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Journal articles on the topic 'Autocorrelation matrix'

Author: Grafiati

Published: 3 June 2025

Last updated: 19 July 2025

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1

Conrey, J. B., D. W. Farmer, J. P. Keating, M. O. Rubinstein, and N. C. Snaith. "Autocorrelation of Random Matrix Polynomials." Communications in Mathematical Physics 237, no. 3 (2003): 365–95. http://dx.doi.org/10.1007/s00220-003-0852-2.

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Sun, Shuai, and Haiping Zhang. "Flow-Data-Based Global Spatial Autocorrelation Measurements for Evaluating Spatial Interactions." ISPRS International Journal of Geo-Information 12, no. 10 (2023): 396. http://dx.doi.org/10.3390/ijgi12100396.

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Spatial autocorrelation analysis is essential for understanding the distribution patterns of spatial flow data. Existing methods focus mainly on the origins and destinations of flow units and the relationships between them. These methods measure the autocorrelation of gravity or the positional and directional autocorrelations of flow units that are treated as objects. However, the intrinsic complexity of actual flow data necessitates the consideration of not only gravity, positional, and directional autocorrelations but also the autocorrelations of the variables of interest. This study propose

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3

Chachlakis, Dimitris G., and Panos P. Markopoulos. "Structured autocorrelation matrix estimation for coprime arrays." Signal Processing 183 (June 2021): 107987. http://dx.doi.org/10.1016/j.sigpro.2021.107987.

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4

Andrews, Donald W. K. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation." Econometrica 59, no. 3 (1991): 817. http://dx.doi.org/10.2307/2938229.

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5

JIAO, Chuan-hai, Ke-ren WANG, and Shuo MEN. "Cooperative blind spectrum sensing using autocorrelation matrix." Journal of China Universities of Posts and Telecommunications 18, no. 3 (2011): 47–53. http://dx.doi.org/10.1016/s1005-8885(10)60062-3.

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6

Podstrigaev, Alexey, and Nhan Nguen Chong. "STUDYING THE ACCURACY OF DETERMINING THE LOCATION OF RADIO EMISSION SOURCES WITH COMPLEX SIGNALS WHEN USING A MATRIX RECEIVER WITH AN AUTOCORRELATION ALGORITHM AT THE OUTPUT." Automation and modeling in design and management 2022, no. 2 (2022): 4–12. http://dx.doi.org/10.30987/2658-6436-2022-2-4-12.

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The root-mean-square errors of the position lines are estimated when using direction-finding and difference-range methods based on a matrix receiver with an autocorrelation algorithm at the output. The study compares the accuracy of determining the location of radio emission sources with linear-frequency-modulated and phase-code-shifted signals by a matrix receiver with an autocorrelation algorithm at the output and a matrix receiver with a detector algorithm at the output. A comparative analysis of calculating the ratio between the root-mean-square errors of locating radio emission sources is

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7

West, Kenneth D. "Another heteroskedasticity- and autocorrelation-consistent covariance matrix estimator." Journal of Econometrics 76, no. 1-2 (1997): 171–91. http://dx.doi.org/10.1016/0304-4076(95)01788-7.

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8

Khalili, Malika, Robert Leconte, and François Brissette. "Stochastic Multisite Generation of Daily Precipitation Data Using Spatial Autocorrelation." Journal of Hydrometeorology 8, no. 3 (2007): 396–412. http://dx.doi.org/10.1175/jhm588.1.

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Abstract There are a number of stochastic models that simulate weather data required for various water resources applications in hydrology, agriculture, ecosystem, and climate change studies. However, many of them ignore the dependence between station locations exhibited by the observed meteorological time series. This paper proposes a multisite generation approach of daily precipitation data based on the concept of spatial autocorrelation. This theory refers to spatial dependence between observations with respect to their geographical adjacency. In hydrometeorology, spatial autocorrelation ca

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9

Cao, Hai-Yan, and Zhen-Yu Ye. "Theoretical analysis and algorithm design of optimized pilot for downlink channel estimation in massive MIMO systems based on compressed sensing." Acta Physica Sinica 71, no. 5 (2022): 050101. http://dx.doi.org/10.7498/aps.71.20211504.

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Aiming at the pilot design problem in channel estimation of large-scale multiple input multiple output (MIMO) systems, an adaptive autocorrelation matrix reduction parameter pilot optimization algorithm based on channel reconstruction error rate minimization is proposed under the framework of compression perception theory. Firstly, the system model and orthogonal matching pursuit (OMP) algorithm are introduced. Secondly, for minimizing the channel reconstruction error rate, the relation between the expected value of the correlation decision in each iteration of the OMP algorithm and the recons

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10

Laikova, L. G., T. A. Tereshchenko, and Y. S. Yamnenko. "Matrix calculation of correlation characteristics based on spectral methods." Технология и конструирование в электронной аппаратуре, no. 3-4 (2020): 11–16. http://dx.doi.org/10.15222/tkea2020.3-4.11.

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The paper is devoted to the problem of calculation of autocorrelation function that is important for solving the tasks that require finding the repeating intervals of the signal or defining the main frequency of the signal against the background of non-stationary noise. The authors propose an algorithm to transform the connection between arithmetic and logical correlation functions in oriented basis into the matrix form. Comparative analysis is conducted for the computational complexity of different types of autocorrelation functions using different spectral methods — Fourier, Walsh, and orien

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11

Chen, Yanguang. "An analytical process of spatial autocorrelation functions based on Moran’s index." PLOS ONE 16, no. 4 (2021): e0249589. http://dx.doi.org/10.1371/journal.pone.0249589.

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A number of spatial statistic measurements such as Moran’s I and Geary’s C can be used for spatial autocorrelation analysis. Spatial autocorrelation modeling proceeded from the 1-dimension autocorrelation of time series analysis, with time lag replaced by spatial weights so that the autocorrelation functions degenerated to autocorrelation coefficients. This paper develops 2-dimensional spatial autocorrelation functions based on the Moran index using the relative staircase function as a weight function to yield a spatial weight matrix with a displacement parameter. The displacement bears analog

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12

Eck, Douglas. "Identifying Metrical and Temporal Structure With an Autocorrelation Phase Matrix." Music Perception 24, no. 2 (2006): 167–76. http://dx.doi.org/10.1525/mp.2006.24.2.167.

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This article introduces a new method for detecting long-timescale structure in music. We describe a way to compute autocorrelation such that the distribution of energy in phase space is preserved in a matrix. The resulting Autocorrelation Phase Matrix (APM) is useful for several tasks involving metrical structure. In this article we describe the details of calculating the APM. We then show how phase-related regularities from music are stored in the APM and present two ways to recover these regularities. The simpler approach uses variance or entropy calculated on the distribution of information

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13

Zhou, Lihong, Wenjiang Pei, Pengcheng Xi, and Zhenya He. "Extrapolated impulse response filter using coefficient-autocorrelation-matrix decomposition." Signal Processing 88, no. 7 (2008): 1762–74. http://dx.doi.org/10.1016/j.sigpro.2008.01.014.

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14

Andrews, Donald W. K., and J. Christopher Monahan. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator." Econometrica 60, no. 4 (1992): 953. http://dx.doi.org/10.2307/2951574.

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15

Sanches, I. "From LPC to normalised autocorrelation coefficients through a matrix." Electronics Letters 34, no. 4 (1998): 333. http://dx.doi.org/10.1049/el:19980310.

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16

Kim, Min Seong, and Yixiao Sun. "Spatial heteroskedasticity and autocorrelation consistent estimation of covariance matrix." Journal of Econometrics 160, no. 2 (2011): 349–71. http://dx.doi.org/10.1016/j.jeconom.2010.10.002.

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17

Wen, Zhi Qiang, Wen Qiu Zhu, Yong Xiang Hu, and Zhao Yi Peng. "Feature Modeling and Classification of Halftone Image Based on Statistical Method." Applied Mechanics and Materials 229-231 (November 2012): 1693–96. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.1693.

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For problem of feature modeling on halftone image, three statistics methods, named gray-level co-occurrence matrix, autocorrelation function and spectrum statistics, are used to extract feature vector of various halftone images. Then, their classification performance is assessed by radial basis function neural network. A mass of experiments show the autocorrelation function is better than other two methods for classification on halftone image.

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18

Martellosio, Federico. "POWER PROPERTIES OF INVARIANT TESTS FOR SPATIAL AUTOCORRELATION IN LINEAR REGRESSION." Econometric Theory 26, no. 1 (2009): 152–86. http://dx.doi.org/10.1017/s0266466609090641.

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This paper derives some exact power properties of tests for spatial autocorrelation in the context of a linear regression model. In particular, we characterize the circumstances in which the power vanishes as the autocorrelation increases, thus extending the work of Krämer (2005). More generally, the analysis in the paper sheds new light on how the power of tests for spatial autocorrelation is affected by the matrix of regressors and by the spatial structure. We mainly focus on the problem of residual spatial autocorrelation, in which case it is appropriate to restrict attention to the class o

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19

Manandhar, A., L. Hoegner, and U. Stilla. "PALM TREE DETECTION USING CIRCULAR AUTOCORRELATION OF POLAR SHAPE MATRIX." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 6, 2016): 465–72. http://dx.doi.org/10.5194/isprsannals-iii-3-465-2016.

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Palm trees play an important role as they are widely used in a variety of products including oil and bio-fuel. Increasing demand and growing cultivation have created a necessity in planned farming and the monitoring different aspects like inventory keeping, health, size etc. The large cultivation regions of palm trees motivate the use of remote sensing to produce such data. This study proposes an object detection methodology on the aerial images, using shape feature for detecting and counting palm trees, which can support an inventory. The study uses circular autocorrelation of the polar shape

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20

Manandhar, A., L. Hoegner, and U. Stilla. "PALM TREE DETECTION USING CIRCULAR AUTOCORRELATION OF POLAR SHAPE MATRIX." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 6, 2016): 465–72. http://dx.doi.org/10.5194/isprs-annals-iii-3-465-2016.

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Palm trees play an important role as they are widely used in a variety of products including oil and bio-fuel. Increasing demand and growing cultivation have created a necessity in planned farming and the monitoring different aspects like inventory keeping, health, size etc. The large cultivation regions of palm trees motivate the use of remote sensing to produce such data. This study proposes an object detection methodology on the aerial images, using shape feature for detecting and counting palm trees, which can support an inventory. The study uses circular autocorrelation of the polar shape

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21

Naha, Arunava, Anik Kumar Samanta, Aurobinda Routray, and Alok Kanti Deb. "Determining Autocorrelation Matrix Size and Sampling Frequency for MUSIC Algorithm." IEEE Signal Processing Letters 22, no. 8 (2015): 1016–20. http://dx.doi.org/10.1109/lsp.2014.2366638.

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22

Leenders, Roger Th A. J. "Modeling social influence through network autocorrelation: constructing the weight matrix." Social Networks 24, no. 1 (2002): 21–47. http://dx.doi.org/10.1016/s0378-8733(01)00049-1.

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23

Eckley, Idris A., and Guy P. Nason. "Efficient computation of the discrete autocorrelation wavelet inner product matrix." Statistics and Computing 15, no. 2 (2005): 83–92. http://dx.doi.org/10.1007/s11222-005-6200-y.

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24

Al-Jazzar, Saleh O. "Angle of Arrival Estimation Using Cholesky Decomposition." International Journal of Antennas and Propagation 2012 (2012): 1–6. http://dx.doi.org/10.1155/2012/803617.

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An angle of arrival (AOA) estimator is presented. Many applications require accurate AOA estimates such as wireless positioning and signal enhancement using space-processing techniques. The proposed AOA estimator depends on the Cholesky decomposition of the received signal autocorrelation matrix. The resultant decomposed matrices are used to modify the crosscorrelation matrix of the received signals at the antenna array doublets. The proposed method is named the Cholesky-decomposition-based-AOA (CDBA) estimator. In comparison with the TLS-ESPRIT algorithm which utilizes the eigenvalue decompos

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25

Hu, Yunfeng, and Batunacun. "An Analysis of Land-Use and Land-Cover Change in the Zhujiang–Xijiang Economic Belt, China, from 1990 to 2017." Applied Sciences 8, no. 9 (2018): 1524. http://dx.doi.org/10.3390/app8091524.

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Land-use and land-cover change (LUCC) are currently contested topics in the research of global environment change and sustainable change. Identifying the historic land-use change process is important for the new economic development belt (the Zhujiang–Xijiang Economic Belt, ZXEB). During this research, based on long-time-series land-use and land-cover data, while using a combination of a transition matrix method and Markov chain model, the authors derive the patterns, processes, and spatial autocorrelations of land-use and land-cover changes in the ZXEB for the periods 1990–2000 and 2000–2017.

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26

Mainali, Janardan, Heejun Chang, and Yongwan Chun. "A review of spatial statistical approaches to modeling water quality." Progress in Physical Geography: Earth and Environment 43, no. 6 (2019): 801–26. http://dx.doi.org/10.1177/0309133319852003.

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We review different regression models related to water quality that incorporate spatial aspects in their model. Spatial aspects refer to the location of different sites and are usually characterized by the distance between different points and directions by which they are related to each other. We focus on spatial lag and error, spatial eigenvector-based, geographically weighted regression, and spatial-stream-network-based models. We evaluated different studies using these methods based on how they dealt with clustering (spatial autocorrelation) of response variables, incorporated those cluste

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27

Keener, Robert W., Jan Kmenta, and Neville C. Weber. "Estimation of the Covariance Matrix of the Least-Squares Regression Coefficients When the Disturbance Covariance Matrix Is of Unknown Form." Econometric Theory 7, no. 1 (1991): 22–45. http://dx.doi.org/10.1017/s0266466600004229.

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This paper deals with the problem of estimating the covariance matrix of the least-squares regression coefficients under heteroskedasticity and/or autocorrelation of unknown form. We consider an estimator proposed by White [17] and give a relatively simple proof of its consistency. Our proof is based on more easily verifiable conditions than those of White. An alternative estimator with improved small sample properties is also presented.

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28

DENG, Chao, Huoxing LI, and Zhiheng WANG. "Corner detection algorithm using multi-channel odd Gabor gradient autocorrelation matrix." Journal of Computer Applications 33, no. 12 (2013): 3548–51. http://dx.doi.org/10.3724/sp.j.1087.2013.03548.

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29

Casini, Alessandro. "Comment on Andrews (1991) “Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation”." Econometrica 90, no. 4 (2022): 1–2. http://dx.doi.org/10.3982/ecta20162.

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30

Newey, Whitney K., and Kenneth D. West. "A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix." Econometrica 55, no. 3 (1987): 703. http://dx.doi.org/10.2307/1913610.

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31

Jamali, Tayeb, and G. R. Jafari. "Spectra of empirical autocorrelation matrices: A random-matrix-theory–inspired perspective." EPL (Europhysics Letters) 111, no. 1 (2015): 10001. http://dx.doi.org/10.1209/0295-5075/111/10001.

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32

Kozicka, Richard J., and Saleem A. Kassamb. "A unified approach to coherent source decorrelation by autocorrelation matrix smoothing." Signal Processing 45, no. 1 (1995): 115–30. http://dx.doi.org/10.1016/0165-1684(95)00045-f.

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33

Angulo, Santiago, Isabel García-Pérez, Cristina Legido-Quigley, and Coral Barbas. "The autocorrelation matrix probing biochemical relationships after metabolic fingerprinting with CE." ELECTROPHORESIS 30, no. 7 (2009): 1221–27. http://dx.doi.org/10.1002/elps.200800554.

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34

Yang, Hong, Xiao Ya Dong, Min Wang, and Yu Guo. "GIS-Aided Evolvement Analysis of Spatial-Temporal Pattern of Regional Tourism Industry Environment." Advanced Materials Research 726-731 (August 2013): 4690–93. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.4690.

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Regional tourism is a part of China's economic income. It is of great significance for tourism economy development to study spatial-temporal evolvement. This study analyzed time-based characteristics and spatial cluster characteristics through methods including Spatial Weight Matrix, global spatial autocorrelation (Morans I) statistic, spatial Statistics (Getis-Ord Gi*) and local spatial autocorrelation calculations. Results show that the overall spatial autocorrelation model changed slowly from negative (-0.05) to positive (0.08) while eastern part of study area clustered as hotspot and weste

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35

Samus, V. M. "Using orthogonal transformations to obtain signal sequences with new autocorrelation and spectral characteristics." Sovremennaya nauka i innovatsii, no. 3 (43) (2023): 19–24. http://dx.doi.org/10.37493/2307-910x.2023.3.2.

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The paper developed a method for obtaining sequences of discrete signals, which greatly simplifies the process of obtaining signal sequences with new autocorrelation and spectral characteristics, based on the properties of orthogonal transformations of basis functions, which includes three stages: building a transition matrix; calculation of coordinates of signal points in a new basis; representation of the signal system in the basis of displaced unit pulses using coordinates in the new basis.

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36

Kiefer, Nicholas M., and Timothy J. Vogelsang. "HETEROSKEDASTICITY-AUTOCORRELATION ROBUST TESTING USING BANDWIDTH EQUAL TO SAMPLE SIZE." Econometric Theory 18, no. 6 (2002): 1350–66. http://dx.doi.org/10.1017/s026646660218604x.

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Asymptotic theory for heteroskedasticity autocorrelation consistent (HAC) covariance matrix estimators requires the truncation lag, or bandwidth, to increase more slowly than the sample size. This paper considers an alternative approach covering the case with the asymptotic covariance matrix estimated by kernel methods with truncation lag equal to sample size. Although such estimators are inconsistent, valid tests (asymptotically pivotal) for regression parameters can be constructed. The limiting distributions explicitly capture the truncation lag and choice of kernel. A local asymptotic power

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37

de Jong, Robert M. "A STRONG CONSISTENCY PROOF FOR HETEROSKEDASTICITY AND AUTOCORRELATION CONSISTENT COVARIANCE MATRIX ESTIMATORS." Econometric Theory 16, no. 2 (2000): 262–68. http://dx.doi.org/10.1017/s0266466600162061.

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A strong consistency result for heteroskedasticity and autocorrelation consistent covariance matrix estimators is proven in this paper. In addition, an error in a weak consistency proof for such estimators in the econometrics literature and a correction of that result is provided.

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38

Sharma, Brij, Pradeep Pilania, Prithvi Singh, Susheela Sharma, and Yenamandra Prabhakar. "CP-MLR directed QSAR study of carbonic anhydrase inhibitors: sulfonamide and sulfamate inhibitors." Open Chemistry 7, no. 4 (2009): 909–22. http://dx.doi.org/10.2478/s11532-009-0073-4.

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AbstractThe inhibition activities of sulfonamide and sulfamate derivatives for human carbonic anhydrases have been quantitatively analyzed using DRAGON descriptors. QSAR models have been obtained through combinatorial protocol-multiple linear regression (CP-MLR) computational procedure. For the hCA I inhibition activity, a higher value of information content index of the 1-order neighborhood symmetry (IC1) and a lower value of the Moran autocorrelations, MATS2v and MATS1p, along with a lower number of sulfur atoms in a molecular structure (nRSR) is beneficial to the activity. A higher number o

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39

Artemova, Tatiana K., Aleksey S. Gvozdarev, and Konstantin S. Artemov. "Mutual antenna coupling influence on the channel correlation matrix for linear antenna arrays." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 820–27. http://dx.doi.org/10.11591/eei.v10i2.2709.

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The paper presents the results of the research of electromagnetic mutual coupling impact on the structure of the correlation matrices in multiantenna communication systems. Classical correlation structures employed in most of the up-to-date communication systems descriptions and designs usually assume unit autocorrelation and exponentially decreasing cross-correlation of antenna elements in the receiving/transmitting array. At the same time numerous studies had shown that these assumptions may not hold under certain conditions. The performed research relates the correlation effects with the im

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40

Tatiana, K. Artemova, S. Gvozdarev Aleksey, and S. Artemov Konstantin. "Mutual antenna coupling influence on the channel correlation matrix for linear antenna arrays." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 820~827. https://doi.org/10.11591/eei.v10i2.2709.

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The paper presents the results of the research of electromagnetic mutual coupling impact on the structure of the correlation matrices in multiantenna communication systems. Classical correlation structures employed in most of the up-to-date communication systems descriptions and designs usually assume unit autocorrelation and exponentially decreasing cross-correlation of antenna elements in the receiving/transmitting array. At the same time numerous studies had shown that these assumptions may not hold under certain conditions. The performed research relates the correlation effects with the im

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41

Dutilleul, P., and C. Potvin. "Among-environment heteroscedasticity and genetic autocorrelation: implications for the study of phenotypic plasticity." Genetics 139, no. 4 (1995): 1815–29. http://dx.doi.org/10.1093/genetics/139.4.1815.

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Abstract The impact of among-environment heteroscedasticity and genetic autocorrelation on the analysis of phenotypic plasticity is examined. Among-environment heteroscedasticity occurs when genotypic variances differ among environments. Genetic autocorrelation arises whenever the responses of a genotype to different environments are more or less similar than expected for observations randomly associated. In a multivariate analysis-of-variance model, three transformations of genotypic profiles (reaction norms), which apply to the residuals of the model while preserving the mean responses withi

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42

KESER, Serkan. "An Image Compression Method Based on Subspace and Downsampling." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 12, no. 1 (2023): 215–25. http://dx.doi.org/10.17798/bitlisfen.1225312.

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In this study, a new Karhunen-Loeve transform based algorithm with acceptable computational complexity is developed for lossy image compression. This method is based on obtaining an autocorrelation matrix by clustering the highly correlated image rows obtained by applying downsampling to the image. The KLT is applied to the blocks created from the downsampled image using the eigenvector (or transform) matrix obtained from the autocorrelation matrix; thus, the transform coefficient matrices are obtained. Then these coefficients were compressed by the lossless coding method. One of the proposed

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Gruzdev, A. N. "Аccounting for autocorrelation in a linear regression problem on an example of analysis of atmospheric column NO2 content". Известия Российской академии наук. Физика атмосферы и океана 55, № 1 (2019): 73–82. http://dx.doi.org/10.31857/s0002-351555173-82.

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A method is proposed for taking into account a serial correlation (an autocorrelation) of data in a linear regression problem, which allows accounting for the autocorrelation on long scales. A residual series is presented as an autoregressive process of an order, k, that can be much larger than 1, and the autocorrelation function of the processes is calculated by solving the system of the Yule–Walker equations. Given the autocorrelation function, the autocorrelation matrix is constructed which enters the formulas for estimates of regression coefficients and their errors. The efficiency of the

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44

Wibun, Anuchit, and Pipat Chaiwiwatworakul. "An Estimation of Thailand's Hourly Solar Radiation Using Markov Transition Matrix Method." Applied Mechanics and Materials 839 (June 2016): 29–33. http://dx.doi.org/10.4028/www.scientific.net/amm.839.29.

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To estimate global solar radiation from easy available weather forecast data (sky condition), Markov model is used for this estimation. The five-year (1996-2000) global radiation data that are taken at an hour intervals from Nakhon Pathom station, Thailand (latitude 13.81ºN and longitude 100.04ºE) are used to construct the Markov transition matrices. The global radiation sequences in 2000 will be generated by based on the characteristic probability of moving global radiation values which were observed from the obtained data during 1996-1999. The autocorrelation function is used for checking th

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45

Zhang, Kai, Jinqiu Li, Bingqian Wang, and Haoran Meng. "Autocorrelation Matrix Knowledge Distillation: A Task-Specific Distillation Method for BERT Models." Applied Sciences 14, no. 20 (2024): 9180. http://dx.doi.org/10.3390/app14209180.

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Pre-trained language models perform well in various natural language processing tasks. However, their large number of parameters poses significant challenges for edge devices with limited resources, greatly limiting their application in practical deployment. This paper introduces a simple and efficient method called Autocorrelation Matrix Knowledge Distillation (AMKD), aimed at improving the performance of smaller BERT models for specific tasks and making them more applicable in practical deployment scenarios. The AMKD method effectively captures the relationships between features using the au

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46

Zhao, Ji-jun, Shu-guang Zhang, and Wen-yu Zhao. "An algorithm to solve autocorrelation matrix singular value based on SNR estimation." Optoelectronics Letters 5, no. 1 (2009): 41–44. http://dx.doi.org/10.1007/s11801-009-8126-7.

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47

Huda, Nur'ainul Miftahul, and Nurfitri Imro'ah. "Hybrid ARIMA-Spatial Autocorrelation (Moran Index and LISA) for Covid-19 Vaccination in All Indonesian Provinces." Jambura Journal of Biomathematics (JJBM) 4, no. 2 (2023): 126–37. http://dx.doi.org/10.37905/jjbm.v4i2.20915.

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Numerous issues arise from stochastic processes with temporal and spatial index parameters. From 2020, Covid-19 has occurred worldwide. Combining time series with geographical analysis is crucial. ARIMA and spatial autocorrelation analysis using Moran’s Index and LISA are prominent models for the two analyses. ARIMA predicts future values. The ARIMA model is applied to all recorded locations since it involves a stochastic process with a time and location parameter index. Then the prediction results at each location were examined using spatial autocorrelation, starting with the Moran index to s

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Tang, Xiaogang, Sun’an Wang, and Jiong Li. "Blind Source Separation for Complex-Valued Signals Using Generalized Autocorrelation." Mathematical Problems in Engineering 2018 (July 12, 2018): 1–9. http://dx.doi.org/10.1155/2018/8076468.

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We introduce a new complex-valued blind source separation approach, based on generalized autocorrelations of sources, to improve the spectrum efficiency for the next-generation wireless communications system. The proposed algorithm considers the temporal structures of communication signals and the natural gradient-based method is used to optimize the demixing matrix. In addition, the local stability condition is proved. Simulation results are presented showing the superior performance of the proposed algorithm in the intersymbol interference of the estimated signals.

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Zhang, Xiao Feng, Zhi Jing Zhang, Zhi Peng Ye, Yan Li, and Xin Jin. "Optimization Design of Modular Desktop Micro-Assembly System Based on Genetic Algorithm." Applied Mechanics and Materials 427-429 (September 2013): 1337–41. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1337.

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For solving the problem of non-silicon MEMS assembly, the framework of Modular Desktop Micro Assembly System (MDMAS) is proposed, the fundamental principles and functional modular partition method of which is described in this paper. Based on it, the structure-capability correlation matrix and structure autocorrelation matrix are established, and the multi-objective optimization design model is constructed. In addition, GA (genetic algorithm) is introduced for best solution searching, and the proposed design method is validated

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Wang, Fang, and Xu He. "Simulation and Analysis of SNR Estimation Algorithm Based on Observation Vector Covariance Matrix." Advanced Materials Research 403-408 (November 2011): 2168–71. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2168.

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AFH (adaptive frequency hopping) is the key technology of modern military tactics anti-interference, and channel quality evaluation is the core technology of AFH system. This paper describes the SNR estimation algorithm based on observation vector covariance matrix, and analyzes the mean and MMSE (minimum mean square error) of the SNR estimation algorithm based on autocorrelation and covariance matrix in the condition of AWGN and Rayleigh fading channels. The result shows that the SNR estimation algorithm based on covariance matrix has a smaller estimation error and more timely, which can be a

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