2,199 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2022-2023

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    Study of compression techniques for partial differential equation solvers

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    Partial Differential Equations (PDEs) are widely applied in many branches of science, and solving them efficiently, from a computational point of view, is one of the cornerstones of modern computational science. The finite element (FE) method is a popular numerical technique for calculating approximate solutions to PDEs. A not necessarily complex finite element analysis containing substructures can easily gen-erate enormous quantities of elements that hinder and slow down simulations. Therefore, compression methods are required to decrease the amount of computational effort while retaining the significant dynamics of the problem. In this study, it was decided to apply a purely algebraic approach. Various methods will be included and discussed, ranging from research-level techniques to other apparently unrelated fields like image compression, via the discrete Fourier transform (DFT) and the Wavelet transform or the Singular Value Decomposition (SVD)

    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/

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Review of deep learning approaches in solving rock fragmentation problems

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    One of the most significant challenges of the mining industry is resource yield estimation from visual data. An example would be identification of the rock chunk distribution parameters in an open pit. Solution of this task allows one to estimate blasting quality and other parameters of open-pit mining. This task is of the utmost importance, as it is critical to achieving optimal operational efficiency, reducing costs and maximizing profits in the mining industry. The mentioned task is known as rock fragmentation estimation and is typically tackled using computer vision techniques like instance segmentation or semantic segmentation. These problems are often solved using deep learning convolutional neural networks. One of the key requirements for an industrial application is often the need for real-time operation. Fast computation and accurate results are required for practical tasks. Thus, the efficient utilization of computing power to process high-resolution images and large datasets is essential. Our survey is focused on the recent advancements in rock fragmentation, blast quality estimation, particle size distribution estimation and other related tasks. We consider most of the recent results in this field applied to open-pit, conveyor belts and other types of work conditions. Most of the reviewed papers cover the period of 2018-2023. However, the most significant of the older publications are also considered. A review of publications reveals their specificity, promising trends and best practices in this field. To place the rock fragmentation problems in a broader context and propose future research topics, we also discuss state-of-the-art achievements in real-time computer vision and parallel implementations of neural networks

    A Routine and Post-disaster Road Corridor Monitoring Framework for the Increased Resilience of Road Infrastructures

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    Deep Neural Network Compression with Filter Pruning

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    The rapid development of convolutional neural networks (CNNs) in computer vision tasks has inspired researchers to apply their potential to embedded or mobile devices. However, it typically requires a large amount of computation and memory footprint, limiting their deployment in those resource-limited systems. Therefore, how to compress complex networks while maintaining competitive performance has become the focus of attention in recent years. On the subject of network compression, filter pruning methods that achieve structured compact model by finding and removing redundant filters, have attracted widespread attention. Inspired by previous dedicated works, this thesis focuses on the way to obtain the compact model while maximizing the retention of the original model performance. In particular, aiming at the limitations of choosing filters on the existing popular pruning methods, several novel filter pruning strategies are proposed to find and remove redundant filters more accurately to reduce the performance loss of the model caused by pruning. For instance, the filter pruning method with an attention mechanism (Chapter 3), data-dependent filter pruning guided by LSTM (Chapter 4), and filter pruning with uniqueness mechanism in the frequency domain (Chapter 5). This thesis first addresses the filter pruning issue from a global perspective. To this end, we propose a new scheme, termed Pruning Filter with an Attention Mechanism (PFAM). That is, by establishing the dependency/relationship between filters at each layer, we explore the long-term dependence between filters via attention module in order to choose the tobe-pruned filters. Unlike prior approaches that identify the to-be-pruned filters simply based on their intrinsic properties, the less correlated filters are first pruned after the pruning step in the current training epoch and then reconstructed and updated during the subsequent training epoch. Thus, the compressed network model can be achieved without the requirement for a pre-trained model since input data can be manipulated with the maximum information maintained when the original training strategy is executed. Next, it is noticed that most existing pruning algorithms seek to prune the filter layer by layer. Specifically, they guide filter pruning at each layer by setting a global pruning rate, which indicates that each convolutional layer is treated equally without regard to its depth and width. In this situation, we argue that the convolutional layers in the network also have varying degrees of significance. Besides, we propose that selecting the appropriate layers for pruning is more reasonable since it can result in more complexity reduction with less performance loss by keeping and removing more filters in those critical and nonsignificant layers, respectively. In order to do this, long short-term memory (LSTM) is employed to learn the hierarchical properties of a network and to generalize a global network pruning scheme. On top of that, we present a data-dependent soft pruning strategy named Squeeze-Excitation-Pruning (SEP), which does not physically prune any filters but removes specific kernels involved in calculating forward and backward propagations based on the pruning scheme. Doing so can further decrease the model’s performance decline while achieving a deep model compression. Lastly, we transfer the concept of relationship from the filter level to the feature map level because the feature maps can reflect the comprehensive information of both input data and filters. Hence, we propose Filter Pruning with Uniqueness Mechanism in the Frequency Domain (FPUM) to serve as a guideline for the filter pruning strategy by generating the correlation between feature maps. Specifically, we first transfer features to the frequency domain by Discrete Cosine Transform (DCT). Then, for each feature map, we compute a uniqueness score, which measures its probability of being replaced by others. Doing so allows us to prune the filters corresponding to the low-uniqueness maps without significant performance degradation. In addition, our strategy is more resistant to noise than spatial methods, further enhancing the network’s compactness while maintaining performance, as the critical pruning clues are more concentrated following DCT

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum
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