Searched for: subject%3A%22Convolutional%255C+Neural%255C+Network%22
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Taklimi, Sam (author)
The objective of this project is to train a model that transforms a tree with its foliage into only its branch structure. This is achieved by employing machine-learning techniques, specifically Generative Adverserial Networks (GANs). By utilizing the proposed method, a predictive model is built that automatically minimizes its own error function...
bachelor thesis 2024
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Sebus, Siert (author)
The Deep Neural Network (DNN) has become a widely popular machine learning architecture thanks to its ability to learn complex behaviors from data. Standard learning strategies for DNNs however rely on the availability of large, labeled datasets. Self-Supervised Learning (SSL) is a style of learning that allows models to also use unlabeled data...
master thesis 2024
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Hu, M. (author), Yue, N. (author), Groves, R.M. (author)
With the improvements in computational power and advances in chip and sensor technology, the applications of machine learning (ML) technologies in structural health monitoring (SHM) are increasing rapidly. Compared with traditional methods, deep learning based SHM (Deep SHM) methods are more efficient and have a higher accuracy. However, due...
journal article 2024
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Sharma, Bhawana (author), Sharma, Lokesh (author), Lal, C. (author), Roy, Satyabrata (author)
The Internet of Things (IoT) is currently seeing tremendous growth due to new technologies and big data. Research in the field of IoT security is an emerging topic. IoT networks are becoming more vulnerable to new assaults as a result of the growth in devices and the production of massive data. In order to recognize the attacks, an intrusion...
journal article 2024
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Magri, Federico (author)
In this study, we present a first step towards a cutting-edge software framework that will enable autonomous racing capabilities for nano drones. Through the integration of neural networks tailored for real-time operation on resource-constrained devices. A lightweight Convolutional Neural Network, with the Gatenet architecture, is adjusted for...
master thesis 2023
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Maiorano, Luigi (author)
While image-based georeferencing systems are widely available, none are designed for edge computing on board small satellites in space. Recent advances in autonomous data processing allow points of interest (POIs) to be identified in the captured images, enabling prioritization of data and reducing required downlink bandwidths. Directly...
master thesis 2023
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Basting, Mark (author)
In real-life scenarios, there are many variations in sizes of objects of the same category and the objects are not always placed at a fixed distance from the camera. This results in objects taking up an arbitrary size of pixels in the image. Vanilla CNNs are by design only translation equivariant and thus have to learn separate filters for...
master thesis 2023
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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
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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
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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
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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
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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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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
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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
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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
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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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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
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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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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
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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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