1,461 research outputs found

    Simultaneous Multiparametric and Multidimensional Cardiovascular Magnetic Resonance Imaging

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    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    4D FLOW CMR in congenital heart disease

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    This thesis showed that the use of a cloud-based reconstruction applicationwith advanced eddy currents correction, integrated with interactiveimaging evaluation tools allowed for remote visualization and interpretationof 4D flow data and that was sufficient for gross visualizationof aortic valve regurgitation. Further, this thesis demonstrated that bulkflow and pulmonary regurgitation can be accurately quantified using 4Dflow imaging analyzed. Peak systolic velocity over the pulmonary valvemay be underestimated. However, the measurement of peak systolicvelocity can be optimized if measured at the level of highest velocity inthe pulmonary artery. Also correlated against invasive measurements (inan animal model), this thesis shows that aorta flow and pulmonary flowcan be accurately and simultaneously measured by 4D flow MRI.When applied in clinical practice, 4D flow has extra advantages, of beingable to visualize flow pattern, vorticity and to predict aortic growth. InASD patients it can measure shunt volume directly following the septumframe by frame. In Fontan patients in can visualize better than standardMRI the Fontan circuit and it can measure flow at multiple points alongthe Fontan circuit. We observed in our Fontan population that shunt lesionswere very common, most of the time via veno-venous collaterals.Further using advanced computations, we showed that WSS angle wasthe only independent predictor of aortic growth in BAV patients. We alsoshowed the feasibility of GLS analysis on 4D flow MRI and presented anintegrative approach in which flow and functional data are acquired inone sequence.From the technical point of view, 4D flow MRI has proved to complementthe traditional components of the standard cardiac MR exams, enablingin-depth insights into hemodynamics. At this moment it proved its addedvalue, but in most of the cases it is not able yet to replace the standardexam. This is still due to long scanning times and relatively longpost-processing times.<br/

    Machine learning in solar physics

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    The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

    Testing Structure-from-Motion imaging technique to quantify Blue mussels (Mytilus spp.) abundance

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    Masteroppgave i biologiBIO399MAMN-HAVSJMAMN-BI

    Enhancing Perception and Immersion in Pre-Captured Environments through Learning-Based Eye Height Adaptation

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    Pre-captured immersive environments using omnidirectional cameras provide a wide range of virtual reality applications. Previous research has shown that manipulating the eye height in egocentric virtual environments can significantly affect distance perception and immersion. However, the influence of eye height in pre-captured real environments has received less attention due to the difficulty of altering the perspective after finishing the capture process. To explore this influence, we first propose a pilot study that captures real environments with multiple eye heights and asks participants to judge the egocentric distances and immersion. If a significant influence is confirmed, an effective image-based approach to adapt pre-captured real-world environments to the user's eye height would be desirable. Motivated by the study, we propose a learning-based approach for synthesizing novel views for omnidirectional images with altered eye heights. This approach employs a multitask architecture that learns depth and semantic segmentation in two formats, and generates high-quality depth and semantic segmentation to facilitate the inpainting stage. With the improved omnidirectional-aware layered depth image, our approach synthesizes natural and realistic visuals for eye height adaptation. Quantitative and qualitative evaluation shows favorable results against state-of-the-art methods, and an extensive user study verifies improved perception and immersion for pre-captured real-world environments.Comment: 10 pages, 13 figures, 3 tables, submitted to ISMAR 202

    Automatic detection and classification of coastal Mediterranean fish from underwater images: Good practices for robust training

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    11 pages, 3 figures, 5 tables, supplementary material https://www.frontiersin.org/articles/10.3389/fmars.2023.1151758/full#supplementary-material.-- Data availability statement: The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary MaterialFurther investigation is needed to improve the identification and classification of fish in underwater images using artificial intelligence, specifically deep learning. Questions that need to be explored include the importance of using diverse backgrounds, the effect of (not) labeling small fish on precision, the number of images needed for successful classification, and whether they should be randomly selected. To address these questions, a new labeled dataset was created with over 18,400 recorded Mediterranean fish from 20 species from over 1,600 underwater images with different backgrounds. Two state-of-the-art object detectors/classifiers, YOLOv5m and Faster RCNN, were compared for the detection of the ‘fish’ category in different datasets. YOLOv5m performed better and was thus selected for classifying an increasing number of species in six combinations of labeled datasets varying in background types, balanced or unbalanced number of fishes per background, number of labeled fish, and quality of labeling. Results showed that i) it is cost-efficient to work with a reduced labeled set (a few hundred labeled objects per category) if images are carefully selected, ii) the usefulness of the trained model for classifying unseen datasets improves with the use of different backgrounds in the training dataset, and iii) avoiding training with low-quality labels (e.g., small relative size or incomplete silhouettes) yields better classification metrics. These results and dataset will help select and label images in the most effective way to improve the use of deep learning in studying underwater organismsProject DEEP-ECOMAR. 10.13039/100018685-Comunitat Autonoma de les Illes Balears through the Direcció General de Política Universitària i Recerca with funds from the Tourist Stay Tax law ITS 2017-006 (Grant Number: PRD2018/26). [...] The present research was carried out within the framework of the activities of the Spanish Government through the “María de Maeztu Centre of Excellence” accreditation to IMEDEA (CSIC-UIB) (CEX2021-001198-M) and the “Severo Ochoa Centre Excellence” accreditation to ICM-CSIC (CEX2019-000928-S) and the Research Unit Tecnoterra (ICM-CSIC/UPC)Peer reviewe

    Fringe platforms: An analysis of contesting alternatives to the mainstream social media platforms in a platformized public sphere

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    Social media companies are ubiquitous in our social lives and public debate. They provide spaces for discussion and grant us access to journalism. In his 1962 Strukturwandel der Öffentlichkeit, Jürgen Habermas described how the public sphere was transformed through the introduction of modern communication systems. With the advent of social media platforms, the public sphere has transformed again through ‘platformization’. Platformization is the process by which Big Tech companies infiltrate infrastructures, economic processes and governmental frameworks of entire public sectors, structuring them around their own practices and logics. This dissertation studies the contemporary platformized public sphere, not by focusing at the center of the public sphere, but by looking at the edges of the platform ecology, where radical or counter platform technology are situated. I do this through the concept of ‘fringe platforms’, which are defined as; alternative platform services that are established as an explicit critique of the ideological premises and practices of mainstream platform services, which strive to cause a shift in the norms of the platform ecology they contest by offering an ideologically different technology. One such platform is alt-right microblogging service Gab.com, which was subjected to a process of 'deplatformization' in 2018, when its user base was implicated in white supremacist terrorism. Deplatformization refers to tech companies’ efforts to reduce toxic content by pushing back controversial platforms and their communities to the edges of the ecosystem by denying them access to the basic infrastructural services required to function online. By studying Gab through three case studies this dissertation poses the following research questions: What is the role of fringe social media platforms in a platformized public sphere? What hierarchies and shifts in power do they signify? And how can they inform us about the platform ecosystem? In the first case study, I explore Gab as an ecosystem, and conclude that the study of fringe platforms entails a more explicit role in the analyses for a platform’s self-positioning and narrative, as well as a shift in focus from a platform as an ecosystem towards a lens that takes into account the (infra)structural consequences of a platform as part of an ecosystem of services. In the second and third case study, I oblige to this conclusion and examine Gab as part of the platform ecosystem, shifting the analytical lens to the power dynamics and infrastructures of the platformized public sphere. There, I conclude that deplatformization demonstrates how the power and influence of private technology platforms reaches far beyond their own boundaries, which reveals platform power as infrastructural and rule-setting power. In the conclusion chapter, I argue that the aforementioned fringe lens is useful, not only for the analysis of fringe platforms, but also for the platformized public sphere as a whole, as it makes the structures and infrastructures of the platformized public sphere visible; highlights power and discourse; focuses on dynamics, conflict and breakdown; and incorporates the dominant and democratically productive as well as the marginal and illiberal, in its analyses

    Digital technologies for enhancing crane safety in construction: a combined quantitative and qualitative analysis

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    A digital-enabled safety management approach is increasingly crucial for crane operations, which are common yet highly hazardous activities sensitive to environmental dynamics on construction sites. However, there exists a knowledge gap regarding the current status and developmental trajectory of this approach. Therefore, this paper aims to provide a comprehensive overview of digital technologies for enhancing crane safety, drawing insights from articles published between 2008 and 2021. Special emphasis is placed on the sensing devices currently in use for gathering “man-machine-environment” data, as well as the communication networks, data processing algorithms, and intuitive visualization platforms employed. Through qualitative and quantitative analysis of the literature, it is evident that while notable advancements have been made in digital-enabled crane safety management, these achievements remain largely confined to the experimentation stage. Consequently, a framework is proposed in this study to facilitate the practical implementation of digital-enabled crane safety management. Furthermore, recommendations for future research directions are presented. This comprehensive review offers valuable guidance for ensuring safe crane operations in the construction industry
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