Results 71 to 80 of about 285,691 (282)
Small Sample Motor Imagery Classification Using Regularized Riemannian Features
IEEE Access, 2019 Motor imagery-based electroencephalogram brain-computer interface (BCI) performance suffers from huge variations within and across subjects. This is due to different spatial and temporal characteristics among the subjects. To address these variabilities,
Amardeep Singh +2 more
doaj +1 more source
Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output
ROBOMECH Journal, 2020 This paper presents an extended Kalman filter for pose estimation using noise covariance matrices based on sensor output. Compact and lightweight nine-axis motion sensors are used for motion analysis in widely various fields such as medical welfare and ...
Ayuko Saito +3 more
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Geometric methods for estimation of structured covariances [PDF]
, 2011 We consider problems of estimation of structured covariance matrices, and in particular of matrices with a Toeplitz structure. We follow a geometric viewpoint that is based on some suitable notion of distance. To this end, we overview and compare several
Georgiou, Tryphon +2 more
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Generative Adversarial Estimation of Channel Covariance in Vehicular Millimeter Wave Systems
, 2018 Enabling highly-mobile millimeter wave (mmWave) systems is challenging because of the huge training overhead associated with acquiring the channel knowledge or designing the narrow beams.
Alkhateeb, Ahmed +2 more
core +1 more source
Advanced Materials, EarlyView.Transition metal oxy/carbo‐nitrides show great promise as catalysts for sustainable processes. A Mn‐Mo mixed‐metal oxynitride attains remarkable performance for the direct synthesis of acetonitrile, an important commodity chemical, via sequential C─N and C─C coupling from syngas (C1) and ammonia (N1) feedstocks.
M. Elena Martínez‐Monje +7 more
wiley +1 more source
Improving the condition number of estimated covariance matrices
Tellus: Series A, Dynamic Meteorology and Oceanography, 2020 High dimensional error covariance matrices and their inverses are used to weight the contribution of observation and background information in data assimilation procedures.
Jemima M. Tabeart +4 more
doaj +1 more source
Invariant Tests on Covariance Matrices
The Annals of Statistics, 1981 Minimal complete classes of invariant tests are presented for modifications of the problem of testing the independence of $Y$ and $X$, where $(Y, X) \equiv (Y, X_1, \cdots, X_p)$ is a multivariate normal random vector. One modification involves having extra independent observations on $Y$.
openaire +2 more sources
Forecasting Covariance Matrices: A Mixed Frequency Approach [PDF]
SSRN Electronic Journal, 2011 This paper proposes a new method for forecasting covariance matrices of financial returns. the model mixes volatility forecasts from a dynamic model of daily realized volatilities estimated with high-frequency data with correlation forecasts based on daily data. This new approach allows for flexible dependence patterns for volatilities and correlations,
Halbleib, Roxana, Voev, Valerie
openaire +7 more sources
Advanced Materials, EarlyView.Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu +3 more
wiley +1 more source
Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes [PDF]
, 2013 Covariance parameter estimation of Gaussian processes is analyzed in an asymptotic framework. The spatial sampling is a randomly perturbed regular grid and its deviation from the perfect regular grid is controlled by a single scalar regularity parameter.
Bachoc, François
core +3 more sources