10,472 research outputs found

    Graduate Catalog of Studies, 2023-2024

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    Space object identification and classification from hyperspectral material analysis

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    This paper presents a data processing pipeline designed to extract information from the hyperspectral signature of unknown space objects. The methodology proposed in this paper determines the material composition of space objects from single pixel images. Two techniques are used for material identification and classification: one based on machine learning and the other based on a least square match with a library of known spectra. From this information, a supervised machine learning algorithm is used to classify the object into one of several categories based on the detection of materials on the object. The behaviour of the material classification methods is investigated under non-ideal circumstances, to determine the effect of weathered materials, and the behaviour when the training library is missing a material that is present in the object being observed. Finally the paper will present some preliminary results on the identification and classification of space objects

    Flood dynamics derived from video remote sensing

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    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    Convolutional neural network ensemble learning for hyperspectral imaging-based blackberry fruit ripeness detection in uncontrolled farm environment

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    Fruit ripeness estimation models have for decades depended on spectral index features or colour-based features, such as mean, standard deviation, skewness, colour moments, and/or histograms for learning traits of fruit ripeness. Recently, few studies have explored the use of deep learning techniques to extract features from images of fruits with visible ripeness cues. However, the blackberry (Rubus fruticosus) fruit does not show obvious and reliable visible traits of ripeness when mature and therefore poses great difficulty to fruit pickers. The mature blackberry, to the human eye, is black before, during, and post-ripening. To address this engineering application challenge, this paper proposes a novel multi-input convolutional neural network (CNN) ensemble classifier for detecting subtle traits of ripeness in blackberry fruits. The multi-input CNN was created from a pre-trained visual geometry group 16-layer deep convolutional network (VGG16) model trained on the ImageNet dataset. The fully connected layers were optimized for learning traits of ripeness of mature blackberry fruits. The resulting model served as the base for building homogeneous ensemble learners that were ensemble using the stack generalization ensemble (SGE) framework. The input to the network is images acquired with a stereo sensor using visible and near-infrared (VIS-NIR) spectral filters at wavelengths of 700 nm and 770 nm. Through experiments, the proposed model achieved 95.1% accuracy on unseen sets and 90.2% accuracy with in-field conditions. Further experiments reveal that machine sensory is highly and positively correlated to human sensory over blackberry fruit skin texture

    Deep saliency detection-based pedestrian detection with multispectral multi-scale features fusion network

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    In recent years, there has been increased interest in multispectral pedestrian detection using visible and infrared image pairs. This is due to the complementary visual information provided by these modalities, which enhances the robustness and reliability of pedestrian detection systems. However, current research in multispectral pedestrian detection faces the challenge of effectively integrating different modalities to reduce miss rates in the system. This article presents an improved method for multispectral pedestrian detection. The method utilises a saliency detection technique to modify the infrared image and obtain an infrared-enhanced map with clear pedestrian features. Subsequently, a multiscale image features fusion network is designed to efficiently fuse visible and IR-enhanced maps. Finally, the fusion network is supervised by three loss functions for illumination perception, light intensity, and texture information in conjunction with the light perception sub-network. The experimental results demonstrate that the proposed method improves the logarithmic mean miss rate for the three main subgroups (all day, day and night) to 3.12%, 3.06%, and 4.13% respectively, at “reasonable” settings. This is an improvement over the traditional method, which achieved rates of 3.11%, 2.77%, and 2.56% respectively, thus demonstrating the effectiveness of the proposed method

    Graduate Catalog of Studies, 2023-2024

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    Oil Spills- Where We Were, Where We Are, And Where We Will Be? A Bibliometric and Content Analysis Discourse

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    More frequently and in more ways than one might think, oil spills are a very common phenomenon. There were three major (>700 tonnes; Asia and Africa) and four minor oil spills only in 2022 (between 7 and 700 tonnes; North America, Asia, and Africa). Oil spills have been known to cause numerous negative ecological, societal, economic, and public health impacts. Not only this but oil spills require rapid response to contain and mitigate multidimensional damages caused. A SCOPUS search of the keyword ‘Oil Spills’ in ‘’Article title, Abstracts, and Keywords’ and ‘Article title’ results in 30529 and 9851 (as of March 4th, 2023) documents (Journal articles, Conference proceedings, Books, Book series, Trade journals, and Reports). In the year 2023 alone, the SCOPUS database had 297 documents at the time of writing. Such a massive database requires a retrospection of underlying and emerging themes for readers to understand the extant literature and to uncover future research agendas. This study is an attempt to conduct a bibliometric analysis of select ‘Oil spill’ publications. This investigation will involve performance analysis (performance of research constituents such as publication and citation evolution, leading authors, publications, affiliations, sources, and countries) and science mapping (relationship between research constituents by analyzing conceptual, intellectual, and social structures). VOSviewer and Biblioshiny The study will conclude future research trends by the content analysis of the fifteen most recent and cited documents

    Thermal photogrammetry on a permafrost rock wall for the active layer monitoring

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    Permafrost and active layer models often cannot explain the high spatial variability, especially in heterogeneous environments like the mountainous regions due to their scarce resolution, paucity of climatic data and topographic details. In this study, we want to introduce a new application of the unmanned aerial vehicle (UAV) in thermal photogrammetry to model the active layer thickness (ALT) of an alpine rock wall through the computation of the thermal inertia and compare the results with a widespread ALT model. On the Gran Zebrù South rock wall, 8 thermal UAV surveys has been conducted in 4 different summer days during 2021-2022 in order to have two 3D thermal models per day at different solar radiation inputs. By analyzing topographic data, visible imagery and the thermal models, the apparent thermal inertias (ATIs) have been converted into heat transfer coefficients (HTCs) and then into ALT of 2021 and 2022. These maps have been validated through the placement of thermistors at different elevations and with variable depths (2, 15 and 40 cm from the rock surface). The resulting ALT has been compared with the Stefan's solution and the alpine permafrost index map (APIM), which showed large underestimations and a noncorrespondence with permafrost occurrence. The average ALT increase of 29.3 cm from 2021 to 2022 has been discussed regarding permafrost formation/degradation future trend under the climatic change and potential risks of alpine areas

    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

    Christ Child Bearing the Instruments of the Passion Technical Study and Treatment of a Painting on Copper from the Viceroyalty of Peru

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    Christ Child Bearing the Instruments of the Passion (acc.# 228017) is a 17th century Peruvian Viceregal painting on copper belonging to the Carl & Marilynn Thoma Art Foundation. The painting depicts the Christ Child on a flower laid path as he carries the instruments of the passion also known as the Arma Christi Paintings executed on copper convey new and challenging preservation issues based on their materials and techniques.. The work had been heavily restored and exhibited several condition issues, including significant overpaint and broad losses. The painting was photographed using multimodal imaging techniques as well as reflectance transformation imaging and multispectral imaging. Analytical techniques including Raman, reflected FTIR, and scanning XRF were performed to better understand the painting’s materials and history. The palette was consistent with historical materials of the region and the period, and indicated the use of earth colors, vermillion, lamp black, lead white, and azurite. Finally, conservation treatments were undertaken to reduce the previous overpaint and improve the aesthetic legibility. The treatment was successful and restored unity to the composition
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