scholarly journals GPU Parallel Visibility Algorithm for a Set of Segments Using Merge Path

2019 ◽  
Vol 342 ◽  
pp. 57-69
Author(s):  
Kevin Zúñiga Gárate
Keyword(s):  
Visibility Algorithm

scholarly journals Complexity traits and synchrony of cryptocurrencies price dynamics

2021 ◽  
Author(s):  
Davide Provenzano ◽  
Rodolfo Baggio
Keyword(s):  
Time Series ◽  
Complex Network ◽  
Complex Structures ◽  
Slope Parameter ◽  
Exponential Distributions ◽  
Horizontal Visibility ◽  
Time Period ◽  
Before And After ◽  
Visibility Algorithm ◽  
Similar Complex

AbstractIn this study, we characterized the dynamics and analyzed the degree of synchronization of the time series of daily closing prices and volumes in US$ of three cryptocurrencies, Bitcoin, Ethereum, and Litecoin, over the period September 1,2015–March 31, 2020. Time series were first mapped into a complex network by the horizontal visibility algorithm in order to revel the structure of their temporal characters and dynamics. Then, the synchrony of the time series was investigated to determine the possibility that the cryptocurrencies under study co-bubble simultaneously. Findings reveal similar complex structures for the three virtual currencies in terms of number and internal composition of communities. To the aim of our analysis, such result proves that price and volume dynamics of the cryptocurrencies were characterized by cyclical patterns of similar wavelength and amplitude over the time period considered. Yet, the value of the slope parameter associated with the exponential distributions fitted to the data suggests a higher stability and predictability for Bitcoin and Litecoin than for Ethereum. The study of synchrony between the time series investigated displayed a different degree of synchronization between the three cryptocurrencies before and after a collapse event. These results could be of interest for investors who might prefer to switch from one cryptocurrency to another to exploit the potential opportunities of profit generated by the dynamics of price and volumes in the market of virtual currencies.

scholarly journals DETECTING SERIES PERIODICITY WITH HORIZONTAL VISIBILITY GRAPHS

2012 ◽  
Vol 22 (07) ◽  
pp. 1250160 ◽  
Author(s):  
ANGEL NUÑEZ ◽  
LUCAS LACASA ◽  
EUSEBIO VALERO ◽  
JOSE PATRICIO GÓMEZ ◽  
BARTOLO LUQUE
Keyword(s):  
Time Series ◽  
Graph Theory ◽  
Noise Reduction ◽  
Time Domain ◽  
Generic Properties ◽  
Horizontal Visibility ◽  
Visibility Graphs ◽  
Visibility Algorithm ◽  
In Series ◽  
Periodic Signals

The horizontal visibility algorithm was recently introduced as a mapping between time series and networks. The challenge lies in characterizing the structure of time series (and the processes that generated those series) using the powerful tools of graph theory. Recent works have shown that the visibility graphs inherit several degrees of correlations from their associated series, and therefore such graph theoretical characterization is in principle possible. However, both the mathematical grounding of this promising theory and its applications are in its infancy. Following this line, here we address the question of detecting hidden periodicity in series polluted with a certain amount of noise. We first put forward some generic properties of horizontal visibility graphs which allow us to define a (graph theoretical) noise reduction filter. Accordingly, we evaluate its performance for the task of calculating the period of noisy periodic signals, and compare our results with standard time domain (autocorrelation) methods. Finally, potentials, limitations and applications are discussed.

Research on Algorithm of Vision-Based Night Visibility Estimation

2013 ◽  
Vol 394 ◽  
pp. 566-570
Author(s):  
Pang Da Dai ◽  
Yu Jun Zhang ◽  
Jing Li Wang ◽  
Chang Hua Lu ◽  
Yi Zhou ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
Light Source ◽  
Level Set ◽  
Source Image ◽  
Light Sources ◽  
Edge Extraction ◽  
Visibility Algorithm ◽  
Visibility Estimation

Accurately achieve the luminance of light sources at night images is significantly important to vision-based night visibility estimation. In this paper, we propose a practical night visibility algorithm. This algorithm contains two main parts, light sources recognition and edge extraction. Firstly, we give the model of vision-based visibility estimation, and propose the framework of algorithm. Secondly, we give the method to extract light sources from multiple potential targets by the prior knowledge of space information. Then, we analysis the features of light source image, and explain the expanded temple to cut sub-image of light source, and induce in PS level set to segment the edge. Experiments show that, the light source average recognition precision is approach to 0.95 at the condition of moderate breeze, and compared with the manual segment, the precision of light source segment is approach to 0.99 at the condition of real visibility larger than 500m.

scholarly journals Color and Laser Data as a Complementary Approach for Heritage Documentation

2020 ◽  
Vol 12 (20) ◽  
pp. 3465
Author(s):  
Yahya Alshawabkeh
Keyword(s):  
Data Integration ◽  
Data Fusion ◽  
Hybrid Methods ◽  
Laser Scanner ◽  
Texture Mapping ◽  
Data Interpretation ◽  
Scanner Data ◽  
Occluded Objects ◽  
Laser Data ◽  
Visibility Algorithm

Heritage recording has received much attention and benefits from recent developments in the field of range and imaging sensors. While these methods have often been viewed as two different methodologies, data integration can achieve different products, which are not always found in a single technique. Data integration in this paper can be divided into two levels: laser scanner data aided by photogrammetry and photogrammetry aided by scanner data. At the first level, superior radiometric information, mobility and accessibility of imagery can be actively used to add texture information and allow for new possibilities in terms of data interpretation and completeness of complex site documentation. In the second level, true orthophoto is generated based on laser data, the results are rectified images with a uniform scale representing all objects at their planimetric position. The proposed approaches enable flexible data fusion and allow images to be taken at an optimum time and position for radiometric information. Data fusion usually involves serious distortions in the form of a double mapping of occluded objects that affect the product quality. In order to enhance the efficiency of visibility analysis in complex structures, a proposed visibility algorithm is implemented into the developed methods of texture mapping and true orthophoto generation. The algorithm filters occluded areas based on a patch processing using a grid square unit set around the projected vertices. The depth of the mapped triangular vertices within the patch neighborhood is calculated to assign the visible one. In this contribution, experimental results from different historical sites in Jordan are presented as a validation of the proposed algorithms. Algorithms show satisfactory performance in terms of completeness and correctness of occlusion detection and spectral information mapping. The results indicate that hybrid methods could be used efficiently in the representation of heritage structures.

A visibility algorithm for converting 3D meshes into editable 2D vector graphics

2009 ◽  
Vol 28 (3) ◽  
pp. 1-8 ◽  
Author(s):  
Elmar Eisemann ◽  
Sylvain Paris ◽  
Frédo Durand
Keyword(s):  
Vector Graphics ◽  
Visibility Algorithm

Analysis of the attention to COVID-19 epidemic based on visibility graph network

2021 ◽  
pp. 2150316
Author(s):  
Qingxiang Feng ◽  
Haipeng Wei ◽  
Jun Hu ◽  
Wenzhe Xu ◽  
Fan Li ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Time Series Data ◽  
Search Behavior ◽  
Series Data ◽  
Global Public Health ◽  
Health Crisis ◽  
Time Space ◽  
Epidemic Period ◽  
Visibility Algorithm

Most of the existing researches on public health events focus on the number and duration of events in a year or month, which are carried out by regression equation. COVID-19 epidemic, which was discovered in Wuhan, Hubei Province, quickly spread to the whole country, and then appeared as a global public health event. During the epidemic period, Chinese netizens inquired about the dynamics of COVID-19 epidemic through Baidu search platform, and learned about relevant epidemic prevention information. These groups’ search behavior data not only reflect people’s attention to COVID-19 epidemic, but also contain the stage characteristics and evolution trend of COVID-19 epidemic. Therefore, the time, space and attribute laws of propagation of COVID-19 epidemic can be discovered by deeply mining more information in the time series data of search behavior. In this study, it is found that transforming time series data into visibility network through the principle of visibility algorithm can dig more hidden information in time series data, which may help us fully understand the attention to COVID-19 epidemic in Chinese provinces and cities, and evaluate the deficiencies of early warning and prevention of major epidemics. What’s more, it will improve the ability to cope with public health crisis and social decision-making level.

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