Browsing by Author "Lee, Chi-Chun"

Now showing 1 - 2 of 2

Generating High-Order Transverse Patterns in Optically Pumped Semiconductor Lasers
(2019-11-01)
Tuan, Pi-Hui
;
Hsieh, Yen-Hui
;
Tu, Chin-Wei
;
Lee, Chi-Chun
;
Tsou, Chia-Han
High-order pattern formation in an optically pumped semiconductor laser (OPSL) under a selective pumping with varying spatial overlap between the pump beam and transverse modes is explored. In contrast to transverse pattern generation by off-axis pumped solid-state lasers where the mode order can be flexibly increased, experimental results reveal that the selective pumping fails to realize high-order mode operation in OPSLs when the pump-to-mode size ratio and pump power are insufficiently large. On the other hand, several high-order patterns belonging to the Hermite-Laguerre-Gaussian (HLG) modes are observed when scanning a large-ratio pump beam to specific positions on the gain chip. These HLG modes are experimentally confirmed to mainly originate from the transverse non-uniformity of present OPSL chip, while their structural features cannot be simply correlated with the pump scanning positions. Nevertheless, it is believed that the first-time observation of pure HLG modes under the large-ratio pumping can offer useful insights into the high-order pattern manipulation in OPSLs.
Investigation of functional brain network reconfiguration during vocal emotional processing using graph-theoretical analysis
(2019-05-01)
Lin, Shih-Yen
;
Lee, Chi-Chun
;
Chen, Yong-Sheng
;
Kuo, Li-Wei
;
資訊工程學系
Vocal expression is essential for conveying the emotion during social interaction. Although vocal emotion has been explored in previous studies, little is known about how perception of different vocal emotional expressions modulates the functional brain network topology. In this study, we aimed to investigate the functional brain networks under different attributes of vocal emotion by graph-theoretical network analysis. Functional magnetic resonance imaging (fMRI) experiments were performed on 36 healthy participants. We utilized the Power-264 functional brain atlas to calculate the interregional functional connectivity (FC) from fMRI data under resting state and vocal stimuli at different arousal and valence levels. The orthogonal minimal spanning trees method was used for topological filtering. The paired-sample t-test with Bonferroni correction across all regions and arousal-valence levels were used for statistical comparisons. Our results show that brain network exhibits significantly altered network attributes at FC, nodal and global levels, especially under high-arousal or negative-valence vocal emotional stimuli. The alterations within/between well-known large-scale functional networks were also investigated. Through the present study, we have gained more insights into how comprehending emotional speech modulates brain networks. These findings may shed light on how the human brain processes emotional speech and how it distinguishes different emotional conditions.