8,969 research outputs found

    Graduate 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

    Graduate Catalog of Studies, 2023-2024

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    Dynamics and Modelling of the 2015 Calbuco eruption Volcanic Debris Flows (Chile). From field evidence to a primary lahar model

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    The Calbuco volcanic eruption of 2015, was characterized by two explosive phases with partialand major column collapses that triggered lahars in many of the flanks of the volcano. Large lahar flows descended to the southern flank where highly fractured ice bodies were emplaced on steep slopes.In this study, we present a chronology of the volcanic flows based on a multi parameterdata set that includes social media, reports of authoritative institutions, instrumental monitoringdata and published research literature on the eruption. Our review established thatlahars in the Amarillo river began during the first phase of the eruption due to the sustained emplacement of pyroclastic flows in its catchment. In contrast, we propose that the lahars in theBlanco – Correntoso river system and the Este river were likely to have been triggered by asudden mechanical collapse of the glacier that triggered mixed avalanches which transitionedinto lahars downstream.Our observations include inundation cross-sections, estimates of flow speeds, and characterization of the morphology, grain sizes, and componentry of deposits.Field measurements are used together with instrumental data for calibrating a dynamic, physics-based model of lahar, Laharflow. We model flows in the Blanco – Correntoso river system and explore the influence of the model parameters on flow predictions in an ensemble of simulations. We develop a calibration that accounts for the substantial epistemic uncertainties in our observations and the model formulation, that seeks to determine plausible ranges for the model parameters, including those representing the lahar source. Our approach highlights the parameters in the model that have a dominant effect on the ability of the model to match observations, indicating where further development and additional observations could improve model predictions. The simulations in our ensemble that provide plausible matches to the observations are combined to produce flow inundation maps

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    GFDM Pulse Shaping Optimization Based Genetic Algorithm

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     يعد تعدد الإرسال بتقسيم التردد العمومي (GFDM) أحد المخططات المرشحة للجيل الخامس وما بعده. إن مخطط وهيكله لتشكيل الموجات الحاملة المتعددة عبارة عن بلوكات مستقلة، ويحتوي كل بلوك على موجات حاملة فرعية ورموز فرعية. يتم ترشيح الموجات الحاملة الفرعية بنموذج أولي لتشكيل النبضة يتحول حسب الوقت ومجال التردد. يستخدم هذا العمل الخوارزمية الجينية (GA) لتعيين أفضل المعلمات لمرشح تشكيل النبض، والذي يستخدم الخطأ كدالة تكلفة. تقوم الخوارزمية بتعيين معلماتها بناءً على حد أدنى من الخطأ من خلال المعالجة التكرارية حتى الوصول إلى قيم النبض التي تعطي الخطأ الطفيف. أعطت هذه الطريقة ميزة تقليل الخطأ الناتج عن التعامد وبالتالي أعطت أداءً محسنًا. تعتمد هذه الطريقة في البداية على البحث عن قيم المرشحات التي توفر أقل قيمة للخطأ ثم اعتماد هذه القيم في بناء مرسل ومستقبل GFDM. خفضت هذه الطريقة معدل الخطأ في البتات إلى 0.0107 عند 10 SNR و0.0033 عند 25 SNR مقارنة بالطريقة التقليدية. وبالتالي، هذه طريقة جديدة لبناء نظام الإرسال والاستقبالGFDM.Generalized Frequency Division Multiplexing (GFDM) is one of the candidate schemes for the 5G and beyond. Its multicarrier modulation scheme and structure are independent blocks, and each block contains sub-carriers and sub-symbols. Sub-carriers are filtered with a prototype pulse shaping that shifts by time and frequency domain. This work uses Genetic Algorithm (GA) to assign the best parameters of the pulse shaping filter, which uses the error as a cost function. The algorithm assigns its parameters based on a minimum error by iteratively processing until reaching the pulse values that give the minor error. This method gave the advantage of reducing the error generated due to orthogonality and thus gave improved performance. This method initially depends on searching for filter values that provide the lowest error value and then adopting these values in building the GFDM transmitter and receiver. This method reduced the BER to 0.0107 at 10 SNR and 0.0033 at 25 SNR compared with the traditional method. Thus, this is a new method for building a GFDM transceiver system

    Undergraduate Catalog of Studies, 2022-2023

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    Spatial Interaction Models in a Big Data Grocery Retailing Environment

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    Grocery expenditure is responsible for around 10% of total household spend in the UK, making the grocery retail market worth over £200bn a year in 2021. The size of this market and the nature of retailing competition makes it important for retailers to make the right decisions. One such decision is the location of their stores for which there have been a number of changes in the location, format and channel of consumer interaction along with the methods that have been employed to determine new store location. In recent years it has been suggested that the spatial interaction model is the most appropriate method for estimating new store revenue and hence location. However, previous attempts to explore the performance of the spatial interaction model in grocery retailing have been limited by access to loyalty card data. In this thesis we show that these models are unable to account for the heterogeneity in store conditions and consumer behaviour to model total store revenue. Notably, we find that at the regional scale the size of the errors are such that these models are unlikely to be used consistently in practice for estimating store revenue or locating new stores. Furthermore, that the performance achieved in previous applications are unlikely to be consistently replicated. Thus our results demonstrate that the spatial interaction model in its current form is no longer appropriate for modelling grocery store revenue. It is anticipated that these results may become a starting point for the development and application of alternative forms of models and methods for predicting grocery retailing store revenue. Notably, such new methods must be able to account for recent changes in consumer behaviour such as convenience store shopping, multi-purpose trips and the growing influence of e-commerce, alongside changes in retailers interaction strategies

    Under construction: infrastructure and modern fiction

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    In this dissertation, I argue that infrastructural development, with its technological promises but widening geographic disparities and social and environmental consequences, informs both the narrative content and aesthetic forms of modernist and contemporary Anglophone fiction. Despite its prevalent material forms—roads, rails, pipes, and wires—infrastructure poses particular formal and narrative problems, often receding into the background as mere setting. To address how literary fiction theorizes the experience of infrastructure requires reading “infrastructurally”: that is, paying attention to the seemingly mundane interactions between characters and their built environments. The writers central to this project—James Joyce, William Faulkner, Karen Tei Yamashita, and Mohsin Hamid—take up the representational challenges posed by infrastructure by bringing transit networks, sanitation systems, and electrical grids and the histories of their development and use into the foreground. These writers call attention to the political dimensions of built environments, revealing the ways infrastructures produce, reinforce, and perpetuate racial and socioeconomic fault lines. They also attempt to formalize the material relations of power inscribed by and within infrastructure; the novel itself becomes an imaginary counterpart to the technologies of infrastructure, a form that shapes and constrains what types of social action and affiliation are possible
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