Amplification of One-Way Information Complexity via Codes and Noise Sensitivity

Author(s):  
Marco Molinaro ◽  
David P. Woodruff ◽  
Grigory Yaroslavtsev
Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 696
Author(s):  
Haipeng Chen ◽  
Zeyu Xie ◽  
Yongping Huang ◽  
Di Gai

The fuzzy C-means clustering (FCM) algorithm is used widely in medical image segmentation and suitable for segmenting brain tumors. Therefore, an intuitionistic fuzzy C-means algorithm based on membership information transferring and similarity measurements (IFCM-MS) is proposed to segment brain tumor magnetic resonance images (MRI) in this paper. The original FCM lacks spatial information, which leads to a high noise sensitivity. To address this issue, the membership information transfer model is adopted to the IFCM-MS. Specifically, neighborhood information and the similarity of adjacent iterations are incorporated into the clustering process. Besides, FCM uses simple distance measurements to calculate the membership degree, which causes an unsatisfactory result. So, a similarity measurement method is designed in the IFCM-MS to improve the membership calculation, in which gray information and distance information are fused adaptively. In addition, the complex structure of the brain results in MRIs with uncertainty boundary tissues. To overcome this problem, an intuitive fuzzy attribute is embedded into the IFCM-MS. Experiments performed on real brain tumor images demonstrate that our IFCM-MS has low noise sensitivity and high segmentation accuracy.


2020 ◽  
Vol 54 (6) ◽  
pp. 482-489
Author(s):  
Daniel Shepherd ◽  
Marja Heinonen-Guzejev ◽  
Kauko Heikkilä ◽  
David Welch ◽  
Kim N. Dirks ◽  
...  

<b><i>Background:</i></b> Sensitivity to noise, or nuisance sounds that interrupt relaxation and task-related activities, has been shown to vary significantly across individuals. The current study sought to uncover predictors of noise sensitivity, focussing on possible social and cultural determinants, including social position, education, ethnicity, gender, and the presence of an illness. <b><i>Method:</i></b> Data were collected from 746 New Zealand adults residing in 6 areas differentiated by social position. Participants responded to questions probing personal characteristics, noise sensitivity, illness, neighbourhood problems, and noise annoyance. It was hypothesized that those in high-deprivation areas and/or experiencing illness report higher levels of noise sensitivity. <b><i>Results:</i></b> Approximately 50 and 10% of the participants reported being moderately or very noise sensitive, respectively. Significant predictors of noise sensitivity included age, length of residence, level of social deprivation, and self-reported illness. <b><i>Conclusion:</i></b> There is evidence of social determinants of noise sensitivity, including social position and residential factors.


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