3,641 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Active and Fast Tunable Plasmonic Metamaterials

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    Active and Fast Tunable Plasmonic Metamaterials is a research development that has contributed to studying the interaction between light and matter, specifically focusing on the interaction between the electromagnetic field and free electrons in metals. This interaction can be stimulated by the electric component of light, leading to collective oscillations. In the field of nanotechnology, these phenomena have garnered significant interest due to their ability to enable the transmission of both optical signals and electric currents through the same thin metal structure. This presents an opportunity to connect the combined advantages of photonics and electronics within a single platform. This innovation gives rise to a new subfield of photonics known as plasmonic metamaterials.Plasmonic metamaterials are artificial engineering materials whose optical properties can be engineered to generate the desired response to an incident electromagnetic wave. They consist of subwavelength-scale structures which can be understood as the atoms in conventional materials. The collective response of a randomly or periodically ordered ensemble of such meta-atoms defines the properties of the metamaterials, which can be described in terms of parameters such as permittivity, permeability, refractive index, and impedance. At the interface between noble metal particles and dielectric media, collective oscillations of the free electrons in the metal particles can be resonantly excited, known as plasmon resonances. This work considered two plasmon resonances: localised surface plasmon resonances (LSPRs) and propagating surface plasmon polaritons (SPPs).The investigated plasmonic metamaterials, designed with specific structures, were considered for use in various applications, including telecommunications, information processing, sensing, industry, lighting, photovoltaic, metrology, and healthcare. The sample structures are manufactured using metal and dielectric materials as artificial composite materials. It can be used in the electromagnetic spectrum's visible and near-infrared wavelength range. Results obtained proved that artificial composite material can produce a thermal coherent emission at the mid-infrared wavelength range and enable active and fast-tunable optoelectronic devices. Therefore, this work focused on the integrated thermal infrared light source platforms for various applications such as thermal analysis, imaging, security, biosensing, and medical diagnosis. Enabled by Kirchhoff's law of thermal radiation, this work combined the concepts of material absorption with material emission. Hence, the results obtained proved that this approach enhances the overall performance of the active and fast-tunable plasmonic metamaterial in terms of with effortless and fast tunability. This work further considers the narrow line width of the coherent thermal emission, tunable emission, and angular tunable emission at the mid-infrared, which are achieved through plasmonic stacked grating structure (PSGs) and plasmonic infrared absorber structure (PIRAs).Three-dimensional (3D) plasmonic stacked gratings (PSGs) was used to create a tunable plasmonic metamaterial at optical wavelengths ranging from 3 m to 6 m, and from 6m to 9 m. These PSGs are made of a metallic grating with corrugations caused by narrow air openings, followed by a Bragg grating (BG). Additionally, this work demonstrated a thermal radiation source customised for the mid-infrared wavelength range of 3 ÎŒm to 5 ÎŒm. This source exhibits intriguing characteristics such as high emissivity, narrowband spectra, and sharp angular response capabilities. The proposed thermal emitter consists of a two-dimensional (2D) metallic grating on top of a one-dimensional dielectric BG.Results obtained presented a plasmonic infrared absorber (PIRA) graphene nanostructure designed for a wavelength range of 3 to 14 ÎŒm. It was observed and concluded that this wavelength range offers excellent opportunities for detection, especially when targeting gas molecules in the infrared atmospheric windows. The design framework is based on active plasmon control for subwavelength-scale infrared absorbers within the mid-infrared range of the electromagnetic spectrum. Furthermore, this design is useful for applications such as infrared microbolometers, infrared photodetectors, and photovoltaic cells.Finally, the observation and conclusion drawn for the sample of nanostructure used in this work, which consists of an artificial composite arrangement with plasmonic material, can contribute to a highly efficient mid-infrared light source with low power consumption, fast response emissions, and is a cost-effective structure

    Self-supervised learning for transferable representations

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    Machine learning has undeniably achieved remarkable advances thanks to large labelled datasets and supervised learning. However, this progress is constrained by the labour-intensive annotation process. It is not feasible to generate extensive labelled datasets for every problem we aim to address. Consequently, there has been a notable shift in recent times toward approaches that solely leverage raw data. Among these, self-supervised learning has emerged as a particularly powerful approach, offering scalability to massive datasets and showcasing considerable potential for effective knowledge transfer. This thesis investigates self-supervised representation learning with a strong focus on computer vision applications. We provide a comprehensive survey of self-supervised methods across various modalities, introducing a taxonomy that categorises them into four distinct families while also highlighting practical considerations for real-world implementation. Our focus thenceforth is on the computer vision modality, where we perform a comprehensive benchmark evaluation of state-of-the-art self supervised models against many diverse downstream transfer tasks. Our findings reveal that self-supervised models often outperform supervised learning across a spectrum of tasks, albeit with correlations weakening as tasks transition beyond classification, particularly for datasets with distribution shifts. Digging deeper, we investigate the influence of data augmentation on the transferability of contrastive learners, uncovering a trade-off between spatial and appearance-based invariances that generalise to real-world transformations. This begins to explain the differing empirical performances achieved by self-supervised learners on different downstream tasks, and it showcases the advantages of specialised representations produced with tailored augmentation. Finally, we introduce a novel self-supervised pre-training algorithm for object detection, aligning pre-training with downstream architecture and objectives, leading to reduced localisation errors and improved label efficiency. In conclusion, this thesis contributes a comprehensive understanding of self-supervised representation learning and its role in enabling effective transfer across computer vision tasks

    Analysis and monitoring of single HaCaT cells using volumetric Raman mapping and machine learning

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    No explorer reached a pole without a map, no chef served a meal without tasting, and no surgeon implants untested devices. Higher accuracy maps, more sensitive taste buds, and more rigorous tests increase confidence in positive outcomes. Biomedical manufacturing necessitates rigour, whether developing drugs or creating bioengineered tissues [1]–[4]. By designing a dynamic environment that supports mammalian cells during experiments within a Raman spectroscope, this project provides a platform that more closely replicates in vivo conditions. The platform also adds the opportunity to automate the adaptation of the cell culture environment, alongside spectral monitoring of cells with machine learning and three-dimensional Raman mapping, called volumetric Raman mapping (VRM). Previous research highlighted key areas for refinement, like a structured approach for shading Raman maps [5], [6], and the collection of VRM [7]. Refining VRM shading and collection was the initial focus, k-means directed shading for vibrational spectroscopy map shading was developed in Chapter 3 and exploration of depth distortion and VRM calibration (Chapter 4). “Cage” scaffolds, designed using the findings from Chapter 4 were then utilised to influence cell behaviour by varying the number of cage beams to change the scaffold porosity. Altering the porosity facilitated spectroscopy investigation into previously observed changes in cell biology alteration in response to porous scaffolds [8]. VRM visualised changed single human keratinocyte (HaCaT) cell morphology, providing a complementary technique for machine learning classification. Increased technical rigour justified progression onto in-situ flow chamber for Raman spectroscopy development in Chapter 6, using a Psoriasis (dithranol-HaCaT) model on unfixed cells. K-means-directed shading and principal component analysis (PCA) revealed HaCaT cell adaptations aligning with previous publications [5] and earlier thesis sections. The k-means-directed Raman maps and PCA score plots verified the drug-supplying capacity of the flow chamber, justifying future investigation into VRM and machine learning for monitoring single cells within the flow chamber

    Graduate Catalog of Studies, 2023-2024

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    Redefining Disproportionate Arrest Rates: An Exploratory Quasi-Experiment that Reassesses the Role of Skin Tone

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    The New York Times reported that Black Lives Matter was the third most-read subject of 2020. These articles brought to the forefront the question of disparity in arrest rates for darker-skinned people. Questioning arrest disparity is understandable because virtually everything known about disproportionate arrest rates has been a guess, and virtually all prior research on disproportionate arrest rates is questionable because of improper benchmarking (the denominator effect). Current research has highlighted the need to switch from demographic data to skin tone data and start over on disproportionate arrest rate research; therefore, this study explored the relationship between skin tone and disproportionate arrest rates. This study also sought to determine which of the three theories surrounding disproportionate arrests is most predictive of disproportionate rates. The current theories are that disproportionate arrests increase as skin tone gets darker (stereotype threat theory), disproportionate rates are different for Black and Brown people (self-categorization theory), or disproportionate rates apply equally across all darker skin colors (social dominance theory). This study used a quantitative exploratory quasi-experimental design using linear spline regression to analyze arrest rates in Alachua County, Florida, before and after the county’s mandate to reduce arrests as much as possible during the COVID-19 pandemic to protect the prison population. The study was exploratory as no previous study has used skin tone analysis to examine arrest disparity. The findings of this study redefines the understanding of the existence and nature of disparities in arrest rates and offer a solid foundation for additional studies about the relationship between disproportionate arrest rates and skin color

    HyperVein: A Hyperspectral Image Dataset for Human Vein Detection

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    HyperSpectral Imaging (HSI) plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. HyperSpectral (HS) image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper presents a HS image dataset encompassing left- and right-hand images captured from 100 subjects with varying skin tones. The dataset was annotated using anatomical data to represent vein and non-vein areas within the images. This dataset is utilised to explore the effectiveness of dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward’s Linkage Strategy using Mutual Information (WaLuMI) for vein detection. To generate experimental results, the HS image dataset was divided into train and test datasets. Optimum performing parameters for each of the dimensionality reduction techniques in conjunction with the Support Vector Machine (SVM) binary classification were determined using the Training dataset. The performance of the three dimensionality reduction-based vein detection methods was then assessed and compared using the test image dataset. Results show that the FPCA-based method outperforms the other two methods in terms of accuracy. For visualization purposes, the classification prediction image for each technique is post-processed using morphological operators, and results show the significant potential of HS imaging in vein detection

    Air Quality Research Using Remote Sensing

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    Air pollution is a worldwide environmental hazard that poses serious consequences not only for human health and the climate but also for agriculture, ecosystems, and cultural heritage, among other factors. According to the WHO, there are 8 million premature deaths every year as a result of exposure to ambient air pollution. In addition, more than 90% of the world’s population live in areas where the air quality is poor, exceeding the recommended limits. On the other hand, air pollution and the climate co-influence one another through complex physicochemical interactions in the atmosphere that alter the Earth’s energy balance and have implications for climate change and the air quality. It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze different scenarios with the aim of assessing the air pollution levels and developing early warning and forecast systems as a means of improving the air quality and safeguarding public health. Such measures can also form part of efforts to achieve a reduction in the number of air pollution casualties and mitigate climate change phenomena. This book contains contributions focusing on remote sensing techniques for evaluating air quality, including the use of in situ data, modeling approaches, and the synthesis of different instrumentations and techniques. The papers published in this book highlight the importance and relevance of air quality studies and the potential of remote sensing, particularly that conducted from Earth observation platforms, to shed light on this topic
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