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48 results on '"Lo, Sio-long"'

6. Classification of Three Anesthesia Stages Based on Near-Infrared Spectroscopy Signals

Author

Liu, Zhian, Si, Lichengxi, Shi, Shaoxian, Li, Jing, Zhu, Jing, Lee, Won Hee, Lo, Sio-Long, Yan, Xiangguo, Chen, Badong, Fu, Feng, Zheng, Yang, and Wang, Gang

Abstract

Proper monitoring of anesthesia stages can guarantee the safe performance of clinical surgeries. In this study, different anesthesia stages were classified using near-infrared spectroscopy (NIRS) signals with machine learning. The cerebral hemodynamic variables of right proximal oxyhemoglobin (HbO2) in maintenance (MNT), emergence (EM) and the consciousness (CON) stage were collected and then the differences between the three stages were compared by phase-amplitude coupling (PAC). Then combined with time-domain including linear (mean, standard deviation, max, min and range), nonlinear (sample entropy) and power in frequency-domain signal features, feature selection was performed and finally classification was performed by support vector machine (SVM) classifier. The results show that the PAC of the NIRS signal was gradually enhanced with the deepening of anesthesia level. A good three-classification accuracy of 69.27% was obtained, which exceeded the result of classification of any single category feature. These results indicate the feasibility of NIRS signals in performing three or even more anesthesia stage classifications, providing insight into the development of new anesthesia monitoring modalities.

Published
2024
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10. Motion Boundary Trajectory for Human Action Recognition

Author

Lo, Sio-Long, Tsoi, Ah-Chung, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Jawahar, C.V., editor, and Shan, Shiguang, editor

Published
2015
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31. Progressive Full Data Convolutional Neural Networks for Line Extraction from Anime-Style Illustrations.

Author

Xin, Yuchen, Wong, Hon-Cheng, Lo, Sio-Long, and Li, Junliang

Subjects
ARTIFICIAL neural networks, DEEP learning, PROGRESSIVE collapse, QUALITY control
Abstract

Anime-style comics are popular world-wide and an important industry in Asia. However, the output quantity and quality control of art workers have become the biggest obstacle to industrialization, and it is time consuming to produce new manga without the help of an intelligence assisted tool. As deep learning techniques have achieved great successes in different areas, it is worth exploring them to develop algorithms and systems for computational manga. Extracting line drawings from finished illustrations is one of the main tasks in drawing a manuscript and also a crucial task in the common painting process. However, traditional filters such as Sobel, Laplace, and Canny cannot output good results and require manual adjustments of the parameters. In order to address these problems, in this paper, we propose progressive full data convolutional neural networks for extracting lines from anime-style illustrations. Experimental results show that our progressive full data convolutional neural networks not only can learn as much as processing methods for the detailed regions, but also can accomplish the target extraction work with only a small training dataset. [ABSTRACT FROM AUTHOR]

Published
2020
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