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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 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 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 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 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 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 The potential reduction of carbon dioxide (CO2) emissions from gas flaring in Nigeria’s oil and gas industry through alternative productive use(MDPI, 23/11/2016) Otene IJJ; Murray PE; Enongene KE; Levy, JK; Yu, PGlobally, climate change and its adverse effects on the human population and the environment has necessitated significant research on the sustainable use of natural resources. Gas flaring in Nigeria’s oil and gas industry causes environmental and health hazards and to a large extent, culminates in yearly loss of the Nation’s revenue. The aim of the study is to highlight the potentials of converting flared gas from the Nigerian oil and gas industry to compressed natural gas (CNG) which could be an alternative fuel for the 220 Lagos Bus Rapid Transit (BRT-Lite) while reducing CO2 emissions. In addition, the study provided an overview of gas flaring in the oil and gas industry and energy utilisation in some selected sectors in the country. The Long-range Energy Alternative Planning System (LEAP) software was employed to model the energy demand and carbon dioxide emissions from the BRT-Lite by creating a current scenario and projections to the year 2030. The use of CNG as an alternative fuel for Lagos BRT-Lite will significantly reduce CO2 emissions in Nigeria’s oil and gas industry. Other utilization options for flared gas from this industry includes: Liquefied Natural Gas (LNG), Liquefied Petroleum Gas (LPG), and power generationItem Analysis of Depth Cameras for Proximal Sensing of Grapes(MDPI (Basel, Switzerland), 2022-06) Parr B; Legg M; Alam FThis work investigates the performance of five depth cameras in relation to their potential for grape yield estimation. The technologies used by these cameras include structured light (Kinect V1), active infrared stereoscopy (RealSense D415), time of flight (Kinect V2 and Kinect Azure), and LiDAR (Intel L515). To evaluate their suitability for grape yield estimation, a range of factors were investigated including their performance in and out of direct sunlight, their ability to accurately measure the shape of the grapes, and their potential to facilitate counting and sizing of individual berries. The depth cameras’ performance was benchmarked using high-resolution photogrammetry scans. All the cameras except the Kinect V1 were able to operate in direct sunlight. Indoors, the RealSense D415 camera provided the most accurate depth scans of grape bunches, with a 2 mm average depth error relative to photogrammetric scans. However, its performance was reduced in direct sunlight. The time of flight and LiDAR cameras provided depth scans of grapes that had about an 8 mm depth bias. Furthermore, the individual berries manifested in the scans as pointed shape distortions. This led to an underestimation of berry sizes when applying the RANSAC sphere fitting but may help with the detection of individual berries with more advanced algorithms. Applying an opaque coating to the surface of the grapes reduced the observed distance bias and shape distortion. This indicated that these are likely caused by the cameras’ transmitted light experiencing diffused scattering within the grapes. More work is needed to investigate if this distortion can be used for enhanced measurement of grape properties such as ripeness and berry size.
