Browsing by Author "Legg, Mathew"

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Acoustic non-destructive testing for wood : a thesis by publications presented in fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Albany, New Zealand
(Massey University, 2023) Abu Bakar, Adli Hasan Bin
The ability to measure the stiffness of wood is important as it can be used to determine the optimal usage of the timber sample to maximise profitability and increase sustainability. The stiffness of trees and logs is measured in order to segregate them into different grades. Stiffness measurements are also made on juvenile trees and seedlings for breeding trials to improve the stiffness quality of future plantations. The traditional static bending test is considered the gold standard for measuring the stiffness of wood. However, this method is destructive, costly and difficult to use. Non-destructive testing (NDT) techniques have therefore been developed to mitigate these issues. Acoustics is the most common NDT technique used to measure wood stiffness. The time-of-flight method is the only acoustic method which can be used on standing trees. However, literature has shown that stiffness measurements obtained using the time-of-flight method can have a significant overestimation. Studies have reported the potential causes of this overestimation but the exact cause is still not known. In recent years, NDT techniques such as guided wave techniques have been developed for other industries. Guided wave testing is extensively used on metallic structures such as pipes and bars. However, there have been very few studies that utilize guided waves for wood. This thesis investigates the use of guided wave knowledge to identify the cause of the overestimation and to obtain improved NDT measurements. This thesis contains some of the first reported works to perform guided wave measurements on cylindrical wood samples. The results from guided wave experiments show that enhancement and suppression of desired wave modes can be achieved using a ring array of shear transducers. The effects of dispersion on ToF measurements are investigated and it was found that dispersion can be a potential cause of overestimation. Guided wave techniques were developed to obtain acoustic velocity and stiffness measurements for wood. The measurements were compared with the traditional resonance, ToF and static bending methods and improved measurements were obtained. More work can be done to further develop guided wave tools and techniques to be used in the wood industry.
D2D communication based disaster response system under 5G networks : a thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy (PhD) in Computer and Electronics Engineering, Massey University, Auckland, New Zealand
(Massey University, 2023-12-14) Ahmed, Shakil
Many recent natural disasters such as tsunamis, hurricanes, volcanoes, earthquakes, etc. have led to the loss of billions of dollars, resources and human lives. These catastrophic disasters have attracted the researchers’ attention onto the significant damage to communication infrastructure. Further, communication within the first 72 hours after a disaster is critical to get help from rescuers. The advancement of wireless communication technologies, especially mobile devices and technologies, could help improve emergency communication systems. The next generation of mobile networks and technologies such as Device to Device (D2D) communication, the Internet of Things (IoT), Blockchain, and Big Data, can play significant roles in overcoming the drawbacks of the current disaster management system for data analysis and decision making. Next-generation cellular 5G and 6G network will provide several complex services for mobile phones and other communication devices. To integrate those services, the 5G cellular network will have the capabilities to handle the significant volume of data rate and the capacity to handle traffic congestion compared with the 4G or 3G cellular network. D2D communication technology, one of the major technologies in the 5G network, has the capability to exchange a high volume of traffic data directly between User Equipment (UE) without additional control from the Base Station(BS). D2D communication is used with other cell tiers in the 5G heterogeneous network (HetNet). Thus, the devices can form a cluster and cooperate with each other. As a result, the system tremendously increases network capacity as devices inside the cluster reuse the same spectrum or use an unlicensed spectrum. It will help to reduce the network’s traffic load and achieve significant throughput. D2D communication also has the ability to increase area spectral efficiency, reduce device power consumption, outage probabilities and improve network coverage. All of these characteristics are vital parameters for public safety and emergency communication applications. IoT paradigm is another promising technology with exciting features such as heterogeneity, interoperability, and flexibility. IoT has the capability to handle vast amounts of data. This huge amount of data creates Data security and data storage problems. Though, there are many technologies used to overcome the problem of validating data authenticity and data storage. Out of them, the Blockchain system is one of the emerging technologies which provides intrinsic data security. In addition, Big data technology provides data storage, modification, process, visualisation and representation in an efficient and easily understandable format. This feature is essential for disaster applications because it requires quickly collecting and processing vast amounts of data for a prompt response. Therefore, the main focus of this research work is exploring and utilising these emerging technologies (D2D, IoT, Big Data and Blockchain) and validating them with mathematical modelling for developing a disaster response system. This thesis proposes a disaster response framework by integrating the emerging technologies to overcome the problem of data communication, data security, data analysis and visualisation. Mathematical analysis and simulation models for multiple disaster sizes were developed based on D2D communication system. The result shows significant improvement in the disaster framework performance. The Quality of Services (QoS) is calculated for different scales of disaster impact. Approximately 40% disaster-affected people can get 5-10 dB and approximately 20% users get 20-25 dB Signal to Interference and Noise Ratio (SINR) when 70% infrastructure is damaged by a disaster. The network coverage increased by 25% and the network lifetime increased by 8%-14%. The research helps to develop a resilient disaster communication network which minimises the communication gap between the disaster-affected people and the rescue team. It identified the areas according to the needs of the disaster-affected people and offered a viable solution for the government and other stakeholders to visualize the disaster’s effect. This helps to make quick decisions and responses for pre and post-disaster.
Grape yield analysis with 3D cameras and ultrasonic phased arrays : a thesis by publications presented in fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Albany, New Zealand
(Massey University, 2024-01-18) Parr, Baden
Accurate and timely estimation of vineyard yield is crucial for the profitability of vineyards. It enables better management of vineyard logistics, precise application of inputs, and optimization of grape quality at harvest for higher returns. However, the traditional manual process of yield estimation is prone to errors and subjectivity. Additionally, the financial burden of this manual process often leads to inadequate sampling, potentially resulting in sub-optimal insights for vineyard management. As such, there is a growing interest in automating yield estimation using computer vision techniques and novel applications of technologies such as ultrasound. Computer vision has seen significant use in viticulture. Current state-of-the-art 2D approaches, powered by advanced object detection models, can accurately identify grape bunches and individual grapes. However, these methods are limited by the physical constraints of the vineyard environment. Challenges such as occlusions caused by foliage, estimating the hidden parts of grape bunches, and determining berry sizes and distributions still lack clear solutions. Capturing 3D information about the spatial size and position of grape berries has been presented as the next step towards addressing these issues. By using 3D information, the size of individual grapes can be estimated, the surface curvature of berries can be used as identifying features, and the position of grape bunches with respect to occlusions can be used to compute alternative perspectives or estimate occlusion ratios. Researchers have demonstrated some of this value with 3D information captured through traditional means, such as photogrammetry and lab-based laser scanners. However, these face challenges in real-world environments due to processing time and cost. Efficiently capturing 3D information is a rapidly evolving field, with recent advancements in real-time 3D camera technologies being a significant driver. This thesis presents a comprehensive analysis of the performance of available 3D camera technologies for grape yield estimation. Of the technologies tested, we determined that individual berries and concave details between neighbouring grapes were better represented by time-of-flight based technologies. Furthermore, they worked well regardless of ambient lighting conditions, including direct sunlight. However, distortions of individual grapes were observed in both ToF and LiDAR 3D scans. This is due to subsurface scattering of the emitted light entering the grapes before returning, changing the propagation time and by extension the measured distance. We exploit these distortions as unique features and present a novel solution, working in synergy with state-of-the-art 2D object detection, to find and reconstruct in 3D, grape bunches scanned in the field by a modern smartphone. An R2 value of 0.946 and an average precision of 0.970 was achieved when comparing our result to manual counts. Furthermore, our novel size estimation algorithm was able accurately to estimate berry sizes when manually compared to matching colour images. This work represents a novel and objective yield estimation tool that can be used on modern smartphones equipped with 3D cameras. Occlusion of grape bunches due to foliage remains a challenge for automating grape yield estimation using computer vision. It is not always practical or possible to move or trim foliage prior to image capture. To this end, research has started investigating alternative techniques to see through foliage-based occlusions. This thesis introduces a novel ultrasonic-based approach that is able to volumetrically visualise grape bunches directly occluded by foliage. It is achieved through the use of a highly directional ultrasonic phased array and novel signal processing techniques to produce 3D convex hulls of foliage and grape bunches. We utilise a novel approach of agitating the foliage to enable spatial variance filtering to remove leaves and highlight specific volumes that may belong to grape bunches. This technique has wide-reaching potential, in viticulture and beyond.