๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

EXTRACTION OF IMPACTING SIGNALS USING BLIND DECONVOLUTION

โœ Scribed by J.-Y. LEE; A.K. NANDI

Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
294 KB
Volume
232
Category
Article
ISSN
0022-460X

No coin nor oath required. For personal study only.

โœฆ Synopsis

Two algorithms, the objective function method (OFM) and the eigenvector algorithm (EVA), of the inverse "lter estimation are applied to extract the impulsive impacting signals.

Both algorithms maximize the estimation of the cumulants with observed data only. The resolution of the reconstructed signals are improved from the observed (measured) signals. Therefore, the &&clean'' impacting signals can be compared to the known impacting phenomena to determine their origin. The performance of both algorithms are investigated and these appear to perform well.

๐Ÿ“œ SIMILAR VOLUMES

BLIND DECONVOLUTION OF IMPACTING SIGNALS
BLIND DECONVOLUTION OF IMPACTING SIGNALS USING HIGHER-ORDER STATISTICS
โœ J.-Y. Lee; A.K. Nandi ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 245 KB

Experimental impacting signals with unknown measurement noise are examined using third-order statistics blind deconvolution. The impulse impact signals are recovered and the estimation of the time between impacts improved. The procedure for obtaining the optimal inverse filter is addressed using obj

MU decomposition from multichannel surfa
MU decomposition from multichannel surface EMG signals using blind deconvolution
โœ Satoshi Maekawa; Takahiko Arimoto; Manabu Kotani ๐Ÿ“‚ Article ๐Ÿ“… 2006 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 656 KB

## Abstract Without any prior knowledge about the statistics of motor unit action potentials, the authors tried to decompose motor units from multichannel surface electromyography (sEMG) signals which can be measured noninvasively. A problem in this case is that the transfer function of sEMG has no