document

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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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

document

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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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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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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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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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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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

document

How does imbalanced data affect performance of regression CNNs?

Thakoersingh, Ratish (author)

This research provides an overview on how training Convolutional Neural Networks (CNNs) on imbalanced datasets affect the performance of the CNNs. Datasets could be imbalanced as a result of several reasons. There are for example naturally less samples of rare diseases. Since the network is trained less on those instances, this might lead to...

bachelor thesis 2021

document

Go Deep or Go Home?

den Heijer, Remco (author)

Does a convolutional neural network (CNN) always have to be deep to learn a task? This is an important question as deeper networks are generally harder to train. We trained shallow and deep CNNs and evaluated their performance on simple regression tasks, such as computing the mean pixel value of an image. For these simple tasks we show that...

bachelor thesis 2021

document

Active Perception in Autonomous Fruit Harvesting: Viewpoint Optimization with Deep Reinforcement Learning

Klaoudatos, Dimitrios (author)

This MSc thesis presents the development of a viewpoint optimization framework to face the problem of detecting occluded fruits in autonomous harvesting. A Deep Reinforcement Learning (DRL) algorithm is developed in order to train a robotic manipulator to navigate to occlusion-free viewpoints of the tomato-target. Two Convolutional Neural...

master thesis 2021

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Automated detection of methane emissions in TROPOMI data using Neural Networks

Schuit, Berend (author)

master thesis 2021

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Reinforcement Learning for Profiled Side-Channel Analysis: Applications of Q-Learning in the SCA Domain

Rijsdijk, Jorai (author)

Side-channel attacks (SCA), which use unintended leakage to retrieve a secret cryptographic key, have become more sophisticated over time. With the recent successes of machine learning (ML) and especially deep learning (DL) techniques against cryptographic implementations even in the presence of dedicated countermeasures, various methods have...

master thesis 2020

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Robust Navigation Framework for Proximity Operations around Uncooperative Spacecraft: A monocular vision-based navigation approach using deep learning

Barad, Kuldeep (author)

Autonomous vision-based navigation is a crucial element for space applications involving a potentially uncooperative target, such as proximity operations for on-orbit servicing or active debris removal. Due to low mass and power characteristics, monocular vision sensors are an attractive choice for onboard vision-based navigation systems. This...

master thesis 2020

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Weight Swapping: A new method for Supervised Domain Adaptation in Computer Vision using Discrete Optimization

Datta, Leonid (author)

Training Convolutional Neural Network (CNN) models is difficult when there is a lack of labeled training data and no unlabeled data is available. A popular method for this is domain adaptation where the weights of a pre-trained CNN model are transferred to the problem setup. The model is pre-trained on the same task but in a different domain...

master thesis 2020

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A Survey On Convolutional Neural Network Explainability Methods

Bouman, Nikki (author), Jaggi, Vanisha (author), Khattat, Mostafa (author), Salami, Nima (author), Wernet, Victor (author), Zonneveld, Wouter (author)

Artificial Intelligence (AI) is increasingly affecting people’s lives. AI is even employed in fields where human lives depend on the AI’s decisions. However, these algorithms lack transparency, i.e. it is unclear how they determine the outcome. If, for instance, the AI’s purpose is to classify an image, the AI will learn this from examples...

student report 2019