Searched for: subject%3A%22Convolutional%255C%252BNeural%255C%252BNetwork%22
(1 - 17 of 17)
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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
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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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Cuperman, Rafael (author), Jansen, K.M.B. (author), Ciszewski, M.G. (author)
Action statistics in sports, such as the number of sprints and jumps, along with the details of the corresponding locomotor actions, are of high interest to coaches and players, as well as medical staff. Current video-based systems have the disadvantage that they are costly and not easily transportable to new locations. In this study, we...
journal article 2022
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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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Chandra, Anant (author)
A Low-Pressure Micro-resistojet (LPM) is a type of in-space electrothermal propulsion system for satellites that works by heating low-pressure (50 to 300 Pa) fluid flowing through microchannels/slots (typically <1 mm diameter) using resistive heating elements like thin-film Molybdenum. This thesis delineates a response surface based method to...
master thesis 2021
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Cuperman Coifman, Rafael (author)
been given to Human Activity Recognition (HAR) based on signals obtained by IMUs placed on different body parts. This thesis studies the usage of Deep Learning-based models to recognize different football activities in an accurate, robust, and fast manner. Several deep architectures were trained with data captured with IMU sensors placed on...
master thesis 2021
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Thangavel, Prasanth (author), Thomas, John (author), Peh, Wei Yan (author), Jing, Jin (author), Yuvaraj, Rajamanickam (author), Cash, Sydney S. (author), Chaudhari, Rima (author), Saini, Vinay (author), Dauwels, J.H.G. (author)
Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefore, it is necessary to build an effective IED detector and an automatic method to classify IED-free versus IED EEGs. In this study, we evaluate features that may provide reliable IED detection...
journal article 2021
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Ewald, Vincentius (author), Sridaran Venkat, Ramanan (author), Asokkumar, Aadhik (author), Benedictus, R. (author), Boller, Christian (author), Groves, R.M. (author)
Predictive maintenance, as one of the core components of Industry 4.0, takes a proactive approach to maintain machines and systems in good order to keep downtime to a minimum and the airline maintenance industry is not an exception to this. To achieve this goal, practices in Structural Health Monitoring (SHM) complement the existing Non...
journal article 2021
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Zhu, Jianfeng (author), Sui, Lichun (author), Zang, Y. (author), Zheng, He (author), Jiang, Wei (author), Zhong, Mianqing (author), Ma, Fei (author)
In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and semantic segmentation, but they cannot be...
journal article 2021
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Pastor Serrano, O. (author), Lathouwers, D. (author), Perko, Z. (author)
Background and objective: One of the main problems with biomedical signals is the limited amount of patient-specific data and the significant amount of time needed to record the sufficient number of samples needed for diagnostic and treatment purposes. In this study, we present a framework to simultaneously generate and classify biomedical...
journal article 2021
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Wu, L. (author), Perin, G. (author)
In recent years, the advent of deep neural networks opened new perspectives for security evaluations with side-channel analysis. Profiling attacks now benefit from capabilities offered by convolutional neural networks, such as dimensionality reduction and the inherent ability to reduce the trace desynchronization effects. These neural...
conference paper 2021
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Simion-Constantinescu, Andrei (author)
This thesis presents a novel self-supervised approach of learning visual representations from videos containing human actions. Our approach tackles the complex problem of learning without the need of labeled data by exploring to what extent the ideas successfully used for images can be transferred, adapted and extended to videos for action...
master thesis 2020
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Wiersma, Ruben (author)
We present a new approach for deep learning on surfaces, combining geometric convolutional networks with rotationally equivariant networks. Existing work either learns rotationally invariant filters, or learns filters in the tangent plane without correctly relating orientations between different tangent planes (orientation ambiguity). We propose...
master thesis 2019
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van der Valk, Daan (author)
Side-channel attacks (SCA) aim to extract a secret cryptographic key from a device, based on unintended leakage. Profiled attacks are the most powerful SCAs, as they assume the attacker has a perfect copy of the target device under his control. In recent years, machine learning (ML) and deep learning (DL) techniques have became popular as...
master thesis 2019
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Gama, F. (author), Marques, Antonio G. (author), Leus, G.J.T. (author), Ribeiro, Alejandro (author)
Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals supported on graphs are introduced. We start with the selection graph neural network (GNN), which replaces linear time invariant filters with linear shift invariant graph filters to generate convolutional features and reinterprets pooling as a...
journal article 2019
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Gama, F. (author), Marques, Antonio G. (author), Leus, G.J.T. (author), Ribeiro, Alejandro (author)
In this ongoing work, we describe several architectures that generalize convolutional neural networks (CNNs) to process signals supported on graphs. The general idea of the replace time invariant filters with graph filters to generate convolutional features and to replace pooling with sampling schemes for graph signals. The different...
conference paper 2019
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Sorgedrager, Riemer (author)
This study focuses on automated malaria diagnosis in low quality blood smear images, captured by a low-cost smartphone based microscope system. The aim is to localize and classify the healthy and infected erythrocytes (red blood cells) in order to evaluate the parasitaemia in an infected blood smear. Due to the lower quality of the smartphone...
master thesis 2018
Searched for: subject%3A%22Convolutional%255C%252BNeural%255C%252BNetwork%22
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