2,286 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    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

    Undergraduate Catalog of Studies, 2023-2024

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    Impact of Imaging and Distance Perception in VR Immersive Visual Experience

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    Virtual reality (VR) headsets have evolved to include unprecedented viewing quality. Meanwhile, they have become lightweight, wireless, and low-cost, which has opened to new applications and a much wider audience. VR headsets can now provide users with greater understanding of events and accuracy of observation, making decision-making faster and more effective. However, the spread of immersive technologies has shown a slow take-up, with the adoption of virtual reality limited to a few applications, typically related to entertainment. This reluctance appears to be due to the often-necessary change of operating paradigm and some scepticism towards the "VR advantage". The need therefore arises to evaluate the contribution that a VR system can make to user performance, for example to monitoring and decision-making. This will help system designers understand when immersive technologies can be proposed to replace or complement standard display systems such as a desktop monitor. In parallel to the VR headsets evolution there has been that of 360 cameras, which are now capable to instantly acquire photographs and videos in stereoscopic 3D (S3D) modality, with very high resolutions. 360° images are innately suited to VR headsets, where the captured view can be observed and explored through the natural rotation of the head. Acquired views can even be experienced and navigated from the inside as they are captured. The combination of omnidirectional images and VR headsets has opened to a new way of creating immersive visual representations. We call it: photo-based VR. This represents a new methodology that combines traditional model-based rendering with high-quality omnidirectional texture-mapping. Photo-based VR is particularly suitable for applications related to remote visits and realistic scene reconstruction, useful for monitoring and surveillance systems, control panels and operator training. The presented PhD study investigates the potential of photo-based VR representations. It starts by evaluating the role of immersion and user’s performance in today's graphical visual experience, to then use it as a reference to develop and evaluate new photo-based VR solutions. With the current literature on photo-based VR experience and associated user performance being very limited, this study builds new knowledge from the proposed assessments. We conduct five user studies on a few representative applications examining how visual representations can be affected by system factors (camera and display related) and how it can influence human factors (such as realism, presence, and emotions). Particular attention is paid to realistic depth perception, to support which we develop target solutions for photo-based VR. They are intended to provide users with a correct perception of space dimension and objects size. We call it: true-dimensional visualization. The presented work contributes to unexplored fields including photo-based VR and true-dimensional visualization, offering immersive system designers a thorough comprehension of the benefits, potential, and type of applications in which these new methods can make the difference. This thesis manuscript and its findings have been partly presented in scientific publications. In particular, five conference papers on Springer and the IEEE symposia, [1], [2], [3], [4], [5], and one journal article in an IEEE periodical [6], have been published

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Undergraduate Catalog of Studies, 2022-2023

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    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Systemic Circular Economy Solutions for Fiber Reinforced Composites

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    This open access book provides an overview of the work undertaken within the FiberEUse project, which developed solutions enhancing the profitability of composite recycling and reuse in value-added products, with a cross-sectorial approach. Glass and carbon fiber reinforced polymers, or composites, are increasingly used as structural materials in many manufacturing sectors like transport, constructions and energy due to their better lightweight and corrosion resistance compared to metals. However, composite recycling is still a challenge since no significant added value in the recycling and reprocessing of composites is demonstrated. FiberEUse developed innovative solutions and business models towards sustainable Circular Economy solutions for post-use composite-made products. Three strategies are presented, namely mechanical recycling of short fibers, thermal recycling of long fibers and modular car parts design for sustainable disassembly and remanufacturing. The validation of the FiberEUse approach within eight industrial demonstrators shows the potentials towards new Circular Economy value-chains for composite materials

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
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