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Hueber, Paul (author)
Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge owing to the constrained operational environment, which requires low latency and high energy efficacy. Previous benchmarks have provided limited insights into power consumption and latency. However, this study introduces algorithmic metrics...
master thesis 2024
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Simonov, Alex (author)
Machine learning, a pivotal aspect of artificial intelligence, has dramatically altered our interaction with technology and our handling of extensive data. Through its ability to learn and make decisions from patterns and previous experiences, machine learning is growing in influence on different aspects of our lives. It is, however, shown that...
master thesis 2024
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Basting, Mark (author)
In real-life scenarios, there are many variations in sizes of objects of the same category and the objects are not always placed at a fixed distance from the camera. This results in objects taking up an arbitrary size of pixels in the image. Vanilla CNNs are by design only translation equivariant and thus have to learn separate filters for...
master thesis 2023
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Tolsma, Pieter (author)
Transparency and specularity are challenging phenomena that modern depth perception systems have to deal with in order to be used in practice. A promising family of depth estimation methods is Multi-View Stereo (MVS), which combines multiple RGB images to predict depth, thus circumventing the need for costly specialized hardware. Although...
master thesis 2023
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Mukherjee, Sayak (author)
Current methods in Federated and Decentralized learning presume that all clients share the same model architecture, assuming model homogeneity. However, in practice, this assumption may not always hold due to hardware differences. While prior research has addressed model heterogeneity in Federated Learning, it remains unexplored in fully...
master thesis 2023
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Huizer, Rick (author)
Automated imaging systems, critical in domains like medical imaging, autonomous driving, and security, experience noise from camera sensors and electronic circuits in bad or dark lighting conditions. This impacts downstream tasks, including object detection. However, an analysis of strategies combining denoising and object detection is lacking....
master thesis 2023
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Găleşanu, Matei (author)
Neural Radiance Fields (NeRF) and their adaptations are known to be computationally intensive during both the training and the evaluating stages. Despite being the end goal, directly rendering a full-resolution representation of the scene is not necessary and not very practical for scenarios like streamed applications. Our goal is to design a...
bachelor thesis 2023
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Toader, Mihnea (author)
Neural radiance fields (NeRF) based solutions for novel view synthesis can achieve state of the art results. Recent work proposes models that take less time to render, need less training data or take up less space. However, few papers explore the use of NeRFs in classic rendering scenarios such as rasterization, which could contribute to wider...
bachelor thesis 2023
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Groenendijk, Jurre (author)
With the current state-of-the-art research, exporting a NeRF to a mesh has the side effect of having to evaluate a Multi Layer Perceptron at render-time, causing a significant decrease in performance. We have found a way to use K-Means clustering to pre-compute values for this MLP, storing them in multiple octahedron maps for the GPU to fetch...
bachelor thesis 2023
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Szilvásy, Medárd (author)
Radiance fields are a promising alternative to conventional 3D representations in the domain of novel view synthesis, with recent research achieving truly impressive photorealistic view synthesis results. In this paper, we deal with the concept of non-photorealistic rendering in the context of radiance fields, for generating more stylistic...
bachelor thesis 2023
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Van Steenweghen, Abel (author)
Over the past years the size of deep learning models has been growing consistently. This growth has led to significant improvements in performance, but at the expense of increased computational resource demands. Compression techniques can be used to improve the efficiency of deep learning models by shrinking their size and computational needs,...
master thesis 2023
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Şabanoğlu, Mahir (author)
An event-based camera enables capturing a video at a high temporal resolution, high dynamical range, reduced power consumption and minimal data bandwidth while the camera has minimal physical dimensions compared to a frame-based camera with the same vision properties. The limiting factor, however, of an event-based camera is the spatial...
master thesis 2023
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Lugtenburg, Jochem (author)
Auto-tagging systems can enrich music audio by providing contextual information in the form of tag predictions. Such context is valuable to solve problems within the MIR field. The majority of re- cent auto-tagging research, however, only considers a fraction of tags from the full set of available annotations in the original datasets. Because of...
master thesis 2022
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te Nijenhuis, Frank (author)
The efficacy of endovascular therapy in large vessel occlusion (LVO) of the anterior circulation is dependent to a high degree on the selection of patients who are likely to benefit from this procedure. To this end, functional outcome prediction based on clinical parameters is an active area of research. In the preoperative screening of LVO...
master thesis 2022
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Dai, Renjie (author)
Workflow analysis aims to improve the efficiency and safety in operating rooms by analysing surgical processes and providing feedback or support, where observations can be made and evaluated by algorithms rather than human experts. For our study, we mount five calibrated cameras from different angles in a Catheterization Laboratory (Cath Lab) to...
master thesis 2022
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Sharma, Agrim (author)
Traditionally, convolutional neural networks are feedforward networks with a deep and complex hierarchy. Conversely, the human brain has a relatively shallow hierarchy with recurrent connections. Replicating this recurrence may allow for shallower and easier to understand computer vision models that may possess characteristics usually attributed...
master thesis 2022
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Shirekar, Ojas (author)
A primary trait of humans is the ability to learn rich representations and relationships between entities from just a handful of examples without much guidance. Unsupervised few-shot learning is an undertaking aimed at reducing this fundamental gap between smart human adaptability and machines. We present a contrastive learning scheme for...
master thesis 2022
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Singh, Anuj (author)
The versatility to learn from a handful of samples is the hallmark of human intelligence. Few-shot learning is an endeavour to transcend this capability down to machines. Inspired by the promise and power of probabilistic deep learning, we propose a novel variational inference network for few-shot classification (coined as TRIDENT) to decouple...
master thesis 2022
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Sangers, Ruben (author)
Contactless measurement of changes in blood volume by exploiting the color fluctuations in the face is a technique commonly referred to as remote photoplethysmography (rPPG). Recent developments show promising results for heart rate estimation from low-cost cameras, making applications in remote healthcare possible. Remote PPG applications in at...
master thesis 2022
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Geijsen, Kamron (author)
This paper presents a preliminary study of the set of trade-offs of UC-Berkley’s RISC-V instruction set architecture experiences, due to its lack of the Scaled Index addressing mode. The strong majority of the popular Instruction Sets such as x86, ARM, MIPS and PowerPC include relatively complex ways to calculate memory addresses (addressing...
bachelor thesis 2022
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