Results 171 to 180 of about 2,877,467 (378)
Return and Volatility Spillovers Among Major Cotton Markets
ABSTRACT This study explores return and volatility transmission among major cotton markets. Several events have disrupted cotton supply and demand in recent years, leading to heightened price volatility and significant shifts in market interconnections.
Susmitha Kalli +3 more
wiley +1 more source
Minimum variance beamforming combined with covariance matrix-based adaptive weighting for medical ultrasound imaging. [PDF]
Wang Y +5 more
europepmc +1 more source
Abstract Three instruments–Raman spectroscopy, attenuated total reflectance–Fourier transform infrared spectroscopy, and focused beam reflectance measurement–were used to detect sensor faults, mixing faults, and unanticipated chemistry in a system of multicomponent slurries.
Steven H. Crouse +2 more
wiley +1 more source
Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution
Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren +6 more
wiley +1 more source
2D-DOA Estimation in Switching UCA Using Deep Learning-Based Covariance Matrix Completion. [PDF]
Mei R, Tian Y, Huang Y, Wang Z.
europepmc +1 more source
Analysis of modified SMI method for adaptive array weight control [PDF]
An adaptive array is applied to the problem of receiving a desired signal in the presence of weak interference signals which need to be suppressed. A modification, suggested by Gupta, of the sample matrix inversion (SMI) algorithm controls the array ...
Dilsavor, R. L., Moses, R. L.
core +1 more source
A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelationconsistent Covariance Matrix
W. Newey, K. West
semanticscholar +1 more source
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Estimation of the Covariance Matrix in Hierarchical Bayesian Spatio-Temporal Modeling via Dimension Expansion. [PDF]
Sun B, Wu Y.
europepmc +1 more source

