Journal articles: 'Cross-correlation analysis'

1

NAKAJIMA, Yoshio, and Saburo HOMMA. "Cross-correlation analysis of neuronal activities." Japanese Journal of Physiology 37, no. 6 (1987): 967–77. http://dx.doi.org/10.2170/jjphysiol.37.967.

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Zu, Chen, and Daoqiang Zhang. "Canonical sparse cross-view correlation analysis." Neurocomputing 191 (May 2016): 263–72. http://dx.doi.org/10.1016/j.neucom.2016.01.053.

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Hohreiter, V., S. T. Wereley, M. G. Olsen, and J. N. Chung. "Cross-correlation analysis for temperature measurement." Measurement Science and Technology 13, no. 7 (June 20, 2002): 1072–78. http://dx.doi.org/10.1088/0957-0233/13/7/314.

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Wang, Jun, and Da-Qing Zhao. "Detrended cross-correlation analysis of electroencephalogram." Chinese Physics B 21, no. 2 (February 2012): 028703. http://dx.doi.org/10.1088/1674-1056/21/2/028703.

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Xi, Caiping, Shuning Zhang, Gang Xiong, Huichang Zhao, and Yonghong Yang. "Two-dimensional multifractal cross-correlation analysis." Chaos, Solitons & Fractals 96 (March 2017): 59–69. http://dx.doi.org/10.1016/j.chaos.2017.01.004.

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Zucker, S. "Cross-correlation and maximum-likelihood analysis: a new approach to combining cross-correlation functions." Monthly Notices of the Royal Astronomical Society 342, no. 4 (July 11, 2003): 1291–98. http://dx.doi.org/10.1046/j.1365-8711.2003.06633.x.

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Alloway, K. D., and S. A. Roy. "Conditional cross-correlation analysis of thalamocortical neurotransmission." Behavioural Brain Research 135, no. 1-2 (September 2002): 191–96. http://dx.doi.org/10.1016/s0166-4328(02)00165-1.

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Beck, S. B. M., N. J. Williamson, N. D. Sims, and R. Stanway. "Pipeline system identification through cross-correlation analysis." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 216, no. 3 (August 1, 2002): 133–42. http://dx.doi.org/10.1243/095440802320225275.

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The pipeline systems used to carry liquids and gases for the ventilation of buildings, water distributions networks, and the oil and chemical industries are usually monitored by a multiplicity of pressure, flow, and valve position sensors. By comparing the input signal to a valve with the pressure reading from the network using cross-correlation analysis, the technique described in this paper enables a single sensor to be used for monitoring. Specifically, the offset and gradient change of the cross-correlation function show the time delay between the input wave and the acquired output signal. These reflections arise from junctions, valves, and terminations, which can be located effectively using the cross-correlation technique. Investigations using a T-shaped pipe network have been conducted with a valve inserted in the pipeline to introduce artificial water hammer-type perturbations into the system. Both computational and experimental data are presented and the results are compared with the actual pipe network geometry. It is shown that it is possible to identify the location of various features of the network from the reflections and thus to perform either system characterisation or condition monitoring.

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Munshi, Dipak, Alan Heavens, Asantha Cooray, and Patrick Valageas. "Secondary non-Gaussianity and cross-correlation analysis." Monthly Notices of the Royal Astronomical Society 414, no. 4 (June 2, 2011): 3173–97. http://dx.doi.org/10.1111/j.1365-2966.2011.18616.x.

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Clarke, Burton R., and Ryan P. Allgaier. "Cross correlation diagnostics tool for vibration analysis." Journal of the Acoustical Society of America 122, no. 5 (2007): 2509. http://dx.doi.org/10.1121/1.2801817.

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El-Gohary, M., and J. McNames. "Establishing Causality With Whitened Cross-Correlation Analysis." IEEE Transactions on Biomedical Engineering 54, no. 12 (December 2007): 2214–22. http://dx.doi.org/10.1109/tbme.2007.906519.

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Reimherr, Frederick W., Michael L. Martin, James M. Eudicone, Barrie K. Marchant, Quynh-Van Tran, Andrei Pikalov, Ronald N. Marcus, Robert M. Berman, and Berit X. Carlson. "A Pooled MADRS/IDS Cross-Correlation Analysis." Journal of Clinical Psychopharmacology 30, no. 3 (June 2010): 300–305. http://dx.doi.org/10.1097/jcp.0b013e3181db320f.

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Reed, Pamela G., and Joyce A. Verran. "Technical Notes Cross-Lagged Panel Correlation Analysis." Western Journal of Nursing Research 10, no. 5 (October 1988): 671–76. http://dx.doi.org/10.1177/019394598801000519.

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Crossnoe, Connie J., Lewis N. Reich, Karen D. Fern, Ruth E. Manny, and Yi-Zhong Wang. "CROSS-CORRELATION ANALYSIS SUPPORTS PSYCHOPHYSICAL ACUITY MEASURES." Optometry and Vision Science 72, SUPPLEMENT (December 1995): 120. http://dx.doi.org/10.1097/00006324-199512001-00192.

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Bazylev, N. B., and N. A. Fomina. "Cross-Correlation Analysis of Digital Speckle Photography." Journal of Engineering Physics and Thermophysics 91, no. 5 (September 2018): 1241–49. http://dx.doi.org/10.1007/s10891-018-1854-4.

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Kostiukov, I. A. "Interval frequency estimation by cross-correlation analysis." Journal of Physics: Conference Series 1878, no. 1 (May 1, 2021): 012011. http://dx.doi.org/10.1088/1742-6596/1878/1/012011.

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Caren, Marta, and Krešimir Pavlić. "Autocorrelation and cross-correlation flow analysis along the confluence of the Kupa and Sava Rivers." Rudarsko-geološko-naftni zbornik 36, no. 5 (2021): 67–77. http://dx.doi.org/10.17794/rgn.2021.5.7.

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In this paper, an autocorrelation and cross-correlation analysis of the flow of the Kupa and Sava rivers was performed. The analysis was performed at hydrological stations close to the confluence of these two rivers near the city of Sisak, based on data of mean daily flows and daily precipitation. The analysed time period is from 2008 to 2017, with the series being divided into two parts of five years each, from 2008 to 2012 and 2013 to 2017. Daily flow data were measured at the hydrological stations Farkašić on the Kupa River and Crnac on the Sava River, and data on precipitation at the main meteorological station and the automatic meteorological station Sisak. The maximum value of the cross-correlation function between the hydrological stations at the Kupa and Sava rivers is very high, but at a time lag of zero days. The value of the cross-correlation function remains high, up to 0.6 and up to a 4 day lag. The cross-correlation function between precipitation and hydrological data has a very low maximum value.

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Dong, Keqiang, and Xiaojie Gao. "Higher-Order Multifractal Detrended Partial Cross-Correlation Analysis for the Correlation Estimator." Complexity 2020 (June 4, 2020): 1–10. http://dx.doi.org/10.1155/2020/7495058.

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In this paper, we develop a new method to measure the nonlinear interactions between nonstationary time series based on the detrended cross-correlation coefficient analysis. We describe how a nonlinear interaction may be obtained by eliminating the influence of other variables on two simultaneous time series. By applying two artificially generated signals, we show that the new method is working reliably for determining the cross-correlation behavior of two signals. We also illustrate the application of this method in finance and aeroengine systems. These analyses suggest that the proposed measure, derived from the detrended cross-correlation coefficient analysis, may be used to remove the influence of other variables on the cross-correlation between two simultaneous time series.

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Zhang, Guifu, Richard J. Doviak, J. Vivekanandan, William O. J. Brown, and Stephen A. Cohn. "Cross-correlation ratio method to estimate cross-beam wind and comparison with a full correlation analysis." Radio Science 38, no. 3 (March 27, 2003): n/a. http://dx.doi.org/10.1029/2002rs002682.

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20

Xia, X. Y., Z. G. Deng, and Y. Z. Liu. "Cross-Correlation Analysis of Galaxies with Different Luminosity." Symposium - International Astronomical Union 130 (1988): 554. http://dx.doi.org/10.1017/s0074180900136836.

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In the former work (Xia, Deng and Zhou, 1986), we have showed by two- point correlation analysis that more luminous galaxies cluster stronger. Now we present the result of cross-correlation analysis for galaxies with different luminosity. This analysis supplies information about the relations between the distributions of galaxies with different luminosity. The analyses are based on the data given by CfA survey and have made the same corrections as in the former work. The samples are divided into three subgroups in absolute magnitude ranges: a) −21–22, b) −20–21 and c)−19–20. We make the cross-correlation analysis for each two subgroups. Fig. 1 gives the obtained cross-correlation function ξc(r) and Fig. 2 shows the log ξc(r)-log r diagram, the straight lines in Fig. 2 are given by linear regression. These results show that the two brightest subgroups have the strongest correlation. Combining with the results of former work, it follows that the probability of two brighter galaxies being close to each other is larger than that of fainter galaxies.

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Aoyagi, M., J. Yokoyama, T. Kiren, Y. Kim, and Y. Koike. "Cross correlation analysis of auditory brain stem response." AUDIOLOGY JAPAN 31, no. 1 (1988): 1–8. http://dx.doi.org/10.4295/audiology.31.1.

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22

Pankratov, S. V., V. A. Labusov, and O. A. Neklyudov. "Qualitative elemental analysis using a cross-correlation function." Аналитика и контроль 17, no. 1 (2013): 33–40. http://dx.doi.org/10.15826/analitika.2013.17.1.005.

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23

Souza, Eniuce Menezes de, and Vinícius Basseto Félix. "Wavelet Cross-correlation in Bivariate Time-Series Analysis." TEMA (São Carlos) 19, no. 3 (December 17, 2018): 391. http://dx.doi.org/10.5540/tema.2018.019.03.391.

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The estimation of the correlation between independent data sets using classical estimators, such as the Pearson coefficient, is well established in the literature. However, such estimators are inadequate for analyzing the correlation among dependent data. There are several types of dependence, the most common being the serial (temporal) and spatial dependence, which are inherent to the data sets from several fields. Using a bivariate time-series analysis, the relation between two series can be assessed. Further, as one time series may be related to an other with a time offset (either to the past or to the future), it is essential to also consider lagged correlations. The cross-correlation function (CCF), which assumes that each series has a normal distribution and is not autocorrelated, is used frequently. However, even when a time series is normally distributed, the autocorrelation is still inherent to one or both time series, compromising the estimates obtained using the CCF and their interpretations. To address this issue, analysis using the wavelet cross-correlation (WCC) has been proposed. WCC is based on the non-decimated wavelet transform (NDWT), which is translation invariant and decomposes dependent data into multiple scales, each representing the behavior of a different frequency band. To demonstrate the applicability of this method, we analyze simulated and real time series from different stochastic processes. The results demonstrated that analyses based on the CCF can be misleading; however, WCC can be used to correctly identify the correlation on each scale. Furthermore, the confidence interval (CI) for the results of the WCC analysis was estimated to determine the statistical significance.

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24

Zhao, Jun Chang, Wan Hu Dou, Hong Da Ji, and Jun Wang. "Detrended Cross-Correlation Analysis of Epilepsy Electroencephalagram Signals." Advanced Materials Research 765-767 (September 2013): 2664–67. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2664.

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The cross-correlation performance between epilepsy electroencephalogram (EEG) signals reflects the status of epilepsy patients which has importance for analyzing long-range correlation of non-stationary signals. For the first time, detrended cross-correlation analysis (DCCA) was applied to analyze different physiological and pathological states of epilepsy EEG signals. It were compared the difference of DCCA values between epilepsy patients EEG signals and normal subjects EEG signals. It was found that the DCCA values of epilepsy patients EEG signals increased compared the normal subjects EEG signals which can be helpful for medical diagnosis and treatment.

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25

Nascimento Filho, A. S., E. J. A. L. Pereira, Paulo Ferreira, T. B. Murari, and M. A. Moret. "Cross-correlation analysis on Brazilian gasoline retail market." Physica A: Statistical Mechanics and its Applications 508 (October 2018): 550–57. http://dx.doi.org/10.1016/j.physa.2018.05.143.

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26

Roume, C., Z. M. H. Almurad, M. Scotti, S. Ezzina, H. Blain, and D. Delignières. "Windowed detrended cross-correlation analysis of synchronization processes." Physica A: Statistical Mechanics and its Applications 503 (August 2018): 1131–50. http://dx.doi.org/10.1016/j.physa.2018.08.074.

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27

Ha, Euiseong, Youhei Kawamura, Koichi Mizutani, Akihiro Kamohara, and Hirokazu Okawa. "Underground Imaging Method Using Magnified Cross-Correlation Analysis." Japanese Journal of Applied Physics 47, no. 5 (May 23, 2008): 3946–51. http://dx.doi.org/10.1143/jjap.47.3946.

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Marquezin, Cassia A., Nicolò G. Ceffa, Franco Cotelli, Maddalena Collini, Laura Sironi, and Giuseppe Chirico. "Image Cross-Correlation Analysis of Time Varying Flows." Analytical Chemistry 88, no. 14 (July 8, 2016): 7115–22. http://dx.doi.org/10.1021/acs.analchem.6b01143.

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29

Marinho, E. B. S., A. M. Y. R. Sousa, and R. F. S. Andrade. "Using Detrended Cross-Correlation Analysis in geophysical data." Physica A: Statistical Mechanics and its Applications 392, no. 9 (May 2013): 2195–201. http://dx.doi.org/10.1016/j.physa.2012.12.038.

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Fan, Qingju, and Dan Li. "Multifractal cross-correlation analysis in electricity spot market." Physica A: Statistical Mechanics and its Applications 429 (July 2015): 17–27. http://dx.doi.org/10.1016/j.physa.2015.02.065.

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31

Steinmeier, Ralf, Robby P. Hofmann, Christian Bauhuf, Ulrich Hübner, and Rudolf Fahlbusch. "Continuous Cerebral Autoregulation Monitoring by Cross-Correlation Analysis." Journal of Neurotrauma 19, no. 10 (October 2002): 1127–38. http://dx.doi.org/10.1089/08977150260337949.

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32

Zhang, Bailin, Shannon Yan, and Kopin Liu. "Unraveling Multicomponent Images by Extended Cross Correlation Analysis†." Journal of Physical Chemistry A 111, no. 38 (September 2007): 9263–68. http://dx.doi.org/10.1021/jp072916z.

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33

WANG, JING, PENGJIAN SHANG, and WEIJIE GE. "MULTIFRACTAL CROSS-CORRELATION ANALYSIS BASED ON STATISTICAL MOMENTS." Fractals 20, no. 03n04 (September 2012): 271–79. http://dx.doi.org/10.1142/s0218348x12500259.

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We introduce a new method, multifractal cross-correlation analysis based on statistical moments (MFSMXA), to investigate the long-term cross-correlations and cross-multifractality between time series generated from complex system. Efficiency of this method is shown on multifractal series, comparing with the well-known multifractal detrended cross-correlation analysis (MFXDFA) and multifractal detrending moving average cross-correlation analysis (MFXDMA). We further apply this method on volatility time series of DJIA and NASDAQ indices, and find some interesting results. The MFSMXA has comparative performance with MFXDMA and sometimes perform slightly better than MFXDFA. Multifractal nature exists in volatility series. In addition, we find that the cross-multifractality of volatility series is mainly due to their cross-correlations, via comparing the MFSMXA results for original series with those for shuffled series.

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YIN, YI, and PENGJIAN SHANG. "MULTISCALE DETRENDED CROSS-CORRELATION ANALYSIS OF STOCK MARKETS." Fractals 22, no. 04 (November 12, 2014): 1450007. http://dx.doi.org/10.1142/s0218348x14500078.

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In this paper, we employ the detrended cross-correlation analysis (DCCA) to investigate the cross-correlations between different stock markets. We report the results of cross-correlated behaviors in US, Chinese and European stock markets in period 1997–2012 by using DCCA method. The DCCA shows the cross-correlated behaviors of intra-regional and inter-regional stock markets in the short and long term which display the similarities and differences of cross-correlated behaviors simply and roughly and the persistence of cross-correlated behaviors of fluctuations. Then, because of the limitation and inapplicability of DCCA method, we propose multiscale detrended cross-correlation analysis (MSDCCA) method to avoid "a priori" selecting the ranges of scales over which two coefficients of the classical DCCA method are identified, and employ MSDCCA to reanalyze these cross-correlations to exhibit some important details such as the existence and position of minimum, maximum and bimodal distribution which are lost if the scale structure is described by two coefficients only and essential differences and similarities in the scale structures of cross-correlation of intra-regional and inter-regional markets. More statistical characteristics of cross-correlation obtained by MSDCCA method help us to understand how two different stock markets influence each other and to analyze the influence from thus two inter-regional markets on the cross-correlation in detail, thus we get a richer and more detailed knowledge of the complex evolutions of dynamics of the cross-correlations between stock markets. The application of MSDCCA is important to promote our understanding of the internal mechanisms and structures of financial markets and helps to forecast the stock indices based on our current results demonstrated the cross-correlations between stock indices. We also discuss the MSDCCA methods of secant rolling window with different sizes and, lastly, provide some relevant implications and issue.

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MAO, XUEGENG, and PENGJIAN SHANG. "DETRENDED CROSS-CORRELATION ANALYSIS BETWEEN MULTIVARIATE TIME SERIES." Fractals 26, no. 04 (August 2018): 1850058. http://dx.doi.org/10.1142/s0218348x18500585.

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It is a crucial topic to identify the cross-correlations between time series in multivariate systems. In this paper, we extend the detrended cross-correlation analysis (DCCA) into the multivariate systems, assigned multivariate detrended cross-correlation analysis (MVDCCA). Numerical simulations of synthetic multivariate time series generated by two-exponent and mix-correlated ARFIMA processes are applied to illustrate the validity of the proposed MVDCCA. Results show that the external coupling parameter determines the strength of cross-correlation no matter that it is inter-independent or correlated among channels in a certain multivariate time series. The MVDCCA method is robust enough to detect the scale properties of time series by estimating the Hurst exponent. And we use cross-correlation coefficient to quantify the level of cross-correlations clearly. Furthermore, the MVDCCA method performs well when applied to the stock markets combining the stock daily price returns and trading volume of stock indices. By comparing results only using stock daily price returns in published literatures, we find that the higher recognizability between the pair stock indices can be observed whatever from the same regions or different regions in multivariate situations and the conclusions are more comprehensive.

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Chen, Yanguang. "A New Methodology of Spatial Cross-Correlation Analysis." PLOS ONE 10, no. 5 (May 19, 2015): e0126158. http://dx.doi.org/10.1371/journal.pone.0126158.

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37

Wang, Fang, Gui-ping Liao, Xiao-yang Zhou, and Wen Shi. "Multifractal detrended cross-correlation analysis for power markets." Nonlinear Dynamics 72, no. 1-2 (January 3, 2013): 353–63. http://dx.doi.org/10.1007/s11071-012-0718-2.

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38

Ang, B. W. "A cross-sectional analysis of energy—output correlation." Energy Economics 9, no. 4 (October 1987): 274–86. http://dx.doi.org/10.1016/0140-9883(87)90035-1.

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39

Alonso, A., J. M. Gaztelu, W. Bun˜o, and E. García-Austt. "Cross-correlation analysis of septohippocampal neurons during ≡-rhythm." Brain Research 413, no. 1 (June 1987): 135–46. http://dx.doi.org/10.1016/0006-8993(87)90162-4.

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40

Keane, Richard D., and Ronald J. Adrian. "Theory of cross-correlation analysis of PIV images." Applied Scientific Research 49, no. 3 (July 1992): 191–215. http://dx.doi.org/10.1007/bf00384623.

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41

Zhu, Changming, Rigui Zhou, and Chen Zu. "Weight-based canonical sparse cross-view correlation analysis." Pattern Analysis and Applications 22, no. 2 (August 14, 2017): 457–76. http://dx.doi.org/10.1007/s10044-017-0644-5.

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42

Pavlov, A. N., O. N. Pavlova, A. A. Koronovskii, and G. A. Guyo. "Extended detrended cross-correlation analysis of nonstationary processes." Chaos, Solitons & Fractals 157 (April 2022): 111972. http://dx.doi.org/10.1016/j.chaos.2022.111972.

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43

Choi, Un-Sook, and Sung-Jin Cho. "Analysis of Cross-correlation Frequency between Non-linear Binary Sequences Family with 5-Valued Cross-Correlation Functions." Journal of the Korea Institute of Information and Communication Engineering 17, no. 12 (December 31, 2013): 2875–82. http://dx.doi.org/10.6109/jkiice.2013.17.12.2875.

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Delignières, Didier, and Vivien Marmelat. "Strong anticipation and long-range cross-correlation: Application of detrended cross-correlation analysis to human behavioral data." Physica A: Statistical Mechanics and its Applications 394 (January 2014): 47–60. http://dx.doi.org/10.1016/j.physa.2013.09.037.

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Wang, Fang, Zhaohui Yang, and Lin Wang. "Detecting and quantifying cross-correlations by analogous multifractal height cross-correlation analysis." Physica A: Statistical Mechanics and its Applications 444 (February 2016): 954–62. http://dx.doi.org/10.1016/j.physa.2015.10.096.

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SHU, JIAN-JUN, and YAJING LI. "HYPERCOMPLEX CROSS-CORRELATION OF DNA SEQUENCES." Journal of Biological Systems 18, no. 04 (December 2010): 711–25. http://dx.doi.org/10.1142/s0218339010003470.

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A hypercomplex representation of DNA is proposed to facilitate comparing DNA sequences with fuzzy composition. With the hypercomplex number representation, the conventional sequence analysis method, such as, dot matrix analysis, dynamic programming, and cross-correlation method have been extended and improved to align DNA sequences with fuzzy composition. The hypercomplex dot matrix analysis can provide more control over the degree of alignment desired. A new scoring system has been proposed to accommodate the hypercomplex number representation of DNA and integrated with dynamic programming alignment method. By using hypercomplex cross-correlation, the match and mismatch alignment information between two aligned DNA sequences are separately stored in the resultant real part and imaginary parts respectively. The mismatch alignment information is very useful to refine consensus sequence based motif scanning.

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47

XU, S. Y., H. J. SUN, and J. J. WU. "CROSS-CORRELATION ANALYSIS IN MIXED TRAFFIC FLOW TIME SERIES." International Journal of Modern Physics B 25, no. 13 (May 20, 2011): 1823–32. http://dx.doi.org/10.1142/s0217979211100151.

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In this paper, we investigate the complexity behind the mixed traffic flow time series generated by multi-lane cellular automaton model. Throughout the paper, the cross-correlation coefficient is introduced to characterize the time series. It is found that there exists a critical vehicle density S, when S < S1 and the ratio of slow vehicle R > 0.01, the cross-correlation coefficient r is larger than 0.5, which indicates a significant linear correlation. Otherwise, the cross-correlation coefficient r < 0.5 which corresponds to a weak linear correlation. That is to say, the vehicle density plays an important role in the cross-correlation coefficient. Additionally, we also found that the asymmetric lane-change probability has no great influence on the cross-correlation coefficient.

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48

李, 昊哲. "Empirical Analysis Based on Correlation Analysis and Cross-Correlation Table Analysis of College Students’ Monthly Consumption Related Factors." Statistics and Application 10, no. 04 (2021): 714–20. http://dx.doi.org/10.12677/sa.2021.104073.

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49

Cao, Guangxi, Minjia Zhang, and Qingchen Li. "Volatility-constrained multifractal detrended cross-correlation analysis: Cross-correlation among Mainland China, US, and Hong Kong stock markets." Physica A: Statistical Mechanics and its Applications 472 (April 2017): 67–76. http://dx.doi.org/10.1016/j.physa.2017.01.019.

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50

Gao, Quanxue, Huanhuan Lian, Qianqian Wang, and Gan Sun. "Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3938–45. http://dx.doi.org/10.1609/aaai.v34i04.5808.

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For cross-modal subspace clustering, the key point is how to exploit the correlation information between cross-modal data. However, most hierarchical and structural correlation information among cross-modal data cannot be well exploited due to its high-dimensional non-linear property. To tackle this problem, in this paper, we propose an unsupervised framework named Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis (CMSC-DCCA), which incorporates the correlation constraint with a self-expressive layer to make full use of information among the inter-modal data and the intra-modal data. More specifically, the proposed model consists of three components: 1) deep canonical correlation analysis (Deep CCA) model; 2) self-expressive layer; 3) Deep CCA decoders. The Deep CCA model consists of convolutional encoders and correlation constraint. Convolutional encoders are used to obtain the latent representations of cross-modal data, while adding the correlation constraint for the latent representations can make full use of the information of the inter-modal data. Furthermore, self-expressive layer works on latent representations and constrain it perform self-expression properties, which makes the shared coefficient matrix could capture the hierarchical intra-modal correlations of each modality. Then Deep CCA decoders reconstruct data to ensure that the encoded features can preserve the structure of the original data. Experimental results on several real-world datasets demonstrate the proposed method outperforms the state-of-the-art methods.

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Journal articles on the topic 'Cross-correlation analysis'

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