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

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...

Autonomous Navigation around Asteroids using Convolutional Neural Networks

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...

Automated Machine Learning in Medical Image Segmentation

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)...

Exploring Convolutional Neural Networks

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....

Automated detection of methane emissions in TROPOMI data using Neural Networks

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

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...

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

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...