scholarly journals Estimating network structure via random sampling: Cognitive social structures and the adaptive threshold method

2012 ◽  
Vol 34 (4) ◽  
pp. 585-600 ◽  
Author(s):  
Michael D. Siciliano ◽  
Deniz Yenigun ◽  
Gunes Ertan
Keyword(s):  
Network Structure ◽  
Random Sampling ◽  
Adaptive Threshold ◽  
Social Structures ◽  
Threshold Method

13‐4: Adaptive Threshold Method for Dither Noise Reduction in Response Time Acceleration for 8K LCD TV

2021 ◽  
Vol 52 (1) ◽  
pp. 161-164
Author(s):  
Ho-Joon Chung ◽  
Hong-Ki Kwon ◽  
Jin-Yong Park ◽  
Tae-Woo Kim ◽  
Hyeon-Su Park ◽  
...  
Keyword(s):  
Response Time ◽  
Noise Reduction ◽  
Adaptive Threshold ◽  
Threshold Method

Visual interpretability analysis of Deep CNNs using an Adaptive Threshold method on Diabetic Retinopathy images

2021 ◽  
Author(s):  
George Ioannou ◽  
Tasos Papagiannis ◽  
Thanos Tagaris ◽  
Georgios Alexandridis ◽  
Andreas Stafylopatis
Keyword(s):  
Diabetic Retinopathy ◽  
Adaptive Threshold ◽  
Threshold Method

Comparison of the early aberration reporting system (EARS) W2 methods to an adaptive threshold method

2011 ◽  
Vol 30 (5) ◽  
pp. 489-504 ◽  
Author(s):  
John L. Szarka ◽  
Linmin Gan ◽  
William H. Woodall
Keyword(s):  
Reporting System ◽  
Adaptive Threshold ◽  
Threshold Method

Adaptive threshold method for recovered images of FMT

2018 ◽  
Vol 35 (2) ◽  
pp. 256 ◽  
Author(s):  
Huangjian Yi ◽  
Hongna Wei ◽  
Jinye Peng ◽  
Yuqing Hou ◽  
Xiaowei He
Keyword(s):  
Adaptive Threshold ◽  
Threshold Method

Locating Method of DPM 2D Barcode Image

2014 ◽  
Vol 989-994 ◽  
pp. 3865-3867 ◽  
Author(s):  
He Rong Zheng ◽  
Bing Shan ◽  
Zhi Liu
Keyword(s):  
Adaptive Threshold ◽  
High Light ◽  
Threshold Method ◽  
Filtering Method ◽  
2D Barcode ◽  
Otsu Method ◽  
Homomorphic Filtering ◽  
Positioning Method ◽  
Convex Hull Algorithm ◽  
Segmentation Problem

This paper studies the positioning method of DPM 2D barcode image. Because of the reflective problems of metal surface, the barcode image gotten will have uneven illumination, high light issues. In this paper, the homomorphic filtering method is used to enhance the image. Then the gradient projection is used to positioning the barcode region preliminary. We use an adaptive threshold method composed of Otsu method and neighborhood threshold method to solve the error segmentation problem. Finally the convex hull algorithm is applied to locate barcode region. Experiment results show that this method can effectively locate the barcode region quickly.

scholarly journals Offspring social network structure predicts fitness in families

2012 ◽  
Vol 279 (1749) ◽  
pp. 4914-4922 ◽  
Author(s):  
Nick J. Royle ◽  
Thomas W. Pike ◽  
Philipp Heeb ◽  
Heinz Richner ◽  
Mathias Kölliker
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Conflicts Of Interest ◽  
Parus Major ◽  
Social Structures ◽  
Emergent Properties ◽  
Social Network Structure ◽  
The Social ◽  
The Individual

Social structures such as families emerge as outcomes of behavioural interactions among individuals, and can evolve over time if families with particular types of social structures tend to leave more individuals in subsequent generations. The social behaviour of interacting individuals is typically analysed as a series of multiple dyadic (pair-wise) interactions, rather than a network of interactions among multiple individuals. However, in species where parents feed dependant young, interactions within families nearly always involve more than two individuals simultaneously. Such social networks of interactions at least partly reflect conflicts of interest over the provision of costly parental investment. Consequently, variation in family network structure reflects variation in how conflicts of interest are resolved among family members. Despite its importance in understanding the evolution of emergent properties of social organization such as family life and cooperation, nothing is currently known about how selection acts on the structure of social networks. Here, we show that the social network structure of broods of begging nestling great tits Parus major predicts fitness in families. Although selection at the level of the individual favours large nestlings, selection at the level of the kin-group primarily favours families that resolve conflicts most effectively.

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