characteristics extracted from a capacitive floor to recognize subject and gender. Several neural network-based machine learning techniques are developed for recognizing the gender and identity of a target. These algorithms were trained and validated using a dataset constructed from the information captured from 23 subjects while walking, alone, on the sensing floor. A deep neural network comprising a Bi-directional Long Short-Term Memory (BLSTM) provided the most accurate identity performance, classifying individuals with an accuracy of 98.12% on the test data. On the other hand, a Convolutional Neural Network (CNN) was the most accurate for gender recognition, attaining an accuracy of 93.3%. The neural network-based algorithms are benchmarked against Support Vector Machine (SVM), which is a classifier used in many reported works for floor-based recognition tasks. The majority of the neural networks outperform SVM across all accuracy metrics.Item Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network(MDPI AG, 11/01/2023) Ali S; Alam F; Arif K; Potgieter J-GThe advent of cost-effective sensors and the rise of the Internet of Things (IoT) presents the opportunity to monitor urban pollution at a high spatio-temporal resolution. However, these sensors suffer from poor accuracy that can be improved through calibration. In this paper, we propose to use One Dimensional Convolutional Neural Network (1DCNN) based calibration for low-cost carbon monoxide sensors and benchmark its performance against several Machine Learning (ML) based calibration techniques. We make use of three large data sets collected by research groups around the world from field-deployed low-cost sensors co-located with accurate reference sensors. Our investigation shows that 1DCNN performs consistently across all datasets. Gradient boosting regression, another ML technique that has not been widely explored for gas sensor calibration, also performs reasonably well. For all datasets, the introduction of temperature and relative humidity data improves the calibration accuracy. Cross-sensitivity to other pollutants can be exploited to improve the accuracy further. This suggests that low-cost sensors should be deployed as a suite or an array to measure covariate factors.
