document

Artificial Intelligence in Radiotherapy: Probabilistic Deep Learning for Dose Prediction and Anatomy Modeling

Pastor Serrano, O. (author)

doctoral thesis 2023

document

Football activity recognition: Improving and testing football activity recognition based on signal data using deep learning.

Tebbens, Ricardo (author)

There is a raising demand for player statistics in the world of football. With the developments over the last years in wearable sensors, Human Activity Recognition (HAR) based on wearable IMU sensors can be used to tackle this problem. This thesis builds upon an earlier research done for this topic, where an end-to-end pipeline based on deep...

master thesis 2023

document

Detection of patient-ventilator asynchrony based on esophageal pressure using a convolutional neural network

Ihaddouchen, Imane (author)

Introduction: In intensive care units (ICU), the most significant life support technology for patients with acute respiratory failure is mechanical ventilation. A mismatch between ventilatory support and patient demand is referred to as patient-ventilator asynchrony (PVA), and it is associated with a series of adverse...

master thesis 2023

document

Identification of Neolithic Circular Enclosures through Aerial Imagery

MENG, YUQI (author)

Traditionally, archaeological investigations, especially archaeological remains detection, mostly depend on human observation. In order to find the objects in large areas, a lot of fieldwork has to be done and it takes a long time for archaeologists to travel around. Nowadays, the development of LIDAR provides accurate 3D geometric information,...

master thesis 2023

document

Spatio-temporal embedding in deep learning for algal bloom forecasting

Bayraktar, Kerem (author)

The term ”Algal Bloom” refers to the accumulation of algae in a confined geological space. They may harm human health and negatively affect ecological systems around the area. Thus, forecasting algal blooms could mitigate the environmental and socio-economical damages. Particularly, the use of deep learning methods could distinguish underlying...

bachelor thesis 2023

document

Using and Abusing Equivariance: Investigating Differences between Exact and Approximate Equivariance in Computer Vision

Edixhoven, Tom (author)

In this work we show how Group Equivariant Convolutional Neural Networks use subsampling to learn to break equivariance to their symmetries. We focus on the 2D roto-translation group and investigate the impact of broken equivariance on network performance. We show that changing the input dimension of a network by as little as a single pixel can...

master thesis 2023

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

document

Digitization of chemical process flow diagrams using deep convolutional neural networks

Theisen, M.F. (author), Nishizaki Flores, K.F. (author), Schulze Balhorn, L. (author), Schweidtmann, A.M. (author)

Advances in deep convolutional neural networks led to breakthroughs in many computer vision applications. In chemical engineering, a number of tools have been developed for the digitization of Process and Instrumentation Diagrams. However, there is no framework for the digitization of process flow diagrams (PFDs). PFDs are difficult to...

journal article 2023

document

The Human Activity Radar Challenge: Benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University

Yang, Shufan (author), Le Kernec, Julien (author), Romain, Olivier (author), Fioranelli, F. (author), Cadart, Pierre (author), Fix, Jeremy (author), Ren, Chengfang (author), Manfredi, Giovanni (author), Letertre, Thierry (author)

journal article 2023

document

Physical pooling functions in graph neural networks for molecular property prediction

Schweidtmann, A.M. (author), Rittig, J. (author), Weber, J.M. (author), Grohe, Martin (author), Dahmen, Manuel (author), Leonhard, Kai (author), Mitsos, Alexander (author)

Graph neural networks (GNNs) are emerging in chemical engineering for the end-to-end learning of physicochemical properties based on molecular graphs. A key element of GNNs is the pooling function which combines atom feature vectors into molecular fingerprints. Most previous works use a standard pooling function to predict a variety of...

journal article 2023

document

Leveraging neural network uncertainty in adaptive unscented Kalman Filter for spacecraft pose estimation

Pasqualetto Cassinis, L. (author), Park, Tae Ha (author), Stacey, Nathan (author), D'Amico, Simone (author), Menicucci, A. (author), Gill, E.K.A. (author), Ahrns, Ingo (author), Sanchez-Gestido, Manuel (author)

This paper introduces an adaptive Convolutional Neural Network (CNN)-based Unscented Kalman Filter for the pose estimation of uncooperative spacecraft. The validation is carried out at Stanford's robotic Testbed for Rendezvous and Optical Navigation on the Satellite Hardware-In-the-loop Rendezvous Trajectories (SHIRT) dataset, which simulates...

journal article 2023

document

A New Logarithmic Quantization Technique and Corresponding Processing Element Design for CNN Accelerators

Jiang, Longxing (author)

Convolutional Neural Networks (CNN) have become a popular solution for computer vision problems. However, due to the high data volumes and intensive computation involved in CNNs, deploying CNNs on low-power hardware systems is still challenging.<br/>The power consumption of CNNs can be prohibitive in the most common implementation platforms:...

master thesis 2022

document

Monocular Vision-Based Pose Estimation of Uncooperative Spacecraft

Pasqualetto Cassinis, L. (author)

Activities in outer space have entered a new era of growth, fostering human development and improving key Earth-based applications such as remote sensing, navigation, and telecommunication. The recent creation of SpaceX's Starlink constellation as well as the steep increase in CubeSat launches are expected to revolutionize the way we use space...

doctoral thesis 2022

document

Automated monitoring of corrosion on piling sheets: a model test to understand the possibilities for asset managers

Maskam, Richie (author)

Various tasks in the construction industry are tedious due to the high amount of repetition or time-consuming nature. In recent years Deep Learning within computer vision has made it possible to automate various tasks using images. The Hoofdvaarweg Lemmer-Delfzijl has been assessed using images and a pointcloud. The images were being worked with...

master thesis 2022

document

Grasp-RCNN for a two-fingered pinch-gripper: A multiple RCNN approach

Lipman, Lars (author)

<b>Introduction </b>- Grasping unknown objects is an important ability for robots in logistic environments. While humans have an excellent understanding of how to grasp objects because of their visual perception and understanding of the 3D world, robotic grasping is still a challenge. Due to the fast-growing development of deep learning methods,...

master thesis 2022

document

Using Artificial Intelligence for Aerosol Data Assimilation

van Hemert, Guus (author)

To study the aerosols in the atmosphere is an important aspect for getting a better understanding of climate change. Therefore, it is important to get accurate observations of aerosols in the atmosphere as well as accurate emission fluxes of aerosol species. Satellite instruments such as SPEXone are able to measure aerosol properties with a high...

master thesis 2022

document

Methodologies for deep learning SCA: An analysis on the design and construction of convolutional neural networks for side-channel datasets

Blankendal, Philip (author)

Side-channel attacks leverage the unintentional leakage of information that indirectly relates to cryptographic secrets such as encryption keys. Previous settings would involve an attacker conducting some manual-statistical analysis to exploit this data and retrieve sensitive information from the target. With the adoption of deep learning...

master thesis 2022

document

Measuring fall risk of the elderly with IMU sensors by developing a Convolutional Neural Network

Claassen, Carlijn (author)

The combination of the high number and the consequences of falls in older adults led to the development of fall risk assessments; non-sensor-based and sensor-based. Multiple studies used ML for older adults' fall risk prediction using raw IMU data. This study's objective was to develop a DL algorithm that predicts the fall risk of people living...

master thesis 2022

document

Recognising Gestures Using Ambient Light and Convolutional Neural Networks: Adapting Convolutional Neural Networks for Gesture Recognition on Resource-constrained Microcontrollers

Narchi, William (author)

This paper presents how a convolutional neural network can be constructed in order to recognise gestures using photodiodes and ambient light. A number of candidates are presented and evaluated, with the most performant being adopted for in-depth analysis. This network is then compressed in order to be ran on an Arduino Nano 33 BLE...

bachelor thesis 2022

document

Full Color Deep Networks

Tahur, Nishad (author)

Color information has been shown to provide useful information during image classification. Yet current popular deep convolutional neural networks use 2-dimensional convolutional layers. The first 2-dimensional convolutional layer in the network combines the color channels of the input images, which produces feature maps per channel with only...

master thesis 2022