The relationship between the cross‐correlation coefficient of two‐channel acoustic signals and sound image quality

1983 ◽  
Vol 74 (6) ◽  
pp. 1726-1733 ◽  
Author(s):  
Kohichi Kurozumi ◽  
Kengo Ohgushi
Keyword(s):  
Image Quality ◽  
Correlation Coefficient ◽  
Cross Correlation ◽  
Acoustic Signals ◽  
Sound Image ◽  
The Cross ◽  
The Relationship

Does the weather affect the Chinese stock markets? Evidence from the analysis of DCCA cross-correlation coefficient

2014 ◽  
Vol 29 (01) ◽  
pp. 1450236 ◽  
Author(s):  
Guangxi Cao ◽  
Yan Han
Keyword(s):  
Correlation Coefficient ◽  
Stock Markets ◽  
Cross Correlation ◽  
Sunshine Duration ◽  
Statistical Test ◽  
Nonlinear Method ◽  
Weather Variables ◽  
Chinese Stock Markets ◽  
The Cross ◽  
The Relationship

Recent studies confirm that weather affects the Chinese stock markets, based on a linear model. This paper revisits this topic using DCCA cross-correlation coefficient (ρ DCCA (n)), which is a nonlinear method, to determine if weather variables (i.e., temperature, humidity, wind and sunshine duration) affect the returns/volatilities of the Shanghai and Shenzhen stock markets. We propose an asymmetric ρ DCCA (n) by improving the traditional ρ DCCA (n) to determine if different cross-correlated properties exist when one time series trending is either positive or negative. Further, we improve a statistical test for the asymmetric ρ DCCA (n). We find that cross-correlation exists between weather variables and the stock markets on certain time scales and that the cross-correlation is asymmetric. We also analyze the cross-correlation at different intervals; that is, the relationship between weather variables and the stock markets at different intervals is not always the same as the relationship on the whole.

scholarly journals Modeling Complex System Correlation Using Detrended Cross-Correlation Coefficient

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Keqiang Dong ◽  
Hong Zhang ◽  
You Gao
Keyword(s):  
Complex System ◽  
Time Series ◽  
Correlation Coefficient ◽  
Cross Correlation ◽  
Heavy Tails ◽  
Correlation Property ◽  
Traffic Time Series ◽  
Cross Correlations ◽  
The Cross ◽  
Active Research

The understanding of complex systems has become an area of active research for physicists because such systems exhibit interesting dynamical properties such as scale invariance, volatility correlation, heavy tails, and fractality. We here focus on traffic dynamic as an example of a complex system. By applying the detrended cross-correlation coefficient method to traffic time series, we find that the traffic fluctuation time series may exhibit cross-correlation characteristic. Further, we show that two traffic speed time series derived from adjacent sections exhibit much stronger cross-correlations than the two speed series derived from adjacent lanes. Similarly, we also demonstrate that the cross-correlation property between the traffic volume variables from two adjacent sections is stronger than the cross-correlation property between the volume variables of adjacent lanes.

Time-Series and Dynamic Cross-Correlations Analysis on Unexpected Information: Evidence from Media News and Online Postings

2019 ◽  
Vol 19 (02) ◽  
pp. 2050011
Author(s):  
Yan Li ◽  
Xiangyu Kong ◽  
Xiao Li ◽  
Zuochao Zhang
Keyword(s):  
Mass Media ◽  
New Media ◽  
Trading Volume ◽  
Cross Correlation ◽  
Short Term ◽  
Cross Correlations ◽  
The Cross ◽  
The One ◽  
The Relationship

In this paper, we investigate the relationship between unexpected information from postings and news, and the unexpected information is measured by the residual of regressions of trading volume on numbers of news or postings. We mainly find that (i) There are significant positive contemporaneous correlations between the unexpected information coming from postings and different kinds of news; the correlation between the unexpected information coming from postings and new media news is stronger than that between the unexpected information coming from postings and mass media news; (ii) The unexpected information coming from postings could cause the unexpected information coming from news, but only the unexpected information coming from the mass media news could cause that coming from postings; (iii) There are persistent power-law cross-correlations between the unexpected information coming from postings and that coming from mass media news and new media news. The cross-correlation between the unexpected information coming from postings and new media news is more persistent than the one between the unexpected information coming from postings and mass media news. The cross-correlations are all more stable in long term than in short term. We attribute our findings above to the dissemination speed of the information on the Internet.

scholarly journals Improving the shape of the cross-correlation function for leak detection in a plastic water distribution pipe using acoustic signals

2017 ◽  
Vol 127 ◽  
pp. 24-33 ◽  
Author(s):  
Yan Gao ◽  
Michael J. Brennan ◽  
Yuyou Liu ◽  
Fabrício C.L. Almeida ◽  
Phillip F. Joseph
Keyword(s):  
Correlation Function ◽  
Water Distribution ◽  
Cross Correlation ◽  
Leak Detection ◽  
Acoustic Signals ◽  
Cross Correlation Function ◽  
The Cross

scholarly journals Применение анализа совместных рекуррентностей к оценке фазовой синхронизации физиологических сигналов

2021 ◽  
Vol 91 (12) ◽  
pp. 2045
Author(s):  
O.E. Дик ◽  
A.Л. Глазов
Keyword(s):  
Atrial Fibrillation ◽  
Correlation Coefficient ◽  
Cognitive Functions ◽  
Cross Correlation ◽  
Phase Synchronization ◽  
Phase Trajectories ◽  
Brain Responses ◽  
The Cross ◽  
Theta Range

Based on the analysis of joint recurrences, differences in phase synchronization between rhythmic photostimulation and brain responses were revealed in individuals with atrial fibrillation of paroxysmal and persistent types. As a measure of phase synchronization between two signals, the cross-correlation coefficient between the probabilities of recurrences of the corresponding phase trajectories is considered. With a lengthening of the lifetime of atrial fibrillation and an increase in the degree of decline in cognitive functions, the value of this coefficient increases for brain responses to theta-range frequencies.

Estimating Membrane Voltage Correlations From Extracellular Spike Trains

2003 ◽  
Vol 89 (4) ◽  
pp. 2271-2278 ◽  
Author(s):  
Jessy D. Dorn ◽  
Dario L. Ringach
Keyword(s):  
Correlation Coefficient ◽  
Cross Correlation ◽  
Spike Trains ◽  
Membrane Voltage ◽  
Upper And Lower Bounds ◽  
Nominal Data ◽  
Order Of Magnitude ◽  
The Mean ◽  
The Cross ◽  
Measure Of Association

The cross-correlation coefficient between neural spike trains is a commonly used tool in the study of neural interactions. Two well-known complications that arise in its interpretation are 1) modulations in the correlation coefficient may result solely from changes in the mean firing rate of the cells and 2) the mean firing rates of the neurons impose upper and lower bounds on the correlation coefficient whose absolute values differ by an order of magnitude or more. Here, we propose a model-based approach to the interpretation of spike train correlations that circumvents these problems. The basic idea of our proposal is to estimate the cross-correlation coefficient between the membrane voltages of two cells from their extracellular spike trains and use the resulting value as the degree of correlation (or association) of neural activity. This is done in the context of a model that assumes the membrane voltages of the cells have a joint normal distribution and spikes are generated by a simple thresholding operation. We show that, under these assumptions, the estimation of the correlation coefficient between the membrane voltages reduces to the calculation of a tetrachoric correlation coefficient (a measure of association in nominal data introduced by Karl Pearson) on a contingency table calculated from the spike data. Simulations of conductance-based leaky integrate-and-fire neurons indicate that, despite its simplicity, the technique yields very good estimates of the intracellular membrane voltage correlation from the extracellular spike trains in biologically realistic models.

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