Searched for: subject%3A%22deep%255C+learning%22
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Li, Y. (author)
Improving the efficiency in deploying deep neural networks (DNNs) and processing complex high-dimensional data has drawn increasing attention in recent years. Yet, the deployment of large DNN models is challenged by the high computational complexity and energy consumption, making it difficult to run on resource-constrained devices such as mobile...
doctoral thesis 2024
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Amalan, Akash (author)
<br/>The rapid advancement of artificial intelligence technologies has significantly increased the complexity of polymorphic and metamorphic malware, presenting new challenges to cybersecurity defenses. Our study introduces a novel bioinformatics-inspired approach, leveraging deep learning and phylogenetic analysis to understand the evolutionary...
master thesis 2024
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Yu, W. (author)
We are surrounded by all kinds of sounds at all times. What we hear varies with the physical environment and our position. Room impulse responses (RIRs) characterize the effect of the environment on a sound produced by a source. A first goal of this dissertation is to analyze RIRs and investigate how to extract environmental information from...
doctoral thesis 2024
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Determan, Brendan (author)
The inspection of extensive and hard-to-access sewer systems is a challenging and expensive task. As these networks age and need to comply with stricter health and environmental regulations, the demand for effective inspection solutions has increased. The introduction of technologies like CCTV (closed-circuit television) and SSET (sewer scanner...
master thesis 2024
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Liu, X. (author)
Deep learning is the core algorithmic tool for automatically processing large amounts of data. Deep learning models are defined as a stack of functions (called layers) with millions of parameters, that are updated during training by fitting them to data. Deep learning models have show remarkable accuracy gains on visual problems in video and...
doctoral thesis 2024
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Koetzier, Lennart (author)
Motivation: Effective management of liver tumors through thermal ablation requires precise monitoring of the ablation zone to ensure successful treatment outcomes. Computed tomography (CT) thermometry offers a promising non-invasive solution to monitor if tumor cells have been heated to the lethal temperature threshold. However, achieving...
master thesis 2024
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Enting, Marnix (author)
Neural Radiance Fields (NeRFs) have showcased remarkable effectiveness in capturing complex 3D scenes and synthesizing novel viewpoints. By inherently capturing the entire scene in a compact representation, they offer a promising avenue for applications such as simulators, where efficient storage of real-world data, fast rendering and dynamic...
master thesis 2024
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Vermeer, Jort (author)
The Random Finite Element Method (RFEM) is a robust stochastic method for slope reliability analysis that incorporates the spatial variability of soil properties. However, the extensive computational time associated with the direct Monte Carlo simulation limits its practical application. To overcome this problem, this study investigates the use...
master thesis 2024
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Kappé, Jeroen (author)
The city of Amsterdam faces the challenge of monitoring and assessing 200 kilometers of historic quay walls, of which much is deemed to be in poor condition. A key monitoring technique used is photogrammetry resulting in deformation testing. The fundamental data source forming the basis of this deformation analysis is a collection of overlapping...
master thesis 2024
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Yin, Junzhe (author)
The thesis explores an innovative technique for enhancing the precision of short-term weather forecasts, particularly in predicting extreme weather phenomena, which present a notable challenge for existing models such as PySTEPS due to their volatile behavior. Leveraging precipitation and meteorological data sourced from the Royal Netherlands...
master thesis 2024
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Graauw, Mitchel (author)
The dose computation algorithm, or dose engine, is one of the fundamental parts of radiotherapy treatment planning. These algorithms predict how the dose will be distributed inside the patient.<br/>Current dose engines are mainly based on either Monte Carlo simulations (MC) or pencil beam algorithms (PBA). MC being very precise, but relatively...
master thesis 2024
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Roy, Ankush (author)
Extreme precipitation, like floods and landslides, poses major risks to safety and the economy, underscoring the need for sophisticated weather forecasting to predict these events accurately, enhancing readiness and resilience. Nowcasting, which uses real-time atmospheric data to predict short-term weather, is key in addressing this challenge....
master thesis 2024
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Mertzanis, Nick (author)
This thesis investigates the integration of algorithm unrolling and genetic algorithms (GA) for optimizing pump scheduling in water distribution systems (WDS), a critical component for ensuring energy-efficient water delivery. In the context of modern civilization’s reliance on clean, affordable water for diverse uses, the operation of a WDS,...
master thesis 2024
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Rijpkema, Gerben (author)
Forensic microtrace investigation relies on a time- and labour-intensive process of manually analysing samples via microscopy. To aid forensic experts in their investigations, an image recognition model for microtrace localisation and classification is needed. This work investigates the trace recognition accuracy that can be achieved by...
master thesis 2024
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Lee, Y. (author)
E-learning has shifted the traditional learning paradigms in higher education, offering more flexible, ubiquitous, and personalized learning experiences. The previous years COVID-19 pandemic required a re-calibration of education to accommodate virtual learning environments from the traditional classroom-based education. Widespread learning...
doctoral thesis 2024
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Niemantsverdriet, Ruben (author)
The periconceptional period, encompassing the embryonic phase, is a critical window where a majority of reproductive failures, pregnancy complications, and adverse pregnancy outcomes arise. The Carnegie staging system comprises 23 stages which are based on embryonic morphological development. This allows for the assessment of normal and abnormal...
master thesis 2024
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Nelemans, Peter (author)
Fully distributed hydrological models take into account the spatial variability of a catchment, and allow for assessing its hydrological response at virtually any location. However, these models can be time-consuming when it comes to model runtime and calibration, especially for large-scale catchments. Meanwhile, deep learning models have shown...
master thesis 2024
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Doornbos, Marie-Claire (author)
Objective: This study introduces a novel deep-learning-based orientation recognition approach for detecting intraoperative lung orientation during robot-assisted anatomical resections, including lobectomy and segmentectomy. This method can potentially aid in anatomical structure identification, facilitate training and education, improve...
master thesis 2024
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Qin, Xusen (author)
Neural networks have made significant progress in domains like image recognition and natural language processing. However, they encounter the challenge of catastrophic forgetting in continual learning tasks, where they sequentially learn from distinct datasets. Learning a new task can lead to forgetting important information from previous tasks,...
master thesis 2024
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Sebus, Siert (author)
The Deep Neural Network (DNN) has become a widely popular machine learning architecture thanks to its ability to learn complex behaviors from data. Standard learning strategies for DNNs however rely on the availability of large, labeled datasets. Self-Supervised Learning (SSL) is a style of learning that allows models to also use unlabeled data...
master thesis 2024
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