On the spectral coherence between two periodically correlated processes

2021 ◽  
Author(s):  
Mahnaz Khalafi ◽  
Ahmad Reza Soltani ◽  
Masoud Golalipour ◽  
Farzad Najafiamiri
Keyword(s):  
Spectral Coherence ◽  
Periodically Correlated Processes

Generalized spectral coherence for cyclostationary signals with α-stable distribution

2021 ◽  
Vol 159 ◽  
pp. 107737
Author(s):  
Piotr Kruczek ◽  
Radosław Zimroz ◽  
Jerome Antoni ◽  
Agnieszka Wyłomańska
Keyword(s):  
Stable Distribution ◽  
Spectral Coherence ◽  
Cyclostationary Signals

Infant EEG Spectral Coherence Data during Quiet Sleep: Unrestricted Principal Components Analysis — Relation of Factors to Gestational Age, Medical Risk, and Neurobehavioral Status

2003 ◽  
Vol 34 (2) ◽  
pp. 54-69 ◽  
Author(s):  
Frank H. Duffy ◽  
Heidelise Als ◽  
Gloria B. McAnulty
Keyword(s):  
Risk Factors ◽  
Principal Components Analysis ◽  
Gestational Age ◽  
Principal Components ◽  
Spectral Coherence ◽  
Quiet Sleep ◽  
Medical Risk ◽  
Medical Risk Factors ◽  
Components Analysis ◽  
Coherence Factors

EEG spectral coherence data in quiet sleep of 312 infants were evaluated, at 42 weeks post-menstrual age. All were medically healthy and living at home by time of evaluation. The sample consisted of prematurely born infants with a wide spectrum of underlying risk factors, as well as healthy fullterm infants. Initial 3040 coherence variables were reduced by principal components analysis in an unrestricted manner, which avoided the folding of spectral and spatial information into among-subject variance. One hundred fifty factors explained 90% of the total variance; 40 Varimax rotated factors explained 65% of the variance yielding a 50:1 data reduction. Factor loading patterns ranged from multiple spectral bands for a single electrode pair to multiple electrode pairs for a single spectral band and all intermediate possibilities. Simple left-right and anterior-posterior pairings were not observed within the factor loadings. By multiple regression analysis, the 40 factors significantly predicted gestational age at birth. By canonical correlation, significant relationships were demonstrated between the coherence factors and medical risk factors as well as neurobehavioral factors. Using discriminant analysis, the coherence factors successfully discriminated between infants with high and low medical risk status and between those with the best and worst neurobehavioral status. The two factors accounting for the most variance, and chosen across several analyses, indicated increased left central-temporal coherence from 6–24 Hz, and increased frontal-occipital coherence at 10 Hz, for the infants born closest to term with lowest medical risk factors and best neurobehavioral performance.

scholarly journals Technical note: Multiple wavelet coherence for untangling scale-specific and localized multivariate relationships in geosciences

2016 ◽  
Vol 20 (8) ◽  
pp. 3183-3191 ◽  
Author(s):  
Wei Hu ◽  
Bing Cheng Si
Keyword(s):  
Multiple Scales ◽  
Free Water ◽  
Wavelet Coherence ◽  
Predictor Variables ◽  
Water Evaporation ◽  
Multivariate Methods ◽  
Spectral Coherence ◽  
Data Set ◽  
Multivariate Relationships ◽  
Multiple Wavelet Coherence

Abstract. The scale-specific and localized bivariate relationships in geosciences can be revealed using bivariate wavelet coherence. The objective of this study was to develop a multiple wavelet coherence method for examining scale-specific and localized multivariate relationships. Stationary and non-stationary artificial data sets, generated with the response variable as the summation of five predictor variables (cosine waves) with different scales, were used to test the new method. Comparisons were also conducted using existing multivariate methods, including multiple spectral coherence and multivariate empirical mode decomposition (MEMD). Results show that multiple spectral coherence is unable to identify localized multivariate relationships, and underestimates the scale-specific multivariate relationships for non-stationary processes. The MEMD method was able to separate all variables into components at the same set of scales, revealing scale-specific relationships when combined with multiple correlation coefficients, but has the same weakness as multiple spectral coherence. However, multiple wavelet coherences are able to identify scale-specific and localized multivariate relationships, as they are close to 1 at multiple scales and locations corresponding to those of predictor variables. Therefore, multiple wavelet coherence outperforms other common multivariate methods. Multiple wavelet coherence was applied to a real data set and revealed the optimal combination of factors for explaining temporal variation of free water evaporation at the Changwu site in China at multiple scale-location domains. Matlab codes for multiple wavelet coherence were developed and are provided in the Supplement.

scholarly journals Real-time characterization of spectral coherence of ultrafast laser based on optical time-stretch

2016 ◽  
Author(s):  
Yiqing Xu ◽  
Xiaoming Wei ◽  
Zhibo Ren ◽  
Kenneth K. Y. Wong ◽  
Kevin Tsia
Keyword(s):  
Real Time ◽  
Ultrafast Laser ◽  
Spectral Coherence ◽  
Optical Time
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