scholarly journals Robust Density Comparison Using Eigenvalue Decomposition

10.5772/38517 ◽  
2012 ◽  
Author(s):  
Omar Arif ◽  
Patricio A.
Keyword(s):  
Eigenvalue Decomposition

Study on the Improvement of MUSIC Algorithm Fire Wireless Communication

2014 ◽  
Vol 1049-1050 ◽  
pp. 1880-1884
Author(s):  
Bin Ni
Keyword(s):  
Covariance Matrix ◽  
Time Domain ◽  
Analytical Signal ◽  
Time Domain Analysis ◽  
Observation Time ◽  
Eigenvalue Decomposition ◽  
Target Range ◽  
Radio Communication ◽  
Music Algorithm ◽  
Good Spatial Resolution

Music algorithm has good spatial resolution, provides the possibility to further improve the performance of fire radio communication system, but the algorithm in the target range rapidly changing circumstances poor stability. Aiming at this problem, this paper proposes a MUSIC algorithm based on time domain analytical signals (TAMUSIC, Time-domain Analysis MUSIC). The TAMUISC algorithm first constructs analytical time-domain signal; then the time domain analytical signal covariance matrix; finally the covariance matrix eigenvalue decomposition, the noise subspace estimation results of spatial spectrum. The simulation results show that, TAMUSIC algorithm in target azimuth change quickly, compared with the conventional MUSIC algorithm, need a short observation time, observation has smaller variance.

scholarly journals PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Kanokmon Rujirakul ◽  
Chakchai So-In ◽  
Banchar Arnonkijpanich
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Expectation Maximization ◽  
High Speed ◽  
Expectation Maximization Algorithm ◽  
Parallel Architecture ◽  
Principal Component ◽  
Eigenvalue Decomposition ◽  
Computational Time ◽  
Recognition Systems

Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages’ complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

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