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Start date: 2021Subject: search.filters.subject.400906 Electronic sensors

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    Development of a community-engaged, low-cost earthquake early warning system using MEMS-based sensors : enhancing and adapting the PLUM algorithm with decentralised processing and P-wave integration : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Emergency Management at Massey University, Wellington, New Zealand
    (Massey University, 2025-03-04) Chandrakumar, Chanthujan
    Earthquakes pose a significant threat to people and infrastructure, particularly in regions near active faults or offshore subduction zones, which are more frequently exposed to moderate to strong shaking. Earthquake Early Warning Systems (EEWS) provide crucial alerts immediately following an earthquake, offering a warning window ranging from a few seconds to tens of seconds. These systems have effectively reduced damage and allowed individuals to take protective actions. However, the high cost of establishing high-end EEWSs makes them unaffordable for many countries. To address this, there is growing interest in using low-cost technologies such as Micro-Electromechanical Systems (MEMS)-based ground motion sensors to implement EEWSs. However, despite their potential, several knowledge gaps must be addressed to enhance their efficiency and effectiveness. Firstly, further investigation into decentralised processing for earthquake detection and alert generation is required. Traditional high-end EEWSs often rely on centralised processing units, which have proven vulnerable to critical delays and communication failures during major seismic events. Secondly, adapting and improving ground-motion-based or wave-field-based EEW algorithms is crucial for enhancing their performance, ensuring that EEWSs can provide timely and effective warnings in all regions during an earthquake, as opposed to the limitations posed by traditional source-based methods. This doctoral research addresses these gaps by developing and evaluating a community-engaged, low-cost MEMS-based EEWS. The system utilises a ground-motion-based EEW algorithm adapted for decentralised processing, enabling rapid earthquake detection. It also integrates a P-wave detection algorithm to enhance the performance of the ground-motion-based approach. Guided by the Design Science Research methodology, this study seeks to answer three key research questions: (1) How can the Propagation of Local Undamped Motion (PLUM) ground-motion-based EEW algorithm be adapted and implemented for New Zealand’s seismic conditions using decentralised processing? (2) How can high-accuracy P-wave detection be achieved in a community-engaged EEW network with high ambient noise? (3) How can the P-wave detection algorithm be integrated into the adapted PLUM algorithm to extend the warning window? The study begins with a comprehensive literature review to identify research gaps in low-cost MEMS-based EEWSs, leading to the formulation of the research questions addressed in this thesis. To answer these questions, an experimental community-engaged EEW network was implemented in Greater Wellington, NZ, using low-cost MEMS-based sensors. This implementation was followed by adapting the PLUM algorithm to NZ-PLUM, making it compatible with New Zealand’s seismic intensity by employing region-specific Ground Motion Intensity Conversion Equations. The NZ-PLUM algorithm was then integrated into a sensor network operating under a decentralised processing architecture using a two-tier communication model, ensuring rapid and reliable data transmission and processing. Building upon the implementation of the NZ-PLUM algorithm, integrating a P-wave detection algorithm into the NZ-PLUM approach was explored to extend the warning window. A performance analysis is conducted to identify the most effective P-wave detection algorithm for integration into the community-engaged EEWS. Subsequently, an empirical relationship between P-wave and S-wave amplitudes is established, leading to the development of a P-wave-based PLUM algorithm (NZ-PLUM-P), which provides an extended warning window before the onset of seismic shaking. The outcomes of this doctoral research make significant advancements in community-engaged, low-cost EEWSs. A key contribution is developing a real-life experimental EEW network using two distinct algorithms, NZ-PLUM and NZ-PLUM-P, tailored to NZ’s seismic context within a decentralised processing architecture. This study offers a versatile framework applicable to implementing community-engaged EEW networks at a low cost, making a substantial contribution to theory and practice. The methods developed for P-wave detection, constructing P-S wave amplitude relationships, executing EEW algorithms using decentralised processing and evaluating EEW network performance provide valuable tools for future research and implementation. Further, this cost-effective, community-driven model not only offers a viable solution for seismically active nations with limited resources but also has the potential to enhance the performance of existing high-end EEWS by increasing sensor density and extending warning capabilities. Providing earthquake early warnings can potentially be crucial in saving lives, protecting critical infrastructure, and enhancing public preparedness.
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    Real-time measurement of fill volume in a vessel using optical and acoustical means : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Manawatū, New Zealand. EMBARGOED until further notice.
    (Massey University, 2024) Barzegar, Mohammad Amin
    This thesis investigates optical and acoustical methods for quickly determining the fill volume in cavities, vessels, or hoppers. The motivation for this study was the demand in the New Zealand aerial topdressing industry for a system that can accurately track the fill volume of vessels containing powders. A notable challenge in this industry is that topdressing aircraft lack systems for measuring the volume of discharge vessels during flight, leading to issues with flight safety and operational efficiency. This thesis specifically addresses the challenge of real-time fill volume determination in hoppers within New Zealand's aerial topdressing industry. Additionally, the outcomes of this thesis may offer insights and applicable methods for other industrial and scientific sectors that require real-time, contactless volume determination techniques. Three contactless volume measurement approaches were investigated: ultrasonic range-finding, 3D scanning, and acoustical resonance. The first approach used an array of ultrasonic rangefinders installed in a 200-litre powder-containing vessel, resulting in material level readings from multiple points. This technique was tested under discharge and no-flow conditions. According to the results, this method provided readings of the vessel fill volume with a measurement rate of ~1 Hz and an uncertainty of ~3% of the vessel capacity. The second approach used stereoscopic technique to provide real-time scans of the material surface in the vessel. A model was developed for calculating the volume of material in the vessel using the vessel internal scans. According to the test results on different bulk materials under discharge and no flow conditions for two vessels of sizes 50 and 200 litres, the real-time fill volume of the vessel was obtained with uncertainties less than 1% of the vessel’s volume. The third approach explored Helmholtz Resonance for determining the volume of powders and solids. This involved studying the impact of inserting a sample into Helmholtz Resonators on resonance parameters. Three models were developed for volume estimation: an extended Helmholtz Resonance model modifying the classical equation for resonators with long ports, a model for estimating solid volume in powders based on resonance frequency and quality factor, and a model for instantaneous volume measurement of a vessel's empty cavity using Helmholtz Resonance. The latter correlated the change in cavity sound pressure to its volume, showing it could accurately determine volume in real-time with less than 0.1% error relative to the vessel capacity.
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    Machine learning based calibration techniques for low-cost air quality sensors : thesis for Doctor of Philosophy, Electronic and Computer Engineering, Massey University
    (Massey University, 2024-05-28) Ali, Mohammad Sharafat
    Breathable air is the single most essential element for life on earth. Polluted air poses numerous risks to health and the environment, especially in urban areas with large populations and many active sources of air pollution. Therefore, researchers from a wide range of disciplines have been working on mitigating the impact of air pollution. Monitoring ambient air pollution is one of the means to ensure public health safety, raise public awareness and build a sustainable urban environment. However, conventional air quality monitoring stations are mostly confined to a few locations due to their costly equipment and large sizes. As a result, although these monitoring stations provide accurate air pollution data, they can only offer a low-fidelity picture of air quality in a large city, leading to a poor spatial resolution of urban pollution data. Low-cost sensor (LCS) technologies aim to address this challenge and intend to make it possible to monitor air quality at a high spatio-temporal resolution. The pollutant data captured by these LCSs are less accurate than their conventional counterparts and thus require calibration techniques to improve their accuracy and reliability. Researchers have proposed different calibration methods and techniques to improve the accuracy of the LCSs, including machine learning based calibration models. This thesis investigates and proposes several machine learning-based calibration techniques and rigorously benchmarks their performance using a robust training, validation and testing method. Based on the findings, One Dimensional Convolutional Neural Network (1DCNN) and Gradient Boosting Regression (GBR) based calibration techniques provide consistently accurate performance. Both of these machine learning techniques, which have not been widely used or evaluated for low-cost ambient gas sensor calibration, can improve the state of the art. This research also demonstrates that readily available and previously unemployed co-variate data, namely the number of days the sensor has been deployed and the time of day at which the reading is taken, can significantly improve the accuracy of Machine Learning based calibration algorithms.
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    Novel visible light positioning techniques : a thesis presented in partial fulfilment of the requirements of the degree of Doctor of Philosophy in Department of Mechanical and Electrical Engineering at Massey University, Albany, New Zealand
    (Massey University, 2024-01-31) Chew, Moi Tin
    Localization is the process of finding an object’s position within the space that it is situated in. Localization can be categorised into two types, indoors and outdoors. Outdoor localization is already a matured technology which mainly relies on well-known positioning satellite systems such as Global Positioning System (GPS) and GLObal NAvigation Satellite System (GLONASS). However, the indoor localization is still a growing area of research. Visible Light Positioning (VLP) has been getting the attention of researchers due to several advantageous factors. VLP is more accurate than many of the competing techniques. As Light Emitting Diode (LED) based luminaires have become an integral part of the indoor lighting systems in modern buildings and residences, such lighting infrastructure can be leveraged for localizing objects. The VLP systems are also suitable in places like hospitals and airports due to the fact that LED does not generate electromagnetic interference which can potentially affect the operation of many equipment used in those places. This doctoral research develops novel techniques and applications for VLP, and these are fully supported by experimental results and data analysis. Fingerprinting is a common positioning method used in VLP systems that employs Received Signal Strength (RSS) as the signal characteristics. Weighted K-Nearest Neighbour (WKNN) is one of the most popular algorithms for such localization systems. This thesis investigates the impact of distance metrics used to compute the weights of the WKNN algorithm on the localization accuracy of the VLP. Experimental results show that Squared Chord distance is the most robust and accurate metric and significantly outperforms the commonly used Euclidean distance metric. Robot navigation is one of the many potential applications of VLP. Recent literature shows a small number of works on robots being controlled by fusing location information acquired by VLP that uses rolling shutter effect camera as a receiver with other sensor data. In contrast, this thesis reports the experimental performance of a cartesian robot that was controlled solely by a VLP system using a cheap photodiode-based receiver. Two different methods (Direct Method and Spring Relaxation Method) were developed to leverage the VLP as an online navigation system to control the robot. The experiments consisted of the robot autonomously repeating various paths multiple times. The results show that both methods offer promising accuracy, with Direct Method and Spring Relaxation Method reaching the target positions of median / 90-percentile error of 27.16mm / 37.04mm, and 26.05mm / 47.48mm respectively. The operation of VLP is very much dependent on the line of sight (LOS) link between the luminaires and the receiver. Unfortunately, in a practical environment, luminaires are positioned to serve illumination needs. Therefore, enough luminaires may not be visible for the purpose of positioning the target. One way to compensate this would be to utilise an ultrasound system to eliminate the “blind spots” of the VLP system. The final part of this work consists of a study of the ultrasound based indoor localization. A bespoke system employing an ultrasonic array to transmit chirp signals and time of flight measurement for ranging was developed. The position of the receiver is estimated iteratively using the spring relaxation technique. The spring relaxation technique, which has not been used for ultrasonic localization in the literature, outperforms the widely adopted linear least square-based lateration technique. The experimental results show that the ultrasonic system can be a viable option for fusing with a VLP system.
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    Thin film electrochemical sensor for water quality monitoring : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering, Massey University, Auckland, New Zealand
    (Massey University, 2023-12-11) Lal, Kartikay
    Freshwater is the most precious natural resource, essential for supporting life. Aquatic ecosystems flourish in freshwater sources, and many regions around the world depend on aquatic food sources, such as fish. Nitrogen and phosphorous are the two nutrients, in particular, that are essential for growth of aquatic plants and algae. However, with rising population and anthropogenic activities, excessive amounts of such nutrients enter our waterways through various natural processes, thereby degrading the quality of freshwater sources. Elevated levels of nitrate-nitrogen content, in particular, lead to consequences for both aquatic life as well as human health, which has been a cause for concern for many decades. As recommended by the World Health Organization, the maximum permissible nitrate level in water is 11.3 mg/L. These levels are often exceeded in coastal areas or freshwater bodies that are close to agricultural land. Therefore, it is essential to monitor nitrate levels in freshwater sources in real-time, which can be achieved by employing detection methods commonly used to detect ionic content in water. Hence, a comprehensive review was carried out on various field-deployable electrochemical and optical detection methods that could be employed for in-situ detection of nitrate ions in water. The primary focus was on electrochemical methods that could be integrated with low-cost planar electrodes to achieve targeted detection of nitrate ions in water. Designing resilient sensors for real-time monitoring of water quality is a challenging task due to the harsh environment to which they are subjected. There is a significant need for sensors with attributes such as repeatability, sensitivity, low-cost, and selectivity. These attributes were first explored by evaluating the performance of silver and copper materials on three distinct geometric patterns of electrodes. The experiments produced promising results with interdigitated pattern of copper electrodes that were successful in detecting 0.1-0.5 mg/L of nitrate ions in deionised water. The interdigitated geometric pattern of electrodes were further analyzed in four distinct materials namely, silver, gold, copper, and tin with real-world freshwater samples that were collected from three different freshwater bodies. The water samples were used to synthesize varying concentrations of nitrate ions. The results showed tin electrodes performed better over other materials for nitrate concentrations from 0.1-1 mg/L in complex matrix of real-world sample. The nitrate sensor eventually needs to be deployed in freshwater bodies, hence a real-time water quality monitoring system was also built that incorporated sensors to monitor five basic water quality parameters with the aim to monitor and study the quality of water around the local area.
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    Development of a focused ultrasonic array system for pasture biomass estimation : a thesis presented in fulfilment of the requirements for the degree of Master of Engineering at Massey University, Albany, New Zealand
    (Massey University, 2023) Jiang, Zhilin
    The ability to accurately measure pasture biomass can significantly impact the profitability of the pasture agriculture industry. One technique that has been used to estimate pasture biomass is to measure pasture height using ultrasonic transducers. It was traditionally achieved using a single ultrasonic transducer with a wide beam angle. Additionally, the previous studies using this method only used the first arrival time of the echo from the top of the grass. However, this can lead to overestimating grass height due to isolated pieces of grass, which may not be directly below the sensor. It does not measure the pasture density. Also, height measurement errors may occur when the sensor is mounted on an agricultural vehicle as the vehicle bounces and tilts. To solve these problems, Legg and Bradley developed a new ultrasonic air-coupled transducer array to estimate the biomass of pastures and achieved good experimental results. However, it was believable that measurement accuracy can be further improved using near field focusing of the transmit and receive arrays. This work describes the development of an ultrasonic array system capable of focusing on the near and far fields for pasture biomass estimation. It extended on the system developed by Legg and Bradley. Angular measurements were made with the array attached to a computer controlled turntable system for different near- and far-field beamforming configurations. It was found that improved beamwidth and dynamic range were obtained when the system focused on the receiver in the near field. Some initial lab measurements were also performed on pasture samples, comparing the effect of using the array's transmit far-field and near-field focusing. The results indicate that focusing the array in the near field improves the performance in detecting the grass, particularly the top, compared with focusing the receiver in the near field and the transmitter in the far field. However, more work is needed, including field trials.
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    Human detection through sensor fusion and convolutional neural networks : a thesis presented in fulfilment of the requirements for the degree of Master of Engineering in Mechatronics at Massey University, Manawatu, New Zealand
    (Massey University, 2023) Williams, Isaac Laurie
    Safety practices in industrial environments can be improved by observing patterns of close calls or accidents. Machine detection of humans is one method that can be used to collect data for this purpose, but it has challenges achieving reliable results in complex and varied environments. Sonasafe, a company manufacturing and managing proximity safety systems for industrial environments, is looking to advance their capability to detect people. This thesis is concerned with object detection, with a specific focus on increasing the accuracy of human detection in industrial environments. Object detection requires the identification of specific, distinguishable features that can be distinctively associated with the object being detected, with the additional challenge of determining its location. The distinctiveness of an object’s features is determined by the type of object, its surroundings, and the sensor being used to detect the object. This study began with an evaluation of sensors that could contribute to distinguishing identifying human features and the mitigation of negative environmental conditions. The senses selected for testing were a standard visual camera for general feature recognition, an infrared camera for temperature features to mitigate the effects of dark environments, along with a millimetre wave (mmWave) radar to enhance location accuracy. This study reviewed the fusion of these sensors in Convolution Neural Networks to determine their respective performance in identifying different features when detecting a person in a complex environment. The study found that for a simple environment, with little noise and uniform data, single sensors had a higher rate of success, but that as the environment became more complex with more variability, the fusion of all three sensors produced more accurate results. Overall sensor fusion was seen to improve the accuracy of human detection in more complex industrial environments.
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    An integrated water quality sensing system : a review and analysis of critical parameters and an evaluation of contact and non-contact sensors : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Mechatronics at Massey University Albany, New Zealand
    (Massey University, 2021) McArdle, Eamon
    Several methodologies and standards exist for the measurement of water quality. The use of established water quality indices is embedded in these methodologies/standards and the measurement approach of these indices involves several different techniques and sensor technologies. Recent development in the field of water quality measurement has moved towards wireless sensor network systems to enable the monitoring of multiple bodies of water in any given geographical region, with most of the research focussing on the use of the Internet of Things (IoT) for the associated water quality sensing systems. There exists a small amount of research into combined sensor technologies that enable measurement simultaneously of multiple parameters. There is currently, however, no analysis available on the feasibility of developing a fully integrated system to measure all desirable water quality parameters simultaneously. Sensor solutions and analysis techniques for such a fully integrated system are therefore lacking. This research analyses common water quality measurement methods, comparing them particularly to non-contact alternatives to determine the viability of a cost effective and fully integrated water quality sensing system. In parallel it seeks to determine which types of sensors are best for effective analysis of water quality in distributed bodies of water. Literature analysis determined that a cost-effective, fully integrated water quality sensing system was feasible if the water quality parameters being measured were limited. As a result, an analysis of contact and non-contact sensors for the selected parameters was conducted. The results of this analysis were varied, and it was concluded that the types of sensor that should be used in an integrated water quality sensing system are dependent on the design of the critical parameter set being measured.