scholarly journals Detection Threshold Estimates for InSAR Time Series: A Simulation of Tropospheric Delay Approach

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1124 ◽  
Author(s):  
Emre Havazli ◽  
Shimon Wdowinski
Keyword(s):  
Time Series ◽  
Detection Threshold ◽  
Simulated Data ◽  
Inverse Correlation ◽  
Tropospheric Delay ◽  
Data Sets ◽  
Sampled Data ◽  
Magma Body ◽  
Study Results ◽  
Temporal Coverage

We present a method for estimating the detection threshold of InSAR time-series products that relies on simulations of both vertical stratification and turbulence mixing components of tropospheric delay. Our simulations take into account case-specific parameters, such as topography and wet delay. We generate the time series of simulated data with given intervals (e.g., 12 and 35 days) for temporal coverages varying between 3 and 10 years. Each simulated acquisition presents the apparent noise due to tropospheric delay, which is constrained by case-specific parameters. As the calculation parameters are randomized, we carry out a large number of simulations and analyze the results statistically and we see that, as temporal coverage increases, the amount of propagated error decreases, presenting an inverse correlation. We validate our method by comparing our results with ERS and Envisat results over Socorro Magma Body, New Mexico. Our case study results indicate that Sentinel-1 can achieve ≈1 mm/yr detection level with regularly sampled data sets that have temporal coverage longer than 5 years.

scholarly journals Comparison of GPS tropospheric delays derived from two consecutive EPN reprocessing campaigns from the point of view of climate monitoring

2016 ◽  
Author(s):  
Zofia Baldysz ◽  
Grzegorz Nykiel ◽  
Andrzej Araszkiewicz ◽  
Mariusz Figurski ◽  
Karolina Szafranek
Keyword(s):  
Time Series ◽  
Point Of View ◽  
Least Square ◽  
Local Analysis ◽  
Trend Test ◽  
Tropospheric Delay ◽  
Data Sets ◽  
University Of Technology ◽  
Seasonal Signals ◽  
Linear Trends

Abstract. The main purpose of this research was to acquire information about consistency of the ZTD (Zenith Tropospheric Delay) linear trends and seasonal components between two consecutive GPS reprocessing campaigns. The analysis concerned two sets of the ZTD time series which were estimated during EPN (EUREF Permanent Network) reprocessing campaigns according to 2008 and 2015 MUT LAC (Military University of Technology Local Analysis Centre) scenarios. Firstly, LombScargle periodograms were generated for 57 EPN stations to obtain characters of oscillations occurring in the ZTD time series. Then, the values of seasonal components and linear trends were estimated using the LSE (Least Square Estimation) approach. The Mann-Kendall Trend Test was also carried out to verify the presence of linear long term ZTD changes. Finally, differences in seasonal signals and linear trends between these two data sets were investigated. In case of spectral analysis, amplitudes of the annual and semiannual periods were almost exactly the same for both reprocessing campaigns. Exceptions were found for only a few stations and they did not exceed 1 mm. The estimated trends were also similar. However, in case of reprocessing performed in 2008, the trends values were generally higher than the values from the other one. All these analyses were conducted for two lengths of the ZTD time series: a shortened 16-year series, and a full 18-year one. In general, shortening of the analysed period of time resulted in decrease of the linear trends values of about 0.7 mm/decade. This was confirmed by analyses based on two data sets.

Detecting Synchrony of Recruitment using Short, Autocorrelated Time Series

1989 ◽  
Vol 46 (10) ◽  
pp. 1831-1838 ◽  
Author(s):  
Keith R. Thompson ◽  
Fred H. Page
Keyword(s):  
Time Series ◽  
Egg Production ◽  
Gadus Morhua ◽  
Statistical Tests ◽  
Simulated Data ◽  
Melanogrammus Aeglefinus ◽  
Data Sets ◽  
Similar Data ◽  
Environmental Forcing ◽  
Fish Stocks

Synchrony of recruitment to distinct fish stocks is difficult to detect because the available time series are generally short and autocorrelated. The recent introduction of more sophisticated statistical techniques has not been particularly helpful; several contradictory interpretations of similar data sets are discussed in the paper. To help resolve the continuing controversy surrounding the question of synchrony, we review three statistical tests of independence and determine their power using simulated data. The tests are then applied to recruitment data for six cod (Gadus morhua) and three haddock (Melanogrammus aeglefinus) stocks of the northwest Atlantic. Prior to analysis each series was first-differenced to reduce autocorrelation and hence increase statistical reliability in the results. The cod stocks are shown to fluctuate independently of the haddock stocks. There is, however, evidence of synchrony for stocks of the same species; the more widely separated cod stocks have a lower mean correlation [Formula: see text] than the haddock [Formula: see text] but both correlations are significant at the 1% level. The within-species synchrony is not due to fluctuations in our index of egg production and it appears that environmental forcing is probably important.

Lymphocyte-to-monocyte ratio in predicting the calcific aortic valve stenosis in a Chinese case–control study

2020 ◽  
Vol 14 (14) ◽  
pp. 1329-1339
Author(s):  
Xiaochun Ma ◽  
Huibo Ma ◽  
Yan Yun ◽  
Shanghao Chen ◽  
Xiaofeng Zhang ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Aortic Valve Stenosis ◽  
Case Control Study ◽  
Independent Predictor ◽  
Inverse Correlation ◽  
Case Control ◽  
Lymphocyte To Monocyte Ratio ◽  
Study Results ◽  
Chinese Case ◽  
Control Study

Aim: This study examined the role of lymphocyte-to-monocyte ratio (LMR), an inflammatory biomarker, in predicting the severity of calcific aortic valve stenosis (CAVS) in a Chinese case–control study. Results: The LMR significantly decreased in the patients with CAVS compared with healthy controls. An inverse correlation was observed between the severity of stenosis and LMR in the patients. Additionally, the LMR was identified in the multivariate analysis as an independent predictor of severe CAVS. Conclusion: This study provides evidence of an inverse correlation between the severity of CAVS and LMR. LMR could potentially be applied as an independent predictor of severe CAVS and could be incorporated into a novel predictive model.

scholarly journals Interpretation of the Chemical and Physical Time-Series Retrieved from Sentik Glacier, Ladakh Himalaya, India

1984 ◽  
Vol 30 (104) ◽  
pp. 66-76 ◽  
Author(s):  
Paul A. Mayewski ◽  
W. Berry Lyons ◽  
N. Ahmad ◽  
Gordon Smith ◽  
M. Pourchet
Keyword(s):  
Time Series ◽  
Chemical Species ◽  
Data Sets ◽  
Reactive Iron ◽  
Physical Time ◽  
Ladakh Himalaya ◽  
Data Density ◽  
Mass Circulation ◽  
The Himalaya ◽  
Analysis Of Time Series

AbstractSpectral analysis of time series of a c. 17 ± 0.3 year core, calibrated for total ß activity recovered from Sentik Glacier (4908m) Ladakh, Himalaya, yields several recognizable periodicities including subannual, annual, and multi-annual. The time-series, include both chemical data (chloride, sodium, reactive iron, reactive silicate, reactive phosphate, ammonium, δD, δ(18O) and pH) and physical data (density, debris and ice-band locations, and microparticles in size grades 0.50 to 12.70 μm). Source areas for chemical species investigated and general air-mass circulation defined from chemical and physical time-series are discussed to demonstrate the potential of such studies in the development of paleometeorological data sets from remote high-alpine glacierized sites such as the Himalaya.

Mean reversion in corporate leverage: evidence from India

2019 ◽  
Vol 45 (9) ◽  
pp. 1183-1198
Author(s):  
Gaurav S. Chauhan ◽  
Pradip Banerjee
Keyword(s):  
Capital Structure ◽  
Emerging Market ◽  
Simulated Data ◽  
Mean Reversion ◽  
Developed Countries ◽  
Data Sets ◽  
Debt Ratio ◽  
Testing Strategy ◽  
Content Type ◽  
Financing Behavior

Purpose Recent papers on target capital structure show that debt ratio seems to vary widely in space and time, implying that the functional specifications of target debt ratios are of little empirical use. Further, target behavior cannot be adjudged correctly using debt ratios, as they could revert due to mechanical reasons. The purpose of this paper is to develop an alternative testing strategy to test the target capital structure. Design/methodology/approach The authors make use of a major “shock” to the debt ratios as an event and think of a subsequent reversion as a movement toward a mean or target debt ratio. By doing this, the authors no longer need to identify target debt ratios as a function of firm-specific variables or any other rigid functional form. Findings Similar to the broad empirical evidence in developed economies, there is no perceptible and systematic mean reversion by Indian firms. However, unlike developed countries, proportionate usage of debt to finance firms’ marginal financing deficits is extensive; equity is used rather sparingly. Research limitations/implications The trade-off theory could be convincingly refuted at least for the emerging market of India. The paper here stimulated further research on finding reasons for specific financing behavior of emerging market firms. Practical implications The results show that the firms’ financing choices are not only depending on their own firm’s specific variables but also on the financial markets in which they operate. Originality/value This study attempts to assess mean reversion in debt ratios in a unique but reassuring manner. The results are confirmed by extensive calibration of the testing strategy using simulated data sets.

scholarly journals An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

2021 ◽  
Author(s):  
Cong Gao ◽  
Ping Yang ◽  
Yanping Chen ◽  
Zhongmin Wang ◽  
Yue Wang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Estimation Method ◽  
Feature Representation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Sets ◽  
Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

scholarly journals A Hypothesis Test for the Goodness-of-Fit of the Marginal Distribution of a Time Series with Application to Stablecoin Data

2021 ◽  
Vol 5 (1) ◽  
pp. 10
Author(s):  
Mark Levene
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Marginal Distribution ◽  
Hypothesis Test ◽  
Data Sets ◽  
Test Statistic ◽  
Sample Test ◽  
Kolmogorov Smirnov ◽  
Heavy Tailed ◽  
Jensen Shannon Divergence

A bootstrap-based hypothesis test of the goodness-of-fit for the marginal distribution of a time series is presented. Two metrics, the empirical survival Jensen–Shannon divergence (ESJS) and the Kolmogorov–Smirnov two-sample test statistic (KS2), are compared on four data sets—three stablecoin time series and a Bitcoin time series. We demonstrate that, after applying first-order differencing, all the data sets fit heavy-tailed α-stable distributions with 1<α<2 at the 95% confidence level. Moreover, ESJS is more powerful than KS2 on these data sets, since the widths of the derived confidence intervals for KS2 are, proportionately, much larger than those of ESJS.

scholarly journals Assessment of Online Deliberative Quality: New Indicators Using Network Analysis and Time-Series Analysis

2021 ◽  
Vol 13 (3) ◽  
pp. 1187
Author(s):  
Bokyong Shin ◽  
Mikko Rask
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Collective Intelligence ◽  
Series Data ◽  
Online Data ◽  
Series Analysis ◽  
Study Results ◽  
Online Deliberation

Online deliberation research has recently developed automated indicators to assess the deliberative quality of much user-generated online data. While most previous studies have developed indicators based on content analysis and network analysis, time-series data and associated methods have been studied less thoroughly. This article contributes to the literature by proposing indicators based on a combination of network analysis and time-series analysis, arguing that it will help monitor how online deliberation evolves. Based on Habermasian deliberative criteria, we develop six throughput indicators and demonstrate their applications in the OmaStadi participatory budgeting project in Helsinki, Finland. The study results show that these indicators consist of intuitive figures and visualizations that will facilitate collective intelligence on ongoing processes and ways to solve problems promptly.

scholarly journals Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 949
Author(s):  
Jiangyi Wang ◽  
Min Liu ◽  
Xinwu Zeng ◽  
Xiaoqiang Hua
Keyword(s):  
Neural Network ◽  
Signal Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Detection Method ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Data Sets ◽  
Feature Maps ◽  
Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

scholarly journals Three-dimensional deformation time series of glacier motion from multiple-aperture DInSAR observation

2019 ◽  
Vol 93 (12) ◽  
pp. 2651-2660 ◽  
Author(s):  
Sergey Samsonov
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Ground Deformation ◽  
Three Dimensional ◽  
Data Sets ◽  
Ice Flow ◽  
The North ◽  
Glacier Ice ◽  
Deformation Component ◽  
3D Deformation

AbstractThe previously presented Multidimensional Small Baseline Subset (MSBAS-2D) technique computes two-dimensional (2D), east and vertical, ground deformation time series from two or more ascending and descending Differential Interferometric Synthetic Aperture Radar (DInSAR) data sets by assuming that the contribution of the north deformation component is negligible. DInSAR data sets can be acquired with different temporal and spatial resolutions, viewing geometries and wavelengths. The MSBAS-2D technique has previously been used for mapping deformation due to mining, urban development, carbon sequestration, permafrost aggradation and pingo growth, and volcanic activities. In the case of glacier ice flow, the north deformation component is often too large to be negligible. Historically, the surface-parallel flow (SPF) constraint was used to compute the static three-dimensional (3D) velocity field at various glaciers. A novel MSBAS-3D technique has been developed for computing 3D deformation time series where the SPF constraint is utilized. This technique is used for mapping 3D deformation at the Barnes Ice Cap, Baffin Island, Nunavut, Canada, during January–March 2015, and the MSBAS-2D and MSBAS-3D solutions are compared. The MSBAS-3D technique can be used for studying glacier ice flow at other glaciers and other surface deformation processes with large north deformation component, such as landslides. The software implementation of MSBAS-3D technique can be downloaded from http://insar.ca/.

Sign in / Sign up

Export Citation Format

Share Document