PERFORMANCE OF GNSS CARRIER-TRACKING LOOP BASED ON KALMAN FILTER IN A CHALLENGING ENVIRONMENT by Y. Luo, Y. Luo, Y. Luo, C. Yu, J. Li, J. Li, N. El-Sheimy

“…An adaptive algorithm for the covariance matrix of the process noise is contained in our system to increase the tracking ability in a weak and dynamic environment. …”
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FREE SHAPE CONTEXT DESCRIPTORS OPTIMIZED WITH GENETIC ALGORITHM FOR THE DETECTION OF DEAD TREE TRUNKS IN ALS POINT CLOUDS by P. Polewski, W. Yao, M. Heurich, P. Krzystek, U. Stilla

“…The experiment, carried out on a set of 285 objects, reveals that an FSC optimized through a GA with manually tuned recombination parameters is able to attain a classification accuracy of 84.2%, yielding an increase of 4.2 pp compared to features derived from eigenvalues of the 3D covariance matrix. Also, we address the issue of automatically tuning the GA recombination metaparameters. …”
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Statistical analysis on spatial correlation of ionospheric day-to-day variability by using GPS and Incoherent Scatter Radar observations by X. Yue, X. Yue, X. Yue, W. Wan, L. Liu, T. Mao, T. Mao, T. Mao

“…We suggest that our results are useful in the specific modeling/forecasting of ionospheric variability and the constructing of a background covariance matrix in ionospheric data assimilation.…”
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Multivariate Bayesian Arm-Based Network Meta-Analysis of Pharmacological Interventions for the Treatment of Acute Bipolar Mania in Adults by Palash Kumar Malo, Binukumar Bhaskarapillai, Muralidharan Kesavan

“…Cholesky and spherical decomposition strategies were adopted for the between-study variance–covariance matrix. Deviance information criterion (DIC) indices were computed to determine the model fit. …”
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Mean Equality Tests for High-Dimensional and Higher-Order Data with <i>k</i>-Self Similar Compound Symmetry Covariance Structure by Ricardo Leiva, Anuradha Roy

“…The new test statistic is very efficient for high-dimensional data where the estimation of unstructured variance-covariance matrix is not feasible due to small sample size.…”
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Point Cloud Registration Based on Multiparameter Functional by Artyom Makovetskii, Sergei Voronin, Vitaly Kober, Aleksei Voronin

“…The application of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>λ</mi></semantics></math></inline-formula>-functional requires a consistent choice of the eigenvectors of the covariance matrix of two point clouds. The paper also proposes an algorithm for choosing the directions of eigenvectors. …”
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Numerical Covariance Evaluation for Linear Structures Subject to Non-Stationary Random Inputs by M. Domaneschi, R. Cucuzza, L. Sardone, S. Londoño Lopez, M. Movahedi, G. C. Marano

“…The computational efforts are reduced by considering the symmetry feature of the covariance matrix. The adopted approach is applied to analyze a multi-story building, aiming to determine the reliability related to the maximum inter-story displacement surpassing a specified acceptable threshold. …”
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Exploratory Analysis of Different Metaheuristic Optimization Methods for Medical Image Enhancement by Muhtahir O. Oloyede, Adeiza J. Onumanyi, Habeeb Bello-Salau, Karim Djouani, Anish Kurien

“…In this paper, we explore this question by analyzing nine well-known MOAs for MIE, namely the artificial bee colony, cuckoo search, differential evolution, firefly, genetic algorithm, particle swarm optimization (PSO), covariance matrix adaptive evolutionary strategy (CMAES), whale optimization algorithm (WOA), and the grey wolf optimization (GWO) algorithms. …”
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Principal Deformations Modes of Articulated Models for the Analysis of 3D Spine Deformities by Jonathan Boisvert, Farida Cheriet, Xavier Pennec, Hubert Labelle, Nicholas Ayache

“…The principal deformation modes were then extracted by performing a principal component analysis (PCA) on the generalized covariance matrix. The principal deformations modes were computed for a large database of untreated scoliotic patients. …”
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Analyzing The Covid-19 Pandemic of Volatility Spillover Influence the Collaboration of Foreign and Indian Stock Markets by Runumi Das, Arabinda Debnath

“…In this work, the positive definite covariance matrix is given, therefore a multivariate GARCH with BEKK model is used to estimate the covariance correlation and identify the consequences that stock market downturns can create. …”
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NSGA-II/SDR-OLS: A Novel Large-Scale Many-Objective Optimization Method Using Opposition-Based Learning and Local Search by Yingxin Zhang, Gaige Wang, Hongmei Wang

“…Then, we compare our algorithm with six existing algorithms, which are promising region-based multi-objective evolutionary algorithms (PREA), a scalable small subpopulation-based covariance matrix adaptation evolution strategy (S3-CMA-ES), a decomposition-based multi-objective evolutionary algorithm guided by growing neural gas (DEA-GNG), a reference vector-guided evolutionary algorithm (RVEA), NSGA-II with conflict-based partitioning strategy (NSGA-II-conflict), and a genetic algorithm using reference-point-based non-dominated sorting (NSGA-III).The proposed algorithm has achieved the best results in the vast majority of test cases, indicating that our algorithm has strong competitiveness.…”
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Using optimal estimation to retrieve winds from velocity-azimuth display (VAD) scans by a Doppler lidar by S. Baidar, S. Baidar, T. J. Wagner, D. D. Turner, W. A. Brewer

“…Another advantage of the new method is that it provides the full error covariance matrix of the solution and profiles of information content, which more easily facilitates the assimilation of the observed wind profiles into numerical weather prediction models. …”
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Comparison between multivariate methods applied for the evaluation of genetic divergence in Cacao (Theobroma cacao L.) by Luiz Antônio dos Santos Dias, Paulo Yoshio Kageyama

“…Thus, in studies on genetic divergence, statistics considering the error variance-covariance matrix should be preferred whenever its estimate is possible.…”
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Bayesian estimation of genetic parameters for multivariate threshold and continuous phenotypes and molecular genetic data in simulated horse populations using Gibbs sampling by Hoeschele Ina, Distl Ottmar, Stock Kathrin F

“…Residual variances were fixed to zero and a proper prior was used for the genetic covariance matrix.</p> <p>Results</p> <p>Effective sample sizes (ESS) and biases of genetic parameters differed significantly between datasets. …”
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Chemical composition of chicken intestines pre-dried with vegetable meals: alternative protein for aquaculture by Aroldo Botello León, Kirenia Pérez-Corría, Yuniel Méndez-Martínez, Marcos Ortega-Ojeda, Yordan Martínez-Aguilar, Yoel Rodríguez-Valera, Mario Cisneros-López, Charles Morán-Montaño

“…The content of protein, lipid, energy and costs, obtained in the different mixtures, accredits them as possible aquaculture feed, to replace conventional protein in diets, according to the species, productive purpose and market requirements. The PCA with covariance matrix suggests that the mixtures CIGCD:VM, the treatments CIGCD:SBM (80:20), CIGCD:SBM (70:30) and CIGCD:SBM (60:40) have better nutritional characteristics, although, the CIGCD:PKC (80:20) ratio showed high protein levels and was the most economical. …”
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Asymptotic Normality in Linear Regression with Approximately Sparse Structure by Saulius Jokubaitis, Remigijus Leipus

“…We focus on the case where the covariance matrix of the regression variables has a KMS structure, in asymptotic settings where the number of predictors, <i>p</i>, is proportional to the number of observations, <i>n</i>. …”
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Despeckling Multitemporal Polarimetric SAR Data Based on Tensor Decomposition by Jiayin Luo, Lu Zhang, Jie Dong, Juan M. Lopez-Sanchez, Yian Wang, Hao Feng, Mingsheng Liao

“…Real information refers to amplitude or intensity values, whereas complex information refers to complex covariance matrix (CCM) derived from either interferometric SAR (InSAR) or PolSAR data. …”
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The effects of estimating a photoionization parameter within a physics-based model using data assimilation by Hodyss Daniel, Allen Douglas R., Tyndall Daniel, Caffrey Peter, McDonald Sarah E.

“…This framework was carefully crafted to be both linear and Gaussian, which allows us to use a Kalman filter to clearly see how: (1) while recombination acts as a sink on the information in the initial condition for ionospheric field variables, recombination does not impact the information in parameter estimates in the same way, (2) when solar forcing dominates the electron density field, the prior covariance matrix becomes dominated by its leading eigenvector whose structure is directly related to that of the solar forcing, (3) estimation of parameters for forcing terms leads to a time-lag in the state estimate relative to the truth, (4) the performance of a DA system in this regime is determined by the relative dominance of solar forcing and recombination to that of the smaller-scale processes and (5) the most impactful observations on the electron density field and on the solar forcing parameter are those observations on the sunlit side of the ionosphere. …”
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An Adaptive Phase Optimization Algorithm for Distributed Scatterer Phase History Retrieval by Shijin Li, Shubi Zhang, Tao Li, Yandong Gao, Qianfu Chen, Xiang Zhang

“…However, conventional POAs rely on a sample covariance matrix or complex coherence matrix (CCM) derived by spatially averaging statistically homogeneous pixel neighborhoods, which may blur and destroy phase information, especially in dense fringe areas. …”
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Approximate Genome-Based Kernel Models for Large Data Sets Including Main Effects and Interactions by Jaime Cuevas, Osval A. Montesinos-López, J. W. R. Martini, Paulino Pérez-Rodríguez, Morten Lillemo, Jose Crossa, Jose Crossa

“…First, we describe the full genomic method for single environment (FGSE) with a covariance matrix (kernel) including all n lines. Second, we select m lines and approximate the original kernel for the single environment model (APSE). …”
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