document

Meta-learning for few-shot on-chip sequence classification

den Blanken, Douwe (author)

The growing interest in edge computing is driving the demand for more efficient deep learning models that fit into resource-constrained edge devices like Internet-of-Things (IoT) sensors. The challenging limitations of these devices in terms of size and power has given rise to the field of tinyML, focusing on enabling low-cost machine learning...

master thesis 2023

document

Data-efficient resolution transfer with Continuous Kernel Convolutional Neural Networks

Haarman, Luuk (author)

Convolutional Neural Networks (CNNs) benefit from fine-grained details in high-resolution images, but these images are not always easily available as data collection can be expensive or time-consuming. Transfer learning pre-trains models on data from a related domain before fine-tuning on the main domain, and is a common strategy to deal with...

master thesis 2023

document

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

document

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

document

Low-Power Gesture Recognition Using Convolutional Neural Networks and Ambient Lighting

de Beer, Arne (author)

This paper presents a study focused on developing an efficient signal processing pipeline and identifying suitable machine learning models for real-time gesture recognition using a testbed consisting of an Arduino Nano 33 BLE and three OPT101 photodiodes. Our research aims to address the challenges of limited computational power whilst...

bachelor 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

document

Glacial Isostatic Adjustment Forward Model with a Convolutional Neural Network

Bouma, Quinten (author)

This thesis introduces a new approach to Glacial Isostatic Adjustment (GIA) modeling using Machine Learning (ML) techniques. The work addresses two main challenges – uncertainty in historical ice load history and the complexity of inverse problems – by developing two ML-based surrogate models (emulators) to rapidly estimate Relative Sea-Level ...

master thesis 2023

document

Data Augmentation for Deep Learning-based Gaze Estimation

Dijk, Jorn (author)

This study aims to provide insights in applying different data augmentation techniques to the input data of a convolutional neural network that estimates gaze. Gaze is used in numerous research domains for understanding and predicting emotions and actions from humans. Data augmentations consists of techniques to increase the size, variance and...

bachelor thesis 2023

document

Improving the Generalisability of Deep Learning NILM Algorithms using One-Shot Transfer Learning: Can one-shot transfer learning be leveraged to enhance the performance of a CNN-based NILM algorithm on unseen data?

Verschuren, Wim (author)

Non-Intrusive Load Monitoring (NILM) is a technique used to disaggregate household power consumption data into individual appliance components without the need for dedicated meters for each appliance. This paper focuses on improving the generalizability of NILM algorithms to unseen households using Convolutional Neural Networks (CNNs) and one...

bachelor thesis 2023

document

A study of the impact of CNN architecture variation on predicting brain activity using feature-weighted receptive fields

Murgoci, Vlad (author)

This study investigates the relationship between deep learning models and the human brain, specifically focusing on the prediction of brain activity in response to static visual stimuli using functional magnetic resonance imaging (fMRI). By leveraging intermediate outputs of pre-trained convolutional neural networks (CNNs) with feature-weighted...

bachelor thesis 2023

document

Deep learning approaches to short term traffic forecasting: Capturing the spatial temporal relation in historic traffic data

Kuiper, Thomas (author)

The amount of cars on the roads is increasing at a rapid pace, causing traffic jams to become commonplace. One way to decrease the amount of traffic congestion is by building an Intelligent Transportation System (ITS) which helps traffic flow optimally. An important tool for an ITS is short term traffic forecasting. Better forecasts will enable...

bachelor thesis 2023

document

Backdoor Attack on gaze estimation

Reda, Yuji (author)

Badnets are a type of backdoor attack that aims at manipulating the behavior of Convolutional Neural Networks. The training is modified such that when certain triggers appear in the inputs the CNN is going to behave accordingly. In this paper, we apply this type of backdoor attack to a regression task on gaze estimation. We examine different...

bachelor thesis 2023

document

Towards a low-cost air-written character recognition system: Designing an embedded machine learning system to recognise the first 10 letters of the Latin alphabet

Pronk, Paco (author)

This study introduces a novel system that leverages three photodiodes and ambient light to identify air-written characters on a resource-constrained device. Through experimentation, suitable methods of data preprocessing, machine learning and model compression were selected to recognize the first 10 characters of the Latin alphabet. The final...

bachelor thesis 2023

document

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

Pastor Serrano, O. (author)

doctoral thesis 2023

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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

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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