Bistatic Scattering from Rough Surfaces Based on Joint Slope Statistics and Covariance Matrix Decomposition

2006 ◽  
Author(s):  
Y. Du ◽  
Y. Luo ◽  
J. Kong
Keyword(s):  
Covariance Matrix ◽  
Rough Surfaces ◽  
Matrix Decomposition ◽  
Bistatic Scattering ◽  
Covariance Matrix Decomposition

scholarly journals Blind Suppression of Nonstationary Diffuse Acoustic Noise Based on Spatial Covariance Matrix Decomposition

2014 ◽  
Vol 79 (2) ◽  
pp. 145-157 ◽  
Author(s):  
Nobutaka Ito ◽  
Emmanuel Vincent ◽  
Tomohiro Nakatani ◽  
Nobutaka Ono ◽  
Shoko Araki ◽  
...  
Keyword(s):  
Covariance Matrix ◽  
Acoustic Noise ◽  
Matrix Decomposition ◽  
Spatial Covariance ◽  
Covariance Matrix Decomposition

CAESAR: An Approach Based on Covariance Matrix Decomposition to Improve Multibaseline–Multitemporal Interferometric SAR Processing

2015 ◽  
Vol 53 (4) ◽  
pp. 2050-2065 ◽  
Author(s):  
Gianfranco Fornaro ◽  
Simona Verde ◽  
Diego Reale ◽  
Antonio Pauciullo
Keyword(s):  
Covariance Matrix ◽  
Matrix Decomposition ◽  
Interferometric Sar ◽  
Sar Processing ◽  
Covariance Matrix Decomposition

Centralized spectrum sensing based on covariance matrix decomposition and particle swarm clustering

2021 ◽  
Vol 46 ◽  
pp. 101322
Author(s):  
Jiawei Zhuang ◽  
Yonghua Wang ◽  
Pin Wan ◽  
Shunchao Zhang ◽  
Yongwei Zhang
Keyword(s):  
Covariance Matrix ◽  
Spectrum Sensing ◽  
Particle Swarm ◽  
Matrix Decomposition ◽  
Covariance Matrix Decomposition

scholarly journals Flexible Covariance Matrix Decomposition Method for Data Augmentation and Its Application to Brainwave Signals

2021 ◽  
Vol 11 (20) ◽  
pp. 9388
Author(s):  
Hoirim Lee ◽  
Wonseok Yang ◽  
Woochul Nam
Keyword(s):  
Covariance Matrix ◽  
Data Augmentation ◽  
Random Noise ◽  
Matrix Decomposition ◽  
Original Data ◽  
Artificial Data ◽  
Data Synthesis ◽  
Standard Distribution ◽  
Generalized Normal Distribution ◽  
Covariance Matrix Decomposition

The acquisition of a large-volume brainwave database is challenging because of the stressful experiments that are required; however, data synthesis techniques can be used to address this limitation. Covariance matrix decomposition (CMD), a widely used data synthesis approach, generates artificial data using the correlation between features and random noise. However, previous CMD methods constrain the stochastic characteristics of artificial datasets because the random noise used follows a standard distribution. Therefore, this study has improved the performance of CMD by releasing such constraints. Specifically, a generalized normal distribution (GND) was used as it can alter the kurtosis and skewness of the random noise, affecting the distribution of the artificial data. For the validation of GND performance, a motor imagery brainwave classification was conducted on the artificial dataset generated by GND. The GND-based data synthesis increased the classification accuracy obtained with the original data by approximately 8%.

scholarly journals FDTD INVESTIGATION ON BISTATIC SCATTERING FROM A TARGET ABOVE TWO-LAYERED ROUGH SURFACES USING UPML ABSORBING CONDITION

2008 ◽  
Vol 88 ◽  
pp. 197-211 ◽  
Author(s):  
Juan Li ◽  
Li-Xin Guo ◽  
Hao Zeng
Keyword(s):  
Rough Surfaces ◽  
Bistatic Scattering

An intercomparison of models for predicting bistatic scattering from rough surfaces

2015 ◽  
Author(s):  
Caglar Yardim ◽  
Joel T. Johnson ◽  
Robert J. Burkholder ◽  
Fernando L. Teixeira ◽  
Jeffrey D. Ouellette ◽  
...  
Keyword(s):  
Rough Surfaces ◽  
Bistatic Scattering

Bistatic Scattering Model for Rough Surfaces

2006 ◽  
Author(s):  
Y. Du ◽  
Y. Qi ◽  
H. Chen ◽  
J. Kong
Keyword(s):  
Rough Surfaces ◽  
Scattering Model ◽  
Bistatic Scattering
Sign in / Sign up

Export Citation Format

Share Document