606 research outputs found

    Perception Intelligence Integrated Vehicle-to-Vehicle Optical Camera Communication.

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    Ubiquitous usage of cameras and LEDs in modern road and aerial vehicles open up endless opportunities for novel applications in intelligent machine navigation, communication, and networking. To this end, in this thesis work, we hypothesize the benefit of dual-mode usage of vehicular built-in cameras through novel machine perception capabilities combined with optical camera communication (OCC). Current key conception of understanding a line-of-sight (LOS) scenery is from the aspect of object, event, and road situation detection. However, the idea of blending the non-line-of-sight (NLOS) information with the LOS information to achieve a see-through vision virtually is new. This improves the assistive driving performance by enabling a machine to see beyond occlusion. Another aspect of OCC in the vehicular setup is to understand the nature of mobility and its impact on the optical communication channel quality. The research questions gathered from both the car-car mobility modelling, and evaluating a working setup of OCC communication channel can also be inherited to aerial vehicular situations like drone-drone OCC. The aim of this thesis is to answer the research questions along these new application domains, particularly, (i) how to enable a virtual see-through perception in the car assisting system that alerts the human driver about the visible and invisible critical driving events to help drive more safely, (ii) how transmitter-receiver cars behaves while in the mobility and the overall channel performance of OCC in motion modality, (iii) how to help rescue lost Unmanned Aerial Vehicles (UAVs) through coordinated localization with fusion of OCC and WiFi, (iv) how to model and simulate an in-field drone swarm operation experience to design and validate UAV coordinated localization for group of positioning distressed drones. In this regard, in this thesis, we present the end-to-end system design, proposed novel algorithms to solve the challenges in applying such a system, and evaluation results through experimentation and/or simulation

    Optimization for Deep Learning Systems Applied to Computer Vision

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    149 p.Since the DL revolution and especially over the last years (2010-2022), DNNs have become an essentialpart of the CV field, and they are present in all its sub-fields (video-surveillance, industrialmanufacturing, autonomous driving, ...) and in almost every new state-of-the-art application that isdeveloped. However, DNNs are very complex and the architecture needs to be carefully selected andadapted in order to maximize its efficiency. In many cases, networks are not specifically designed for theconsidered use case, they are simply recycled from other applications and slightly adapted, without takinginto account the particularities of the use case or the interaction with the rest of the system components,which usually results in a performance drop.This research work aims at providing knowledge and tools for the optimization of systems based on DeepLearning applied to different real use cases within the field of Computer Vision, in order to maximizetheir effectiveness and efficiency

    The Vibrancy and Resilience of British High Streets

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    British high streets have endured significant economic and cultural challenges both in the leadup to and as a consequence of the COVID-19 pandemic. The volatile and challenging socio-economic environment has been brought about by the lingering effects of the 2008 recession, high business rates, competition from online retailers, and the impact and implications of the global pandemic. The changes to the high street retail landscape have been recorded using new sources of data that can supplement traditional data sources such as local government retail surveys. New sources of data such as consumer data, property portal data and mobility data are more spatially and temporally granular. As a result, local governments, the retail sector and stakeholders can use these emerging forms of data to create more easily updateable measures of high street composition and performance. This thesis utilises the Local Data Company’s Britain-wide database on retail location, type and vacancy. The data ranges between the start of 2017 and June of 2021, containing around 800,000 records of occupiers. The analysis within this thesis starts by describing the composition and vibrancy of British high streets in the lead-up to the pandemic. Next, the thesis provides an evaluation of the impact of the COVID-19 pandemic and the subsequent shift towards remote working on the viable resilience of commuter towns. This section is followed by an exploration of the short-term impacts of the COVID-19 lockdown restrictions on the resilience of Britain’s high streets. Finally, the application of new forms of data in informing local government high street regeneration policy is studied as part of a knowledge exchange with the London Borough of Camden. This thesis contributes to our understanding of how the circumstances of different British high streets can be monitored and mapped, with the goal of improving understanding of vibrancy, resilience, and potential for regeneration

    Effectual Urban Governance: The Effectuation of Cities for Systems Change Under Uncertainty

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    Three key drivers create the imperative for a new approach to urban governance. Firstly, scientists around the world agree that global ecological systems are at risk of collapse if current development trajectories continue. Secondly, decision-makers are facing a heightened level of uncertainty, due to factors including climate risk, ecosystem changes and geopolitical tensions – and since 2020, the COVID-19 global pandemic. And thirdly, given these complexities, current models of forecasting and prediction for strategic decision-making are increasingly constrained and unreliable, particularly for informing urban infrastructure governance decisions with multi-decade legacies. While urban infrastructure decision-makers find uncertainty challenging, for entrepreneurs uncertainty is the basis for opportunity. Entrepreneurs are agents of systems change, especially under conditions of heightened uncertainty. As a result, this thesis turns to the entrepreneurship domain to inform a new approach to urban governance, specifically the entrepreneurial decision-making logic of ‘effectuation’ developed by Saras Sarasvathy through her study of expert entrepreneurs’ approaches to new venture creation. Effectual urban governance includes establishing design principles, beginning with available means, establishing partnerships, and taking effectual action to iteratively increasing the structuration of innovations. In Part 1 - the thesis develops this model by reviewing and synthesizing the literature on sustainability transitions, urban governance, and entrepreneurship, with a historical analysis illustrating the role of entrepreneurship in industrial systems change. Building on a novel taxonomy of urban governance along the axes of uncertainty and systems change, the dynamic model of effectual urban governance combines entrepreneurship theory with sustainability transitions theory and is demonstrated through an illustrative civil infrastructure case study of the Willunga Basin Water Company informed by semi-structured research interviews. Part 2 of the thesis justifies the applicability of this model through focus on four key elements of effectual urban governance with application to urban transport, elaborating the theoretical rationale for each element and providing insights from effectuation literature and supporting complementary academic theories and research conducted during this thesis. In doing so, the thesis makes theoretical and practical contributions to urban governance and the development of civil infrastructure in the 21st century. At a time of heightened uncertainty, when global industrial and economic transformation to avert ecological collapse is imperative, this thesis begins a new conversation by demonstrating how adopting an entrepreneurial approach to civil infrastructure development can help government and civil actors proactively address the world’s shared and complex challenges. Effectual urban governance is this approach.Thesis (Ph.D.) -- University of Adelaide, School of Architecture and Civil Engineering, 202

    Estimación del impacto ambiental y social de los nuevos servicios de movilidad

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    El transporte es fuente de numerosas externalidades negativas, como los accidentes de tráfico, la congestión en las zonas urbanas y la falta de calidad del aire. El transporte también es un sector que contribuye sustancialmente a la crisis climática con más del 16% de las emisiones globales de gases de efecto invernadero como resultado de las actividades de transporte. Muchos creen que la introducción de nuevos servicios de movilidad podría ayudar a reducir esas externalidades. Sin embargo, con cada introducción de un nuevo servicio de movilidad podemos observar factores que podrían contribuir negativamente a la sostenibilidad del sistema de transporte: una cadena de cambios de comportamiento causados por la introducción de posibilidades completamente nuevas. El objetivo de esta tesis es investigar cómo los nuevos servicios de movilidad, habilitados por la electrificación, la conectividad y la automatización, podrían impactar en las externalidades causadas por el transporte. En particular, el objetivo es desarrollar y validar un marco de modelado capaz de capturar la complejidad del sistema de transporte y aplicarlo para evaluar el impacto potencial de los vehículos automatizados.Transport is a source of numerous negative externalities, such as road accidents, congestion in urban areas and lacking air quality. Transport is also a sector substantially contributing to climate crisis with more than 16% of global greenhouse gas emissions being a result of transport activities. Many believe that the introduction of new mobility services could help reduce those externalities. However, with each introduction of a new mobility service we can observe factors that could negatively contribute to the sustainability of the transport system – a chain of behavioural changes caused by introduction of entirely new possibilities. The aim of this thesis is to investigate how the new mobility services, enabled by electrification, connectivity and automation, could impact the externalities caused by transport. In particular the objective is to develop and validate a modelling framework able to capture the complexity of the transport system and to apply it to assess the potential impact of automated vehicles.This work was realised with the collaboration of the European Commission Joint Research Centre under the Collaborative Doctoral Partnership Agreement N035297. Moreover, this research has been partially funded by the Spanish Ministry of Science and Innovation through the project: AUTONOMOUS – InnovAtive Urban and Transport planning tOols for the implementation of New mObility systeMs based On aUtonomouS driving”, 2020-2023, ERDF (EU) (PID2019-110355RB-I00)

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    Managing Fish or Governing Fisheries? An Historical Recount of Marine Resources Governance in the Context of Latin America – The Ecuadorian Case

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    The narratives and images about ocean and its resources governance, their use and value have deep roots in human history. Traditionally, the contemporary images of fish and fisheries have been shaped under the cultural construction of power, wealth and exclusion, and also as one of poverty and marginalization. This perception was formed on early notions of natural (marine) resources access and use that were born within the colonial machinery that ruled the world from the Middle Ages until late XVII. This research explores the historical overview of marine resources usage and governance in Latin America, from a ‘critical approach to development’ perspective, by following a narrative description based on a ‘three acts’ format. It illustrates how and to what extent politics, power and knowledge have deeply influenced policies and practices at exploring the marine and terrestrial resources and at managing fish and seafood, historically, and how the fisheries resources’ management practices are influenced by principles of appropriation, regulation and usage, put in place already in the XV century that were imposed at the conquering and colonization of the Americas, disregarded previous governance practices. This article argues that fisheries governance cannot be improved without some appreciation for the social, historical, geopolitical, and cultural significance of the fishing resources themselves, of the perceptions of them by humans, and of the interactions Global North-Global South. The analysis also opens the dialogue about what kind of ocean and governance “science” we want, to support decisions, policies and practices regarding fisheries governance. Final thoughts highlight a reflection about whose knowledge is created and used to support decision and policy making in Ecuador

    Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics

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    Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting certain components in the traditional integration methods. Here, methods (1) and (2) relied on Long-Short-Term Memory (LSTM) architecture, with method (3) relying on convolutional neural networks. Pure ML methods to solve (nonlinear) PDEs are represented by Physics-Informed Neural network (PINN) methods, which could be combined with attention mechanism to address discontinuous solutions. Both LSTM and attention architectures, together with modern and generalized classic optimizers to include stochasticity for DL networks, are extensively reviewed. Kernel machines, including Gaussian processes, are provided to sufficient depth for more advanced works such as shallow networks with infinite width. Not only addressing experts, readers are assumed familiar with computational mechanics, but not with DL, whose concepts and applications are built up from the basics, aiming at bringing first-time learners quickly to the forefront of research. History and limitations of AI are recounted and discussed, with particular attention at pointing out misstatements or misconceptions of the classics, even in well-known references. Positioning and pointing control of a large-deformable beam is given as an example.Comment: 275 pages, 158 figures. Appeared online on 2023.03.01 at CMES-Computer Modeling in Engineering & Science
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