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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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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

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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

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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

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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

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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

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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

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Detecting strawberries using different Convolutional Neural Networks

Bechtold, Jeroen (author)

This paper tries to combat the food waste of strawberries during the harvesting steps.<br/>An automatic pipeline must be established to combat this food waste.<br/>One of the steps needed in this pipeline is detecting strawberries in images.<br/>Therefore, this paper aims to find out which Convolutional Neural Network (CNN) can be best used to...

bachelor thesis 2022

document

Lightsail design with neural optimization of topology

Norder, Lucas (author)

Conventional Topology Optimization (TO) enables the inverse design of nanophotonic structures by specifying the objective and constraints without a predefined topological concept. Yet, extreme scenarios such as the design of a...

master thesis 2022

document

Mitigation of Weather effects on Optical Satellite Imagery

Eijgenraam, Bram (author)

How to deal with the presence of weather affected data is an unavoidable topic in the processing of optical imagery. Clouds and cloud shadows significantly alter the spectral signatures obtained from satellite data, which often leads to problems for any kind of scientific analysis. In this research there has been elaborated on two different kind...

master thesis 2022

document

How does a CNN mixed with LSTM methods compare with the individual one in predicting earthquakes?

Hashmi, Irtaza (author)

Earthquakes are one of the most dangerous natural disasters that occur worldwide. Predicting them is one of the unsolved problems in the field of science. In the past decade, there has been an increase in seismic monitoring stations worldwide, which has allowed us to design and implement data-driven and deep learning solutions. In this paper, we...

bachelor thesis 2022

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Preventive-corrective contingency control of distribution power grids using convolutional neural networks

Treurniet, Wiljo (author)

To achieve the goals on greenhouse gas emissions, the energy supply and demand is in transition. Distribution power grids therefore are increasingly reaching their capacity limits due to electrification and the vast increase of distributed energy resource (DER) connection requests with large peak power output. Increasing physical grid capacity...

master thesis 2022

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Individual Prediction Modelling for Air Traffic Control using Supervised Learning

Kloosterman, Luc (author)

In the future, Air Traffic Controllers are expected to work together with more advanced computer-based automation that can automatically take action. The main challenge is then how to design computer-based tools such that they foster acceptance among air traffic controllers. One possible approach to foster acceptance is by matching the automated...

master thesis 2022

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Convolutional neural network for predicting crack pattern and stress-crack width curve of air-void structure in 3D printed concrete

Chang, Z. (author), Wan, Z. (author), Xu, Y. (author), Schlangen, E. (author), Šavija, B. (author)

Extrusion-based 3D concrete printing (3DCP) results in deposited materials with complex microstructures that have high porosity and distinct anisotropy. Due to the material heterogeneity and rapid growth of cracks, fracture analysis in these air-void structures is often complex, resulting in a high computational cost. This study proposes a...

journal article 2022

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Map-Grasp: A deep learning approach to bin-picking

Wiersma, Mark (author)

Automated bin-picking is a difficult task that requires solving multiple robotic vision problems including object detection and grasp proposal generation. Current methods use deep learning to approach each of the vision problems of bin-picking separately with the main focus on generating the grasp proposals. For grasp proposal generation, neural...

master thesis 2021

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Classifying Human Manual Control Behavior in Tracking Tasks with Various Display types Using the InceptionTime CNN

Verkerk, Gertjan (author)

Despite the increasing amount of automation in manual control tasks, such as driving a car or piloting an aircraft, the human ability to adapt to unexpected events still makes us an essential part of the control loop. Before we can remove the human completely, a better understanding of this unique characteristic is necessary so that we can apply...

master thesis 2021

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Image Acquisition and Attenuation Map Estimation for Multi-pinhole Clinical SPECT

Chen, Y. (author)

Single-photon emission computed tomography (SPECT) is a well-established nuclear imaging modality for studying functional and pathological properties of the brain. Conventional general purpose SPECT systems typically offer a spatial resolution of about 10 mm with a sensitivity of 0.01-0.02%. A few dedicated brain SPECT scanners have been...

doctoral thesis 2021

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Convolutional neural network based attenuation correction for ¹²³I-FP-CIT SPECT with focused striatum imaging

Chen, Y. (author), Goorden, M.C. (author), Beekman, F.J. (author)

SPECT imaging with 123I-FP-CIT is used for diagnosis of neurodegenerative disorders like Parkinson's disease. Attenuation correction (AC) can be useful for quantitative analysis of 123I-FP-CIT SPECT. Ideally, AC would be performed based on attenuation maps (μ-maps) derived from perfectly registered CT scans. Such μ-maps, however, are most...

journal article 2021

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Hear-and-avoid for unmanned air vehicles using convolutional neural networks

Wijnker, D.C. (author), van Dijk, Tom (author), Snellen, M. (author), de Croon, G.C.H.E. (author), de Wagter, C. (author)

To investigate how an unmanned air vehicle can detect manned aircraft with a single microphone, an audio data set is created in which unmanned air vehicle ego-sound and recorded aircraft sound are mixed together. A convolutional neural network is used to perform air traffic detection. Due to restrictions on flying unmanned air vehicles close to...

journal article 2021

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Probabilistic wind power forecasting combining deep learning architectures

Arends, Eric Lacoa (author), Watson, S.J. (author), Basu, S. (author), Cheneka, B.R. (author)

A series of probabilistic models were bench-marked during the European Energy Markets forecasting Competition 2020 to assess their relative accuracy in predicting aggregated Swedish wind power generation using as input historic weather forecasts from a numerical weather prediction model. In this paper, we report the results of one of these...

conference paper 2020