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

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

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

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

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MNEMOSENE++: Scalable Multi-Tile Design with Enhanced Buffering and VGSOT-MRAM based Compute-in-Memory Crossbar Array

Escuin, Carlos (author), García-Redondo, Fernando (author), Zahedi, M.Z. (author), Ibáñez, Pablo (author), Monreal, Teresa (author), Viñals, Victor (author), Llabería, José María (author), Myers, James (author), Ryckaert, Julien (author), Biswas, Dwaipayan (author), Catthoor, Francky (author)

This paper optimizes the MNEMOSENE architecture, a compute-in-memory (CiM) tile design integrating computation and storage for increased efficiency. We identify and address bottlenecks in the Row Data (RD) buffer that cause losses in performance. Our proposed approach includes mitigating these buffering bottlenecks and extending MNEMOSENE’s...

conference paper 2023

document

Jumping Shift: A Logarithmic Quantization Method for Low-Power CNN Acceleration

Jiang, Longxing (author), Aledo Ortega, D. (author), van Leuken, T.G.R.M. (author)

Logarithmic quantization for Convolutional Neural Networks (CNN): a) fits well typical weights and activation distributions, and b) allows the replacement of the multiplication operation by a shift operation that can be implemented with fewer hardware resources. We propose a new quantization method named Jumping Log Quantization (JLQ). The key...

conference paper 2023

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Predictive Maintenance Planning Using Renewal Reward Processes and Probabilistic RUL Prognostics: Analyzing the Influence of Accuracy and Sharpness of Prognostics

Mitici, M.A. (author), de Pater, I.I. (author), Zeng, Zhiguo (author), Barros, Anne (author)

We pose the maintenance planning for systems using probabilistic Remaining Useful Life (RUL) prognostics as a renewal reward process. Data-driven probabilistic RUL prognostics are obtained using a Convolutional Neural Network with Monte Carlo dropout. The maintenance planning model is illustrated for aircraft turbofan engines. The results show...

conference paper 2023

document

Neural cellular automata for solidification microstructure modelling

Tang, Jian (author), Kumar, Siddhant (author), De Lorenzis, Laura (author), Hosseini, Ehsan (author)

We propose Neural Cellular Automata (NCA) to simulate the microstructure development during the solidification process in metals. Based on convolutional neural networks, NCA can learn essential solidification features, such as preferred growth direction and competitive grain growth, and are up to six orders of magnitude faster than the...

journal article 2023

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Convolutional neural network framework for wind turbine electromechanical fault detection

Stone, Emilie (author), Giani, Stefano (author), Zappalá, D. (author), Crabtree, C.J. (author)

Effective and timely health monitoring of wind turbine gearboxes and generators is essential to reduce the costs of operations and maintenance activities, especially offshore. This paper presents a scalable and lightweight convolutional neural network (CNN) framework using high-dimensional raw condition monitoring data for the automatic...

journal article 2023

document

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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Constructing Health Indicators for Systems with Few Failure Instances Using Unsupervised Learning

de Pater, I.I. (author), Mitici, M.A. (author)

Health indicators are crucial to assess the health of complex systems. In recent years, several studies have developed data-driven health indicators using supervised learning methods. However, due to preventive maintenance, there are often not enough failure instances to train a supervised learning model, i.e., the data is unlabelled with an...

conference paper 2023

document

Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis

Sabbaqi, M. (author), Isufi, E. (author)

Devising and analysing learning models for spatiotemporal network data is of importance for tasks including forecasting, anomaly detection, and multi-agent coordination, among others. Graph Convolutional Neural Networks (GCNNs) are an established approach to learn from time-invariant network data. The graph convolution operation offers a...

journal article 2023