Searched for: subject%3A%22Network%22
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Lu, C.J. (author)
Real-time nonintrusive occupancy estimation can maximize the use of existing sensors to infer occupant information in buildings with the advantages of fewer privacy concerns and fewer extra device costs. Recently, many deep learning architectures have proven effective in estimating occupancy directly from raw sensor data. However, some...
journal article 2024
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Paredes-Vallés, Federico (author)
In the ever-evolving landscape of robotics, the quest for advanced synthetic machines that seamlessly integrate with human lives and society becomes increasingly paramount. At the heart of this pursuit lies the intrinsic need for these machines to perceive, understand, and navigate their surroundings autonomously. Among the senses, vision...
doctoral thesis 2023
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Holtgrefe, Tim (author)
Microtubules are long cylindrical polymers, assembled from tubulin proteins. Microtubule ends can be visualized using fluorescence and confocal microscopy. This allows for the study of microtubule dynamics. However, the manual annotation of microtubules is laborious, which is why automated tracking methods are used. In this project we have...
bachelor thesis 2023
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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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do Lago, Cesar A.F. (author), Giacomoni, Marcio H. (author), Bentivoglio, Roberto (author), Taormina, R. (author), Gomes, Marcus N. (author), Mendiondo, Eduardo M. (author)
Two-dimensional hydrodynamic models are computationally expensive. This drawback can limit their application to solving problems requiring real-time predictions or several simulation runs. Although the literature presented improvements in using Deep Learning as an alternative to hydrodynamic models, Artificial Neural Networks applications for...
journal article 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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Rahmani, S. (author), Baghbani, Asiye (author), Bouguila, Nizar (author), Patterson, Zachary (author)
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no...
journal article 2023
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Madadi, B. (author), Correia, Gonçalo (author)
This study proposes a hybrid deep-learning-metaheuristic framework with a bi-level architecture for road network design problems (NDPs). We train a graph neural network (GNN) to approximate the solution of the user equilibrium (UE) traffic assignment problem and use inferences made by the trained model to calculate fitness function...
journal article 2023
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Wang, Zhiyi (author)
Deep-learning models are commonly used in short-term precipitation forecasting. However, most deep-learning models are likely to produce blurry output problems. In order to get realistic and accurate results, AENN, a variant of Generative Adversarial Networks (GANs), has been developed. The AENN implements an additional temporal discriminator to...
master thesis 2022
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Zhou, H. (author)
Applying deep neural networks (DNNs) for system identification (SYSID) has attracted more andmore attention in recent years. The DNNs, which have universal approximation capabilities for any measurable function, have been successfully implemented in SYSID tasks with typical network structures, e.g., feed-forward neural networks and recurrent...
doctoral thesis 2022
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Yang, Ximei (author), Guendel, Ronny (author), Yarovoy, Alexander (author), Fioranelli, F. (author)
Human activities classification in assisted living is one of the emerging applications of radar. The conventional analysis considers micro-Doppler signatures as the chosen input for feature extraction or deep learning classification algorithms, or, less frequently, other radar data formats such as the range-time, the range-Doppler, or the...
conference paper 2022
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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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Wu, L. (author), Perin, G. (author), Picek, S. (author)
In the last decade, machine learning-based side-channel attacks have become a standard option when investigating profiling side-channel attacks. At the same time, the previous state-of-the-art technique, template attack, started losing its importance and was more considered a baseline to compare against. As such, most of the results reported...
journal article 2022
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Suryanarayanan, Surya Narayanan (author)
Inverse design with topology optimization has followed the same computational<br/>graph for decades. The unknown material density is distributed within a domain,<br/>a computational analysis predicts the response of that design and its derivative<br/>with respect to the unknown, and this information is used by a chosen gradient­<br/>based...
master thesis 2021
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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 &lt;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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Mikalauskas, Liudas (author)
Logging is a common practice in software development that assists developers with the maintenance of software. Logging a system optimally is a challenging task, thus Li et al. have proposed a state-of-the-art log recommendation model. However, no further attempts exist to improve the model or reproduce their results using different training data...
bachelor 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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Lourenço Baptista, M. (author), Henriques, Elsa M.P. (author), Prendinger, Helmut (author)
Traditionally, prognostics approaches to predictive maintenance have focused on estimating the remaining useful life of the equipment. However, from an industrial point of view, the goal is often not to predict the residual life but to determine the need for a maintenance action at a given time window. This approach allows us to frame the...
journal article 2021
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Li, G. (author), Knoop, V.L. (author), van Lint, J.W.C. (author)
Accurate and explainable short-term traffic forecasting is pivotal for making trustworthy decisions in advanced traffic control and guidance systems. Recently, deep learning approach, as a data-driven alternative to traffic flow model-based data assimilation and prediction methods, has become popular in this domain. Many of these deep learning...
journal article 2021
Searched for: subject%3A%22Network%22
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