Introducing stochastic recurrence interval to classification algorithms for identifying asperity patterns

2018 ◽  
Vol 512 ◽  
pp. 566-577
Author(s):  
K. Arvanitakis ◽  
M. Avlonitis ◽  
E. Papadimitriou
Keyword(s):  
Recurrence Interval ◽  
Classification Algorithms ◽  
Stochastic Recurrence

Spectral and Topographic Microstructure of Brain Alpha Activity During Drowsiness at Sleep Onset and REM Sleep

2000 ◽  
Vol 14 (3) ◽  
pp. 151-158 ◽  
Author(s):  
José Luis Cantero ◽  
Mercedes Atienza
Keyword(s):  
Rem Sleep ◽  
Spectral Component ◽  
Sleep Onset ◽  
Quantitative Information ◽  
Maximum Energy ◽  
Alpha Activity ◽  
The Other ◽  
Classification Algorithms ◽  
Microstructural Properties ◽  
Brain States

Abstract High-resolution frequency methods were used to describe the spectral and topographic microstructure of human spontaneous alpha activity in the drowsiness (DR) period at sleep onset and during REM sleep. Electroencephalographic (EEG), electrooculographic (EOG), and electromyographic (EMG) measurements were obtained during sleep in 10 healthy volunteer subjects. Spectral microstructure of alpha activity during DR showed a significant maximum power with respect to REM-alpha bursts for the components in the 9.7-10.9 Hz range, whereas REM-alpha bursts reached their maximum statistical differentiation from the sleep onset alpha activity at the components between 7.8 and 8.6 Hz. Furthermore, the maximum energy over occipital regions appeared in a different spectral component in each brain activation state, namely, 10.1 Hz in drowsiness and 8.6 Hz in REM sleep. These results provide quantitative information for differentiating the drowsiness alpha activity and REM-alpha by studying their microstructural properties. On the other hand, these data suggest that the spectral microstructure of alpha activity during sleep onset and REM sleep could be a useful index to implement in automatic classification algorithms in order to improve the differentiation between the two brain states.

Algorithms for segmentation and recognition of objects on medical images based on chiarlet transformation and neural networks

2020 ◽  
pp. 35-45
Author(s):  
Y.A. Hamad ◽  
K.V. Simonov ◽  
A.S. Kents
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Medical Images ◽  
Classification Algorithms ◽  
Visual Data ◽  
Recognition Of Objects

The paper considers general approaches to image processing, analysis of visual data and computer vision. The main methods for detecting features and edges associated with these approaches are presented. A brief description of modern edge detection and classification algorithms suitable for isolating and characterizing the type of pathology in the lungs in medical images is also given.

Study on performance of typical source camera classification algorithms

2011 ◽  
Vol 31 (4) ◽  
pp. 1133-1137 ◽  
Author(s):  
Chang-hui ZHOU ◽  
Yong-jian HU ◽  
Li-ling TAN
Keyword(s):  
Classification Algorithms
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