Texture classification based on image (natural and horizontal) visibility graph constructing methods

2021 ◽  
Vol 31 (1) ◽  
pp. 013128
Author(s):  
Laifan Pei ◽  
Zhaohui Li ◽  
Jie Liu
Keyword(s):  
Texture Classification ◽  
Visibility Graph ◽  
Horizontal Visibility ◽  
Horizontal Visibility Graph

scholarly journals Analysing the Dynamics of Interbeat Interval Time Series Using Grouped Horizontal Visibility Graph

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 9926-9934 ◽  
Author(s):  
Gulraiz Iqbal Choudhary ◽  
Wajid Aziz ◽  
Ishtiaq Rasool Khan ◽  
Susanto Rahardja ◽  
Pasi Franti
Keyword(s):  
Time Series ◽  
Visibility Graph ◽  
Interbeat Interval ◽  
Horizontal Visibility ◽  
Interval Time ◽  
Interval Time Series ◽  
Horizontal Visibility Graph

Inference On Streamflow Predictability Using Horizontal Visibility Graph Based Networks

2020 ◽  
Author(s):  
Ganesh Ghimire ◽  
Navid Jadidoleslam ◽  
Witold Krajewski ◽  
Anastasios Tsonis
Keyword(s):  
Time Series ◽  
Time Scale ◽  
Temporal Trends ◽  
Characteristic Exponent ◽  
Visibility Graph ◽  
Clustering Coefficient ◽  
Series Data ◽  
Dynamical Process ◽  
Horizontal Visibility ◽  
Horizontal Visibility Graph

<p>Streamflow is a dynamical process that integrates water movement in space and time within basin boundaries. The authors characterize the dynamics associated with streamflow time series data from about seventy-one U.S. Geological Survey (USGS) stream-gauge stations in the state of Iowa. They employ a novel approach called visibility graph (VG). It uses the concept of mapping time series into complex networks to investigate the time evolutionary behavior of dynamical system. The authors focus on a simple variant of VG algorithm called horizontal visibility graph (HVG). The tracking of dynamics and hence, the predictability of streamflow processes, are carried out by extracting two key pieces of information called characteristic exponent, λ of degree distribution and global clustering coefficient, GC pertaining to HVG derived network. The authors use these two measures to identify whether streamflow process has its origin in random or chaotic processes. They show that the characterization of streamflow dynamics is sensitive to data attributes. Through a systematic and comprehensive analysis, the authors illustrate that streamflow dynamics characterization is sensitive to the normalization, and the time-scale of streamflow time-series. At daily scale, streamflow at all stations used in the analysis, reveals randomness with strong spatial scale (basin size) dependence. This has implications for predictability of streamflow and floods. The authors demonstrate that dynamics transition through potentially chaotic to randomly correlated process as the averaging time-scale increases. Finally, the temporal trends of λ and GC are statistically significant at about 40% of the total number of stations analyzed. Attributing this trend to factors such as changing climate or land use requires further research.</p>

Characterization of SSMVEP-based EEG signals using multiplex limited penetrable horizontal visibility graph

2019 ◽  
Vol 29 (7) ◽  
pp. 073119 ◽  
Author(s):  
Zhong-Ke Gao ◽  
Wei Guo ◽  
Qing Cai ◽  
Chao Ma ◽  
Yuan-Bo Zhang ◽  
...  
Keyword(s):  
Visibility Graph ◽  
Eeg Signals ◽  
Horizontal Visibility ◽  
Horizontal Visibility Graph

scholarly journals Insights on Streamflow Predictability Across Scales Using Horizontal Visibility Graph Based Networks

2020 ◽  
Vol 2 ◽  
Author(s):  
Ganesh R. Ghimire ◽  
Navid Jadidoleslam ◽  
Witold F. Krajewski ◽  
Anastasios A. Tsonis
Keyword(s):  
Visibility Graph ◽  
Horizontal Visibility ◽  
Horizontal Visibility Graph
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