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Wyszukujesz frazę "Kowal, Marcin" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Virtual Reality Could Improve Exercise Performance on a Stationary Bike
Autorzy:
Kowal, Marta
Piskorz, Joanna
Czub, Marcin
Powiązania:
https://bibliotekanauki.pl/articles/2129930.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
stationary bike
cycling
optic flow
physical training
Virtual Reality
Opis:
The present study aimed to investigate the effects of manipulating visual information about one’s movement in Virtual Reality (VR) during physical training on a stationary bike. In the first experiment, the participants’ (N=30) task was to cycle on a stationary bike while embodying a virtual avatar. Fifteen participants experienced the Slow condition, in which a virtual avatar cycled at the constant speed of 15km/h, while the other fifteen participants experienced the Fast condition, in which a virtual avatar cycled at the constant speed of 35km/h. In the second experiment, we tested whether introducing agency (i.e., linking real-life cycling speed with the cycling speed of a virtual avatar), would improve exercise performance. Participants (N=31) experienced counterbalanced conditions: Faster optic flow (avatar’s speed was 15% faster than the participants’ real cycling speed), and Slower optic flow (avatar’s speed was 15% slower than the participants’ real cycling speed). Results showed that all participants increased their cycling speed when experiencing altered cycling speed of a virtual avatar compared with their baselines, but in the first experiment, participants cycled faster in the faster optic flow condition, while in the second experiment, when participants controlled the virtual avatar’s cycling speed, there were no differences between the Fast and Slow conditions. Participants described the cycling in VR as a pleasant experience. The present study suggests that the addition of Virtual Reality during exercise training may increase cycling performance.
Źródło:
Polish Psychological Bulletin; 2021, 52, 4; 365-372
0079-2993
Pojawia się w:
Polish Psychological Bulletin
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Breast cancer nuclei segmentation and classification based on a deep learning approach
Autorzy:
Kowal, Marek
Skobel, Marcin
Gramacki, Artur
Korbicz, Józef
Powiązania:
https://bibliotekanauki.pl/articles/1838197.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
breast cancer
nuclei segmentation
image processing
nowotwór piersi
segmentacja jądra
przetwarzanie obrazu
Opis:
One of the most popular methods in the diagnosis of breast cancer is fine-needle biopsy without aspiration. Cell nuclei are the most important elements of cancer diagnostics based on cytological images. Therefore, the first step of successful classification of cytological images is effective automatic segmentation of cell nuclei. The aims of our study include (a) development of segmentation methods of cell nuclei based on deep learning techniques, (b) extraction of some morphometric, colorimetric and textural features of individual segmented nuclei, (c) based on the extracted features, construction of effective classifiers for detecting malignant or benign cases. The segmentation methods used in this paper are based on (a) fully convolutional neural networks and (b) the marker-controlled watershed algorithm. For the classification task, seven various classification methods are used. Cell nuclei segmentation achieves 90% accuracy for benign and 86% for malignant nuclei according to the F-score. The maximum accuracy of the classification reached 80.2% to 92.4%, depending on the type (malignant or benign) of cell nuclei. The classification of tumors based on cytological images is an extremely challenging task. However, the obtained results are promising, and it is possible to state that automatic diagnostic methods are competitive to manual ones.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 85-106
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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