Results 161 to 170 of about 147,262 (309)
An Empirical State Error Covariance Matrix for Batch State Estimation
, 2011 State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the ...
Frisbee, Joseph H., Jr.
core +1 more source
Research of Covariance Intersection Algorithm Based on Unscented Transformatio
Gong-kuang zidonghua, 2011 In view of problems existed in data fusion of distributed data observation system, namely bigger error in data linearization process and uncorrecting error in filtering process, the paper proposed a covariance intersection algorithm based on unscented ...
HAO Xiao-hong, WANG Rui, XU Wei-tao
doaj
Advanced Intelligent Discovery, EarlyView.Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto +3 more
wiley +1 more source
Classification efficiencies for robust linear discriminant analysis. [PDF]
Linear discriminant analysis is typically carried out using Fisher’s method. This method relies on the sample averages and covariance matrices computed from the different groups constituting the training sample.
Croux, Christophe +2 more
core
Advanced Intelligent Discovery, EarlyView.A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley +1 more source
Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy
Advanced Intelligent Discovery, EarlyView.Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang +9 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia +2 more
wiley +1 more source
Wavelet based representation of observation error covariance
, 2015 International audienceA common approximation in data assimilation is to assume observations to be uncorrelated (i.e. observation error covariance matrices are diagonal). This is obviously not true, in particular for satellite data.
Vidard, Arthur +2 more
core