In this thesis, we develop and test a technology for computer-assisted assessments of newborn brain maturity from sleep electroencephalogram (EEG). Brain maturation of newborns is reflected in rapid development of EEG patterns over a number of weeks after conception. Observing the maturational patterns, experts can assess newborn’s EEG maturity with an accuracy ±2 weeks of newborn’s stated age. A mismatch between the EEG patterns and newborn’s physiological age alerts clinicians about possible neurological problems. Analysis of newborn EEG requires specialised skills to recognise the maturity-related waveforms and patterns and interpret them in the context of newborns age and behavioural state. It is highly desirable to make the results of maturity assessment most accurate and reliable. However, the expert analysis is limited in capability to estimate the uncertainty in assessments. To enable experts quantitatively evaluate risks of brain dysmaturity for each case, we employ the Bayesian model averaging methodology. This methodology, in theory, provides the most accurate assessments along with the estimates of uncertainty, enabling experts to take into account the full information about the risk of decision making. Such information is particularly important when assessing the EEG signals which are highly variable and corrupted by artefacts. The use of decision tree models within the Bayesian averaging enables interpreting the results as a set of rules and finding the EEG features which make the most important contribution to assessments. The developed technology was tested on approximately 1,000 EEG recordings of newborns aged 36 to 45 weeks post conception, and the accuracy of assessments was comparable to that achieved by EEG experts. In addition, it was shown that the Bayesian assessment can be used to quantitatively evaluate the risk of brain dysmaturity for each EEG recording.

Methods exploring the application of neural networks to still image compression are detailed in both the spatial and frequency domains. In particular the sparse properties of Support Vector Machine (SVM) learning are exploited in the compression algorithms. A classic radial basis function (RBF) neural network requires that the topology of the network be defined before training. An SVM has the property that it will choose the minimum number of training points to use as centres of the Gaussian kernel functions. It is this property that is exploited as the basis for image compression algorithms presented in this thesis. Several novel algorithms are developed applying SVM learning to both directly model the colour surface and model transform coefficients after the surface has been transformed into the frequency domain. It is demonstrated that compression is more efficient in frequency space. The Discrete Cosine Transform (DCT) is used to transform the colour surface into the frequency domain. A counter-intuitive result is shown where mapping the DCT coefficients to a 1-dimensional function for SVM modelling produces better results than SVM modelling of the 2-dimensional transform surface. Results are presented in comparison to the JPEG image compression algorithm. In the frequency domain, results are superior to that of JPEG. For example, the quality of the 'Lena' image compressed 63:1 for JPEG is slightly worse quality than the same image compressed 192:1 with the RKi-1 algorithm presented in this thesis. Due to the commercial value of the algorithms detailed in this thesis, a patent has been filed.

Methods exploring the application of neural networks to still image compression are detailed in both the spatial and frequency domains. In particular the sparse properties of Support Vector Machine (SVM) learning are exploited in the compression algorithms. A classic radial basis function (RBF) neural network requires that the topology of the network be defined before training. An SVM has the property that it will choose the minimum number of training points to use as centres of the Gaussian kernel functions. It is this property that is exploited as the basis for image compression algorithms presented in this thesis. Several novel algorithms are developed applying SVM learning to both directly model the colour surface and model transform coefficients after the surface has been transformed into the frequency domain. It is demonstrated that compression is more efficient in frequency space. The Discrete Cosine Transform (DCT) is used to transform the colour surface into the frequency domain. A counter-intuitive result is shown where mapping the DCT coefficients to a 1-dimensional function for SVM modelling produces better results than SVM modelling of the 2-dimensional transform surface. Results are presented in comparison to the JPEG image compression algorithm. In the frequency domain, results are superior to that of JPEG. For example, the quality of the 'Lena' image compressed 63:1 for JPEG is slightly worse quality than the same image compressed 192:1 with the RKi-1 algorithm presented in this thesis. Due to the commercial value of the algorithms detailed in this thesis, a patent has been filed.

Water is a vital element of agriculture. Almost 75% of the world's water resources are used for farm irrigation. Using greenhouses in agriculture provides a good environment for plant growth and reduces water consumption. Desalination to obtain freshwater from seawater or brackish water has been used in the arid costal regions and areas that have encountered water shortages. Solar desalination systems integrated into greenhouses have been considered for fresh water production to satisfy their water demand. Two main types of greenhouse integrated desalination systems are used, namely, solar stills and greenhouse-integrated humidification-dehumidification type solar systems. The main objective of this project is to carry out a thermodynamic analysis and a comparison of solar stills and humidification-dehumidification type desalination units. The basic principles, components, types, advantages and disadvantages of solar stills and humidification-dehumidification type greenhouse integrated desalination systems were investigated in detail. A conventional single basin type solar still that includes a basin and a symmetrical tilted condensing cover (greenhouse roof), and a humidification- dehumidification desalination unit that consists of two evaporators and one condenser were selected for detailed analysis. In order to carry out the thermal analysis, some important data such as plant transpiration and evaporation, solar radiation and indoor conditions of the greenhouse were determined. The thermal analysis was based on tomato production. Typical year ambient air temperature, relative humidity, and wind velocity values were taken from TRNSYS 16 for Adelaide conditions. In order to provide a good environment for the tomato crops, the internal conditions of the greenhouse were selected in the range 15-29oC temperature and 60-80% relative humidity. Detailed mathematical thermal models of both conventional solar stills and the new humidification-dehumidification type systems were simulated and the fresh water production of both systems was evaluated by means of MATLAB 7.8. The results were compared with previous experimental results. The results demonstrated that even if the whole roof area is used, the required fresh water supply cannot be produced in the months of May, June and July by the simple solar still system, whereas adequate amounts of fresh water can be produced throughout the year by means of humidification-dehumidification type system. On the other hand, the annual water production of the simple solar still system and humidification-dehumidification type system were determined as 308.5 and 260 m3/year respectively. The thesis also considers the option of water storage for providing water requirement of the greenhouse plants. The parameters that affect the fresh water requirement of the both systems were also considered and their impact evaluated. The effects of the desalination system on the internal environment of the greenhouse were also considered. It was revealed from the results that the use of the solar still system during the period from April to October causes unsuitable greenhouse conditions for the greenhouse crops whilst appropriate conditions for the greenhouse crops were achieved throughout the year in the case of the humidification-dehumidification type system. On these and other grounds, the humidification-dehumidification type system was found more suitable for the given greenhouse and climatic conditions. / Thesis (MEng(MechanicalEngineering)--University of South Australia, 2010

Methods exploring the application of neural networks to still image compression are detailed in both the spatial and frequency domains. In particular the sparse properties of Support Vector Machine (SVM) learning are exploited in the compression algorithms. A classic radial basis function (RBF) neural network requires that the topology of the network be defined before training. An SVM has the property that it will choose the minimum number of training points to use as centres of the Gaussian kernel functions. It is this property that is exploited as the basis for image compression algorithms presented in this thesis. Several novel algorithms are developed applying SVM learning to both directly model the colour surface and model transform coefficients after the surface has been transformed into the frequency domain. It is demonstrated that compression is more efficient in frequency space. The Discrete Cosine Transform (DCT) is used to transform the colour surface into the frequency domain. A counter-intuitive result is shown where mapping the DCT coefficients to a 1-dimensional function for SVM modelling produces better results than SVM modelling of the 2-dimensional transform surface. Results are presented in comparison to the JPEG image compression algorithm. In the frequency domain, results are superior to that of JPEG. For example, the quality of the 'Lena' image compressed 63:1 for JPEG is slightly worse quality than the same image compressed 192:1 with the RKi-1 algorithm presented in this thesis. Due to the commercial value of the algorithms detailed in this thesis, a patent has been filed.

Methods exploring the application of neural networks to still image compression are detailed in both the spatial and frequency domains. In particular the sparse properties of Support Vector Machine (SVM) learning are exploited in the compression algorithms. A classic radial basis function (RBF) neural network requires that the topology of the network be defined before training. An SVM has the property that it will choose the minimum number of training points to use as centres of the Gaussian kernel functions. It is this property that is exploited as the basis for image compression algorithms presented in this thesis. Several novel algorithms are developed applying SVM learning to both directly model the colour surface and model transform coefficients after the surface has been transformed into the frequency domain. It is demonstrated that compression is more efficient in frequency space. The Discrete Cosine Transform (DCT) is used to transform the colour surface into the frequency domain. A counter-intuitive result is shown where mapping the DCT coefficients to a 1-dimensional function for SVM modelling produces better results than SVM modelling of the 2-dimensional transform surface. Results are presented in comparison to the JPEG image compression algorithm. In the frequency domain, results are superior to that of JPEG. For example, the quality of the 'Lena' image compressed 63:1 for JPEG is slightly worse quality than the same image compressed 192:1 with the RKi-1 algorithm presented in this thesis. Due to the commercial value of the algorithms detailed in this thesis, a patent has been filed.

Virtual Reality (VR) technique has been studied and applied in a variety of academic and industrial fields. Because the advancement of the areas of Science, Technology, Engineering, and Mathematics (STEM) are important to national developments, literature on VR educational applications has focused almost exclusively on the areas of STEM. However, to date, no systematic investigation has considered the possibility of utilizing VR in painting appreciation. This researcher reconstructed a modern painting into a VR environment and employed constructivism with the model of art appreciation to investigate the effects of applying the VR technique in appreciating the painting on student cognitive and non-cognitive outcomes. Participants in the study included 60 undergraduates in the Department of Computer Graphics Technology at Purdue University. Quantitative analysis methods were used to analyze these students' responses to worksheets of painting appreciation. The findings from the research shows that under the situation without lectures/instructions: (a) the students using VR modern painting have significantly lower learning outcomes in interpreting the painting than the students using traditional 2D modern painting; (b) the students using VR modern painting have significantly higher levels of Interest/Enjoyment in motivation than the students using traditional 2D modern painting; (c) there is no significant effect of the VR modern painting on the intensity of induced emotions. This study discusses the implications of these findings and the influences of using the VR modern painting on art appreciation. Furthermore, this study not only offers the insights into the application of using VR technique in appreciating the modern painting, but also provides the recommendations for future teaching and/or learning in relevant environments or areas. Suggestions are also listed for future quantitative studies on VR application in painting appreciation.

<div>Sediment has a significant impact on social, economic, and environmental systems. With the need for an effective sediment management and monitoring system growing more important,</div><div>a method for precisely and reproducibly obtaining sediment samples that represent the actual environment is essential for water resource management and researchers across aquatic domains (such as lakes, rivers, reservoirs, mine drainage ponds, and wastewater lagoons).</div><div>Sediment sampling is usually carried out less frequently than water sampling because of the cost and labor involved. However, more frequent sediment sampling and an increase in the</div><div>range of the sampling area are necessary to more effectively monitor the ecosystem and water quality.</div><div>To fill this gap, robotic approaches for sediment sampling have been introduced. However, they are not tailored to a sediment sampling method and do not focus on the quality of</div><div>the sediment sample. Moreover, there are many challenges involved in developing such a sediment sampling system for the surface water of rivers, streams, lakes, ponds reservoirs, and lagoons. Thus, this study can be conducted to investigate to design and develop an uncrewed sediment sampling system for surface-water environments based on marine robot platforms that are capable of collecting intact sediment samples from a range of sediment types. As part of this study, an unmanned surface vehicle (USV) was used to deploy the underwater sediment sampler (USS) at the sampling locations. The USS adopted a core sampling method to collect the sediment samples. The specific requirements were integrated, taking into consideration the challenges posed by surface water and underwater environments, to design and develop an unmanned sediment sampling system.</div><div>The USV has two missions - deploying and positioning. Users can deploy the USV with the USS to the desired sampling area. Once the USV arrives, it has to maintain its position while launching the USS and during the sampling process. The USS also has two missions — launching and sampling. The USS must be a negative-buoyancy platform so it can reach the bottom and maintain its stability during sampling. To sample the sediment, the USS has to generate a sampling pattern. We defined and formulated challenges based on the missions of each platform.</div><div><div>The USV consists of three sub-systems; propulsion, launching, and monitoring system to accomplish missions. The propulsion system and launching system are necessary to accomplish deploying and positioning missions. The propulsion system is consists of two thrusters to navigate the USV. The launching system is to launch anchors for positioning and the USS for sampling. The monitoring system is to monitor and control other systems on-board via online video. The USS can generate sampling patterns based on three motions; linear, rotational, and hammering motion. We integrated servos, sensors, and mechanical components to generate three motions. The main system of the USS is completely waterproof, even for linear and rotational motion with enclosures, O-rings, and rubber bellows. Since the USS operates underwater, the water pressure causes the pressure difference between inside and outside the enclosure. We designed a pressure-equalizing system to compensate for the volume change because of sampling motions and pressure differences. Extensive field experiments were conducted to evaluate the proposed system. Users can monitor and control the system from the base station based on all data and images from each platform. The evaluation of the system is based on the data from sensors installed on each platform. Deploying and positioning missions of the USV can be shown based on the trajectory data. Launching and sampling missions of the USS can be validated based on depth, orientation, and reaction force data.</div><div>Contributions of the proposed unmanned sediment sampling system are, 1) It is the first unmanned system with a novel design to collect the less disturbed sediment samples even</div><div>from the inaccessible area and remove the potential risks of human-based sampling tasks, 2) We proposed and integrated a new sediment sampling pattern based on the sediment</div><div>sampling pattern analysis to increase the quality of sediment samples by minimizing disturbances, and 3) The proposed unmanned sediment sampling system is the first step toward the autonomous environmental monitoring system for more effective environmental monitoring.</div><div>This proposed system has many potential elements that can be a total solution for robotic environmental monitoring in addition to other features such as water sampling system, and various types of sensing system.</div></div>

This case study investigated cultural diversity discrimination claims within the Texas construction industry. The research questions guiding this study were: <div>1.To what extent is there a pattern of discrimination based on cultural differences in the workplace within the Texas construction industry? </div><div>2.What changes in legislation, policies, or practices have been proposed or enacted in Texas to address issues of cultural diversity discrimination? </div><div>Using publicly available archival data, historical documents, and court records from the EEOC, Texas Workforce Commission and Civil Rights Division, and the Texas court system, this case study examined claims involving cultural diversity discrimination over a ten-year period, from 2010 through 2020. An analysis of the data revealed a clear pattern of ongoing cultural diversity discrimination within the Texas construction industry based on race/ethnicity, color, and national origin for the entire period under investigation. This study did not find new legislation, policies, or practices enacted by the state of Texas in response to the growing discrimination claims filed against the Texas construction industry. The study culminated with recommendations for future research and proposed the Goldstein Cultural Integration Model as one of several approaches toward embracing cultural diversity in the workplace.</div>

Environmental issues have been described as one of society’s wicked problems. In contrast to widespread technological responses to environmental issues, I spotlight social aspects as chief barriers to productive change. I posit that socially constructed frames can influence people’s perspectives, opinions, and behaviors regarding environmental issues. In this project, I explored organizational work and framing processes as a means to bridge the chasm between technological and social approaches to environmental issues. To date, researchers using framing theory have narrowed their focus to testing the effectiveness of different frames. By doing so, however, researchers remain limited to discursive explanations regarding how frames are constructed at a micro level. In contrast, I adopted a multimodal approach that accounts for both discursive and non-discursive modalities to investigate how organizations deploy visual, material, and textual approaches to shape environmental meaning through framing processes. Specifically, I focused on organizational campaigns to construct meaning around the contentious issue of bottled water. I adopted a qualitative approach, using a multimodal analysis, to explore advertisements and campaigns used by bottled water companies and environmental activist groups to shape perspectives, opinions and behaviors of plastic containers and bottled water usage. I found that visual, material, and textual modalities can be used as value-neutral tools to help stakeholders construct different frames and shape the public’s opinion of bottled water. Different multimodal elements serve different functions in constructing different frames. I also identified particular barriers for the framing construction process.