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Prediction of heat transfer to supercritical CO₂ flowing upward in a uniformly heated vertical tube: Master thesis

Krijgsman, Floris (author)

Supercritical CO₂ (SCO₂) is a promising alternative to traditional working fluids in heat pumps and power cycles due to its high density, thermal efficiency, and stability. These properties allow for the design of more compact and efficient equipment. However, accurately modeling supercritical heat transfer, especially near...

master thesis 2024

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Procedural Tree Generation: How to efficiently predict branching structures from foliage?

Taklimi, Sam (author)

The objective of this project is to train a model that transforms a tree with its foliage into only its branch structure. This is achieved by employing machine-learning techniques, specifically Generative Adverserial Networks (GANs). By utilizing the proposed method, a predictive model is built that automatically minimizes its own error function...

bachelor thesis 2024

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A Novel Onboard Georeferencing System for Small Satellites: Enabling Real-Time Localized Insights

Maiorano, Luigi (author)

While image-based georeferencing systems are widely available, none are designed for edge computing on board small satellites in space. Recent advances in autonomous data processing allow points of interest (POIs) to be identified in the captured images, enabling prioritization of data and reducing required downlink bandwidths. Directly...

master thesis 2023

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Augment it Maybe?: Improving Deep Vision Models with Adversarial Scene Text Augmentation

Sharma, Anirvin (author)

Image data augmentation has been regarded as a reliable and effective way to increase the data available for training. With the advent and rise of Generative AI, generative data augmentation has been shown to realize even better gains in performance for downstream tasks. However, these performance gains are often the cause of "extra information"...

master thesis 2023

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Design and Interpretation of a Convolutional Neural Network Architecture for Imaging Mass Spectrometry Data

CHANOPOULOU, IOANNA (author)

Convolutional Neural Networks (CNNs) have emerged primarily from research focusing on image classification tasks and as a result, most of the well-motivated design choices found in literature are relevant to computer vision applications. CNNs' application on Imaging Mass Spectrometry (IMS) data is quite recent and involves new challenges, such...

master thesis 2023

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Touchless Hand Gesture-Based Digit Recognition: using Light-Sensors, Convolutional Neural Networks and a Microcontroller

Smit, Winstijn (author)

Touchless interaction with computers has become more important in recent years, especially in the context of the COVID-19 pandemic.<br/>Applications include situations where touch input is not possible or not desirable, e.g. for hygienic purposes in a public setting or a medical setting.<br/>Practical examples for touchless interaction include...

bachelor thesis 2023

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ImECGnet: Cardiovascular Disease Classification from Image-Based ECG Data Using a Multi-branch Convolutional Neural Network

Ghahremani, A. (author), Lofi, C. (author)

Reliable Cardiovascular Disease (CVD) classification performed by a smart system can assist medical doctors in recognizing heart illnesses in patients more efficiently and effectively. Electrocardiogram (ECG) signals are an important diagnostic tool as they are already available early in the patients’ health diagnosis process and contain...

journal article 2023

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Toward Improved Real-Time Rainfall Intensity Estimation Using Video Surveillance Cameras

Zheng, Feifei (author), Yin, Hang (author), Ma, Yiyi (author), Duan, Huan Feng (author), Gupta, Hoshin (author), Savic, Dragan (author), Kapelan, Z. (author)

Under global climate change, urban flooding occurs frequently, leading to huge economic losses and human casualties. Extreme rainfall is one of the direct and key causes of urban flooding, and accurate rainfall estimates at high spatiotemporal resolution are of great significance for real-time urban flood forecasting. Using existing rainfall...

journal article 2023

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A Survey on Accelerating Sparse CNN Inference on GPUs

Chen, Qilin (author)

Convolutional neural networks (CNNs) are often pruned to achieve faster training and inference speed while also requiring less memory. Nevertheless, during computation, most modern GPUs cannot take advantage of the sparsity automatically, especially on networks with unstructured sparsity. Therefore, many libraries that exploit sparsity, have...

bachelor thesis 2022

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Hybrid Semantic Mapping for Autonomous Off-Road Driving

Bos, Roel (author)

Unmanned Ground Vehicle (UGV) navigation in unstructured off-road environments can benefit from accurate traversability estimation. Often, experiments with UGVs use semantic segmentation networks for visual scene understanding. Based on the pixel-wise classification of a semantic segmentation network, the UGV can distinguish traversable from non...

master thesis 2022

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Autonomous Navigation around Asteroids using Convolutional Neural Networks

van der Heijden, Lars (author)

Missions to small bodies are increasingly gaining interest as they might hold the secrets to our solar system’s origin while some are also posing a threat to life on Earth. The small size and irregular shape result in complex dynamics complicating the close-proximity operations. Furthermore, due to the long round-trip time communication delays...

master thesis 2022

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A Hybrid Spatial-temporal Sequence-to-one Neural Network Model for Lane Detection

Dong, Y. (author), Patil, Sandeep (author), Farah, H. (author), van Arem, B. (author)

Reliable and accurate lane detection is of vital importance for the safe performance of Lane Keeping Assistance and Lane Departure Warning systems. However, under certain challenging peculiar circumstances (e.g., marking degradation, serious vehicle occlusion), it is quite difficult to get satisfactory performance in accurately detecting the...

poster 2022

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Retrieval of forest height information using spaceborne LiDAR data: a comparison of GEDI and ICESat-2 missions for Crimean pine (Pinus nigra) stands

Vatandaslar, Can (author), Narin, O.G. (author), Abdikan, Saygin (author)

Key message: Despite showing a cost-effective potential for quantifying vertical forest structure, the GEDI and ICESat-2 satellite LiDAR missions fall short of the data accuracy standards required by tree- and stand-level forest inventories. Abstract: Tree and stand heights are key inventory variables in forestry, but measuring them manually...

journal article 2022

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Infrared thermal defect identification and reconstruction of artworks using a spatiotemporal deep neural network

Moradi, M. (author), Ghorbani, R. (author), Sfarra, Stefano (author), Tax, D.M.J. (author), Zarouchas, D. (author)

Assessment of cultural heritage assets is now extremely important all around the world. Non-destructive inspection is essential for preserving the integrity of the artworks while avoiding the loss of any precious materials that make it up. The use of Infrared Thermography (IRT) is an interesting concept since surface and subsurface faults can be...

conference paper 2022

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Estimating Rainfall Intensity Using an Image-Based Deep Learning Model

Yin, Hang (author), Zheng, Feifei (author), Duan, Huan-Feng (author), Savić, Dragan (author), Kapelan, Z. (author)

Urban flooding is a major issue worldwide, causing huge economic losses and serious threats to public safety. One promising way to mitigate its impacts is to develop a real-time flood risk management system; however, building such a system is often challenging due to the lack of high spatiotemporal rainfall data. While some approaches (i.e.,...

journal article 2022

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Prior-Guided Deep Interference Mitigation for FMCW Radars

Wang, J. (author), Li, Runlong (author), He, Yuan (author), Yang, Yang (author)

In this article, the interference mitigation (IM) problem is tackled as a regression problem. A prior-guided deep learning (DL)-based IM approach is proposed for frequency-modulated continuous-wave (FMCW) radars. Considering the complex-valued nature of radar signals, a complex-valued convolutional neural network, which is different from the...

journal article 2022

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Recurrent Convolutional Neural Network-Based Assessment of Power System Transient Stability and Short-Term Voltage Stability

Tapia, Estefania Alexandra (author), Colomé, Delia Graciela (author), Rueda, José L. (author)

Transient stability (TS) and short-term voltage stability (STVS) assessment are of fundamental importance for the operation security of power systems. Both phenomena can be mutually influenced in weak power systems due to the proliferation of power electronic interface devices and the phase-out of conventional heavy machines (e.g., thermal power...

journal article 2022

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Automated Machine Learning in Medical Image Segmentation

van Gruijthuijsen, Coen (author)

Semantic Segmentation of medical images are used to improve diagnosis and treatment. In recent years, the application of machine learning methods are increasingly used. However, the design of these models is difficult and time-consuming. In this thesis, we investigated the automation of this process using an Automated Machine Learning (AutoML)...

master thesis 2021

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Exploration of Deep Learning-based Computer Vision for the detection of floating plastic debris in waterways

Vallendar, André (author)

Plastic pollution is one of the most challenging global environmental problems. Currently, more than 1000 rivers transport approximately 80% of the plastic influx into the oceans. Naturally, more and more companies are interested in tackling this problem. One of them is Noria Sustainable Innovators, a company based in Delft (Netherlands). It is...

master thesis 2021

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Exploring Convolutional Neural Networks

Hoogenberg, Ruben (author)

In this thesis we have looked into the complexity of neural networks. Especially convolutional neural networks (CNNs), which are useful for image recognition, are looked into. In order to better understand the process in the neural networks, in the first half of this report a mathematical foundation for neural networks and CNNs is constructed....

bachelor thesis 2021