Zobrazeno 1 - 10
of 11 265
pro vyhledávání: '"A. Reinders"'
Current deep learning approaches in computer vision primarily focus on RGB data sacrificing information. In contrast, RAW images offer richer representation, which is crucial for precise recognition, particularly in challenging conditions like low-li
Externí odkaz:
http://arxiv.org/abs/2411.13150
Autor:
Mateus, Pedro, Garst, Swier, Yu, Jing, Cats, Davy, Harms, Alexander G. J., Birhanu, Mahlet, Beekman, Marian, Slagboom, P. Eline, Reinders, Marcel, van der Grond, Jeroen, Dekker, Andre, Jansen, Jacobus F. A., Beran, Magdalena, Schram, Miranda T., Visser, Pieter Jelle, Moonen, Justine, Ghanbari, Mohsen, Roshchupkin, Gennady, Vojinovic, Dina, Bermejo, Inigo, Mei, Hailiang, Bron, Esther E.
Biological age scores are an emerging tool to characterize aging by estimating chronological age based on physiological biomarkers. Various scores have shown associations with aging-related outcomes. This study assessed the relation between an age sc
Externí odkaz:
http://arxiv.org/abs/2409.01235
Autor:
Yan, Yuyang, Simons, Sami O., van Bemmel, Loes, Reinders, Lauren, Franssen, Frits M. E., Urovi, Visara
Voice signals originating from the respiratory tract are utilized as valuable acoustic biomarkers for the diagnosis and assessment of respiratory diseases. Among the employed acoustic features, Mel Frequency Cepstral Coefficients (MFCC) is widely use
Externí odkaz:
http://arxiv.org/abs/2408.07522
Autor:
Reinders, Samuel, Butler, Matthew, Zukerman, Ingrid, Lee, Bongshin, Qu, Lizhen, Marriott, Kim
Despite the recent surge of research efforts to make data visualizations accessible to people who are blind or have low vision (BLV), how to support BLV people's data analysis remains an important and challenging question. As refreshable tactile disp
Externí odkaz:
http://arxiv.org/abs/2408.04806
Evaluating anomaly detection algorithms in time series data is critical as inaccuracies can lead to flawed decision-making in various domains where real-time analytics and data-driven strategies are essential. Traditional performance metrics assume i
Externí odkaz:
http://arxiv.org/abs/2405.12096
Anomaly detection in time series data is crucial across various domains. The scarcity of labeled data for such tasks has increased the attention towards unsupervised learning methods. These approaches, often relying solely on reconstruction error, ty
Externí odkaz:
http://arxiv.org/abs/2405.07509
We leverage Physics-Informed Neural Networks (PINNs) to learn solution functions of parametric Navier-Stokes Equations (NSE). Our proposed approach results in a feasible optimization problem setup that bypasses PINNs' limitations in converging to sol
Externí odkaz:
http://arxiv.org/abs/2402.03153
Autor:
Holloway, Leona, Cracknell, Peter, Stephens, Kate, Fanshawe, Melissa, Reinders, Samuel, Marriott, Kim, Butler, Matthew
Refreshable tactile displays (RTDs) are predicted to soon become a viable option for the provision of accessible graphics for people who are blind or have low vision (BLV). This new technology for the tactile display of braille and graphics, usually
Externí odkaz:
http://arxiv.org/abs/2401.15836
Publikováno v:
BNAIC/BENELEARN 2023
Unlike the more commonly analyzed ECG or PPG data for activity classification, heart rate time series data is less detailed, often noisier and can contain missing data points. Using the BigIdeasLab_STEP dataset, which includes heart rate time series
Externí odkaz:
http://arxiv.org/abs/2311.13285
Autor:
Garst, Swier, Reinders, Marcel
Federated learning is a technique that enables the use of distributed datasets for machine learning purposes without requiring data to be pooled, thereby better preserving privacy and ownership of the data. While supervised FL research has grown subs
Externí odkaz:
http://arxiv.org/abs/2310.01195