Zobrazeno 1 - 10
of 1 144
pro vyhledávání: '"P. Anderer"'
Publikováno v:
Proceedings of Interspeech 2024
This paper presents a benchmark dataset for aligning lecture videos with corresponding slides and introduces a novel multimodal algorithm leveraging features from speech, text, and images. It achieves an average accuracy of 0.82 in comparison to SIFT
Externí odkaz:
http://arxiv.org/abs/2409.16765
Autor:
M. M. van Gilst, B. M. Wulterkens, P. Fonseca, M. Radha, M. Ross, A. Moreau, A. Cerny, P. Anderer, X. Long, J. P. van Dijk, S. Overeem
Publikováno v:
BMC Research Notes, Vol 13, Iss 1, Pp 1-5 (2020)
Abstract Objective The maturation of neural network-based techniques in combination with the availability of large sleep datasets has increased the interest in alternative methods of sleep monitoring. For unobtrusive sleep staging, the most promising
Externí odkaz:
https://doaj.org/article/9523da9ccc46414ca5ce77e024ea625a
Autor:
Anderer, Matthias, Li, Feng
Publikováno v:
International Journal of Forecasting (2022)
Hierarchical forecasting with intermittent time series is a challenge in both research and empirical studies. Extensive research focuses on improving the accuracy of each hierarchy, especially the intermittent time series at bottom levels. Then hiera
Externí odkaz:
http://arxiv.org/abs/2103.08250
Autor:
Fons Schipper, Angela Grassi, Marco Ross, Andreas Cerny, Peter Anderer, Lieke Hermans, Fokke van Meulen, Mickey Leentjens, Emily Schoustra, Pien Bosschieter, Ruud J. G. van Sloun, Sebastiaan Overeem, Pedro Fonseca
Publikováno v:
Sensors, Vol 24, Iss 17, p 5717 (2024)
Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algo
Externí odkaz:
https://doaj.org/article/2b949ab816014a0ca955c56c9a0f3abb
Autor:
Pedro Fonseca, Marco Ross, Andreas Cerny, Peter Anderer, Fokke van Meulen, Hennie Janssen, Angelique Pijpers, Sylvie Dujardin, Pauline van Hirtum, Merel van Gilst, Sebastiaan Overeem
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-14 (2023)
Abstract This study describes a computationally efficient algorithm for 4-class sleep staging based on cardiac activity and body movements. Using an accelerometer to calculate gross body movements and a reflective photoplethysmographic (PPG) sensor t
Externí odkaz:
https://doaj.org/article/1d9458e787b04caf95576ccd6305c8e1
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Marco Ross, Pedro Fonseca, Sebastiaan Overeem, Ray Vasko, Andreas Cerny, Edmund Shaw, Peter Anderer
Publikováno v:
Frontiers in Physiology, Vol 14 (2023)
Introduction: The apnea-hypopnea index (AHI), defined as the number of apneas and hypopneas per hour of sleep, is still used as an important index to assess sleep disordered breathing (SDB) severity, where hypopneas are confirmed by the presence of a
Externí odkaz:
https://doaj.org/article/cabeade1c8394efebb000332c3bf4fc6
Autor:
Radha, Mustafa, Fonseca, Pedro, Ross, Marco, Cerny, Andreas, Anderer, Peter, Aarts, Ronald M.
Automated sleep stage classification using heart-rate variability is an active field of research. In this work limitations of the current state-of-the-art are addressed through the use of deep learning techniques and their efficacy is demonstrated. F
Externí odkaz:
http://arxiv.org/abs/1809.06221
Publikováno v:
Big Data and Cognitive Computing, Vol 8, Iss 1, p 2 (2023)
The emergence of generative language models (GLMs), such as OpenAI’s ChatGPT, is changing the way we communicate with computers and has a major impact on the educational landscape. While GLMs have great potential to support education, their use is
Externí odkaz:
https://doaj.org/article/fe6da94ea516411390be392aff5f897a