scholarly journals Novel Unconventional-Active-Jamming Recognition Method for Wideband Radars Based on Visibility Graphs

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2344
Author(s):  
Congju Du ◽  
Bin Tang
Keyword(s):  
Time Series ◽  
Random Forests ◽  
Clustering Coefficient ◽  
Recognition Method ◽  
Visibility Graphs ◽  
Recognition Experiment ◽  
Recognition Probability ◽  
Visibility Algorithm ◽  
Deceptive Jamming ◽  
Wideband Radar

Radar unconventional active jamming, including unconventional deceptive jamming and barrage jamming, poses a serious threat to wideband radars. This paper proposes an unconventional-active-jamming recognition method for wideband radar. In this method, the visibility algorithm of converting the radar time series into graphs, called visibility graphs, is first given. Then, the visibility graph of the linear-frequency-modulation (LFM) signal is proved to be a regular graph, and the rationality of extracting features on visibility graphs is theoretically explained. Therefore, four features on visibility graphs, average degree, average clustering coefficient, Newman assortativity coefficient, and normalized network-structure entropy, are extracted from visibility graphs. Finally, a random-forests (RF) classifier is chosen for unconventional-active-jamming recognition. Experiment results show that recognition probability was over 90% when the jamming-to-noise ratio (JNR) was above 0 dB.

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.

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.

Multifractal characterisation of particulate matter (PM10) time series in the Caribbean basin using visibility graphs

2020 ◽  
Author(s):  
Thomas Plocoste ◽  
Rafael Carmona-Cabezas ◽  
Francisco José Jiménez-Hornero ◽  
Eduardo Gutiérrez de Ravé ◽  
Rudy Calif
Keyword(s):  
Time Series ◽  
Particulate Matter ◽  
Caribbean Basin ◽  
Visibility Graphs ◽  
The Caribbean

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>

scholarly journals Hospital-Based Back Surgery: Geospatial-Temporal, Explanatory, and Predictive Models

10.2196/14609 ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. e14609 ◽  
Author(s):  
Lawrence Fulton ◽  
Clemens Scott Kruse
Keyword(s):  
Time Series ◽  
Random Forests ◽  
Geographical Information Systems ◽  
The United States ◽  
Practice Variation ◽  
Geographical Information ◽  
Federal State ◽  
Test Set ◽  
Back Surgery ◽  
Over Time

Background Hospital-based back surgery in the United States increased by 60% from January 2012 to December 2017, yet the supply of neurosurgeons remained relatively constant. During this time, adult obesity grew by 5%. Objective This study aimed to evaluate the demand and associated costs for hospital-based back surgery by geolocation over time to evaluate provider practice variation. The study then leveraged hierarchical time series to generate tight demand forecasts on an unobserved test set. Finally, explanatory financial, technical, workload, geographical, and temporal factors as well as state-level obesity rates were investigated as predictors for the demand for hospital-based back surgery. Methods Hospital data from January 2012 to December 2017 were used to generate geospatial-temporal maps and a video of the Current Procedural Terminology codes beginning with the digit 63 claims. Hierarchical time series modeling provided forecasts for each state, the census regions, and the nation for an unobserved test set and then again for the out-years of 2018 and 2019. Stepwise regression, lasso regression, ridge regression, elastic net, and gradient-boosted random forests were built on a training set and evaluated on a test set to evaluate variables important to explaining the demand for hospital-based back surgery. Results Widespread, unexplained practice variation over time was seen using geographical information systems (GIS) multimedia mapping. Hierarchical time series provided accurate forecasts on a blind dataset and suggested a 6.52% (from 497,325 procedures in 2017 to 529,777 in 2018) growth of hospital-based back surgery in 2018 (529,777 and up to 13.00% by 2019 [from 497,325 procedures in 2017 to 563,023 procedures in 2019]). The increase in payments by 2019 are estimated to be US $323.9 million. Extreme gradient-boosted random forests beat constrained and unconstrained regression models on a 20% unobserved test set and suggested that obesity is one of the most important factors in explaining the increase in demand for hospital-based back surgery. Conclusions Practice variation and obesity are factors to consider when estimating demand for hospital-based back surgery. Federal, state, and local planners should evaluate demand-side and supply-side interventions for this emerging problem.

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