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Subject: search.filters.subject.Spectral imaging

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    CMOS-compatible nanostructured colour filters for visible and near-infrared regions : a thesis presented in total fulfillment for the degree of Doctor of Philosophy in the Department of Mechanical and Electrical Engineering, Massey University
    (Massey University, 2022) Shaukat, Ayesha
    The human eye is sensitive to electromagnetic radiations with a wavelength range from 380 to 700 nm, and the mechanism of the eye’s colour filtering is emulated in colour image sensing devices. Today, image sensing devices can detect colour with distinct hue, brightness, and saturation characteristics. In contrast, spectral imaging filters are developed to perceive electromagnetic radiations, which the human eye cannot perceive. A spectral filter should ideally have narrow bandwidth, high transmission, and independence from incident and polarization angles of incident light. Most importantly, it should be compatible with current power saving CMOS technology for high throughput and cost-effectiveness. Unfortunately, such filters do not exist, and the replacement of pigment-based colour filters is still not realized. Therefore, new designs and materials need to be explored to address the current limitations. This thesis presents an all-dielectric cascaded multilayered thin film filter for near-infrared (NIR) filtration, which is helpful for spectral imaging applications. With preference to the CMOS compatibility, the material investigation is done through rigorous numerical simulations. Furthermore, the behaviour of the device is observed by varying thicknesses of layered films. This helped in finalizing a design comprised of only eight layers, consisting of amorphous silicon (A-Si) and silicon nitride (Si3N4), deposited successively on a glass substrate. Simulation results demonstrate a distinct peak in the NIR region with a transmission efficiency of up to 70 % and full width at half maximum (FWHM) is 77 nm. The results are angle insensitive up to 60◦ and show polarization insensitivity in Transverse Magnetic (TM) and Transverse Electric (TE) modes. The design is fabricated and tested at Australian National Fabrication Facility (ANFF). The physical and optical characterization including polarization insensitivity and angle invariance of the thin films are obtained through Spectroscopic Ellipsometry (SE), which shows practical relevance to the theoretical results with angle invariance up to 50◦. However, the thicknesses obtained through Scanning Electron Microscopy (SEM) are not helpful, and show discrepancies in the simulated and experimental results. The discrepancy is accredited to the same average atomic mass of the utilized materials. In addition, rigorous study of CMOS compatible materials led to the usage of tungsten (W) for the design of a three-layered subtractive colour filter based on asymmetric Fabry-P´erot (FP) nanocavity, where titanium oxide (TiO2) nanocavity is sandwiched between thin tungsten (W) and optically thick aluminium (Al) film. The filter outputs vivid colours with 41.3 % of standard red, green, and blue (sRGB) coverage on the CIE 1931 map. The results show incident angle insensitivity up to 40o. The device is fabricated, but the physical characterization of the sample with the help of an SEM image is not helpful to obtain the thicknesses of the deposited films. However, the thicknesses obtained with the help of spectroscopic ellipsometry agree with the results obtained from optical characterization.
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    Evaluating various classification strategies for identifying tree species for tree inventory creation from a hyperspectral image : a thesis presented in the partial fulfilment of the requirements for the degree of Master of Science in Agriculture at Massey University, Manawatū, New Zealand
    (Massey University, 2017) Mackereth, Joel David
    An inventory showing tree species locations is a valuable tool for urban forest managers to support a healthy ecosystem. Urban areas offer harsh environmental conditions for these trees. This intensifies the value of a tree inventory to make sure the urban forest provides environmental, social and economic benefits. But the frequency and coverage of an inventory can be limited due to cost, time, level of expertise and poor access to private property. This study aims to overcome this limitation by using hyperspectral remote sensing and analysis to create cost effective and relatively fast tree inventories that cover both private and public land. This research tests if this technology accumulates enough information to separate and classify twenty tree species within a diverse canopy. To classify this image, this study used two stages. The first stage removed areas of the map that did not represent trees while the second stage separated twenty tree species from each other. This study used the aisaFENIX airborne imaging spectrometer to gather reflected light in the visible-shortwave infra-red (SWIR) range (400-2500 nm) over Palmerston North, New Zealand. The image has a 1 m2 spatial resolution, 3.5-11 nm spectral resolution of 448 spectral bands. Then ground sampling of tree species locations collected correct training and accuracy testing data for the classifiers. The classification compared 45 different strategies (9 pre-processing methods and five supervised classifiers). These combinations identified the best method to pre-process and classify the image at each stage. The pre-processing methods included band selection, and the noise reducing techniques of minimum noise fraction (MNF) and derivative reflectance (DR). While the classifiers used included the support vector machine (SVM), binary encoding (BE), Mahalanobis distance (MHD), maximum likelihood (ML), and minimum distance (MD) classifiers. The strategies produced vastly different results. In the first stage the MD classifier together with DR, MNF, and band selection pre-processing produced the best results when removing the non-tree surfaces from the image. In the second stage the SVM classifier together with MNF and band selection pre-processing achieved the best overall accuracy of 94.85% to separate twenty specific tree species. (Other tree species are misclassified as one of the twenty tree species). Therefore, this accuracy means that pixels representing each of the twenty tree species will be correctly classified within their own class 94.85% of the time. Evaluating multiple strategies led to combination producing a high overall accuracy in being able to separate twenty tree species from each other. This shows that hyperspectral remote sensing could be an effective tool to create tree inventories in urban environments.
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    In-plant, non-invasive spectral imaging for the prediction of lamb meat quality attributes : a thesis presented to Massey University for the partial fulfilment of the requirements of the degree of Masters of Food Technology, Massey University, Manawatū, New Zealand
    (Massey University, 2016) Stuart, Adam Douglas
    Muscle foods such as meat are a perishable, nutritious, relatively expensive food commodity, a great source of human nutrition and are a large part of the New Zealand economy, as well as overseas. Currently, New Zealand’s meat producing companies measure meat quality attributes by using a different technology for every trait, with no overarching way to combine them, with many of the technologies requiring collection and destruction of the product. There is a desire by the meat industry to find a single way to measure and compare meat quality parameters in a single process or technology. The development of an in-line (within the normal production line of an abattoir or meat processor), real time, non-destructive quality control system could help define multiple meat traits in a way that can guarantee the product in terms of composition, safety and consistency. These guarantees not only help the producer to ask a higher premium for their product, but also give assurances to the consumer that they are getting exactly what they are expecting and paying for. This thesis focused on determining whether the spectral imaging technologies of near infrared and hyperspectral imaging, and relevant pre-processing and modelling techniques were suitable for use in an in-plant situation for the prediction of lamb meat quality attributes. Data was collected on 2511 lambs from 10 separate kills. The lambs were slaughtered through three abattoirs owned by Alliance Group Limited with near infrared and hyperspectral imaging of intact M. Longissimus thoracis et lumborum muscle surface collected at 24 hours post-mortem. Traditional meat quality measurements were also collected; tenderness using a MIRINZ tenderometer, CIELab colour using a CR-400 colour meter, ultimate pH using an Eutech Cyberscan pH 300 meter, marbling using subjective scoring by trained personnel and intramuscular fat content using gas chromatography – flame ionisation detector. The resulting data were split and used to generate calibration and validation data sets. The calibration data was used together with the spectral data that was processed using a variety of chemometric techniques including partial least squares, variable selection and neural networks to generate predictive models. The accuracy of the predictive models was then tested using the validation data set. This work found that not all meat quality traits were able to be predicted accurately and certain techniques worked better for differing traits. The best predictive models for ultimate pH using the near infrared and hyperspectral data achieved R2 values (a measure of goodness of fit) from the validation data sets of 0.63 and 0.48 respectively. For near infrared the best predictive models were achieved using partial least squares with pre-processing (standard normal variate, orthogonal signal correction and mean centring) applied, while for hyperspectral imaging neural networks provided the best model using a decay of 0.00004 and a node size of 2. The best predictive models for intramuscular fat using the near infrared and hyperspectral data achieved R2 values from the validation data sets of 0.56 and 0.75 respectively. For near infrared this was achieved using partial least squares with pre-processing (normalisation, multiplicative scatter correction and mean centring) applied, while for hyperspectral imaging neural networks provided the best model using a decay of 0.0009 and a node size of 4. This performance of these two traits in particular, shows that that the prediction abilities are of a quality that future work on implementing these into an in-line system at a pilot scale should be considered. Overall, the use of novel modelling techniques such as neural networks showed potential to increase the predictive abilities of the resulting models, over more traditional modelling techniques. Additionally, it was demonstrated that the number of predictors needed to create a calibration model could be reduced, increasing the speed of analysis with only minimal loss in the accuracy of the resulting model. Results obtained during this study suggest that the calibration models are not abattoir dependent and the transfer of one calibration model to multiple abattoirs could decrease the costs and allow for faster development and implementation of an in-line, in-plant system.